def interp_hmats(newtimes, oldtimes, oldhmats):
oldposes = openravepy.poseFromMatrices(oldhmats)
newposes = np.empty((len(newtimes), 7))
newposes[:,4:7] = mu.interp2d(newtimes, oldtimes, oldposes[:,4:7])
newposes[:,0:4] = interp_quats(newtimes, oldtimes, oldposes[:,0:4])
newhmats = openravepy.matrixFromPoses(newposes)
return newhmats
def make_joint_traj(xyzs, quats, manip, ref_frame, targ_frame, filter_options=0):
"do ik and then fill in the points where ik failed"
n = len(xyzs)
assert len(quats) == n
robot = manip.GetRobot()
joint_inds = manip.GetArmIndices()
robot.SetActiveDOFs(joint_inds)
orig_joint = robot.GetActiveDOFValues()
joints = []
inds = []
for i in xrange(0, n):
mat4 = conv.trans_rot_to_hmat(xyzs[i], quats[i])
joint = PR2.cart_to_joint(manip, mat4, ref_frame, targ_frame, filter_options)
if joint is not None:
joints.append(joint)
inds.append(i)
robot.SetActiveDOFValues(joint)
robot.SetActiveDOFValues(orig_joint)
rospy.loginfo("found ik soln for %i of %i points", len(inds), n)
if len(inds) > 2:
joints2 = mu.interp2d(np.arange(n), inds, joints)
return joints2, inds
else:
return np.zeros((n, len(joints))), []
def make_joint_traj(xyzs, quats, manip, ref_frame, targ_frame, filter_options = 0):
"do ik and then fill in the points where ik failed"
n = len(xyzs)
assert len(quats) == n
robot = manip.GetRobot()
joint_inds = manip.GetArmIndices()
robot.SetActiveDOFs(joint_inds)
orig_joint = robot.GetActiveDOFValues()
joints = []
inds = []
for i in xrange(0,n):
mat4 = conv.trans_rot_to_hmat(xyzs[i], quats[i])
joint = PR2.cart_to_joint(manip, mat4, ref_frame, targ_frame, filter_options)
if joint is not None:
joints.append(joint)
inds.append(i)
robot.SetActiveDOFValues(joint)
robot.SetActiveDOFValues(orig_joint)
LOG.info("found ik soln for %i of %i points",len(inds), n)
if len(inds) > 2:
joints2 = mu.interp2d(np.arange(n), inds, joints)
return joints2, inds
else:
return np.zeros((n, len(joints))), []
def adaptive_resample(x, tol, max_change=None, min_steps=3):
"""
resample original signal with a small number of waypoints so that the the sparsely sampled function,
when linearly interpolated, deviates from the original function by less than tol at every time
input:
x: 2D array in R^(t x k) where t is the number of timesteps
tol: tolerance. either a single scalar or a vector of length k
max_change: max change in the sparsely sampled signal at each timestep
min_steps: minimum number of timesteps in the new trajectory. (usually irrelevant)
output:
new_times, new_x
assuming that the old signal has times 0,1,2,...,len(x)-1
this gives the new times, and the new signal
"""
x = np.asarray(x)
assert x.ndim == 2
if np.isscalar(tol):
tol = np.ones(x.shape[1])*tol
else:
tol = np.asarray(tol)
assert tol.ndim == 1 and tol.shape[0] == x.shape[1]
times = np.arange(x.shape[0])
if max_change is None:
max_change = np.ones(x.shape[1]) * np.inf
elif np.isscalar(max_change):
max_change = np.ones(x.shape[1]) * max_change
else:
max_change = np.asarray(max_change)
assert max_change.ndim == 1 and max_change.shape[0] == x.shape[1]
dl = mu.norms(x[1:] - x[:-1],1)
l = np.cumsum(np.r_[0,dl])
def bad_inds(x1, t1):
ibad = np.flatnonzero( (np.abs(mu.interp2d(l, l1, x1) - x) > tol).any(axis=1) )
jbad1 = np.flatnonzero((np.abs(x1[1:] - x1[:-1]) > max_change[None,:]).any(axis=1))
if len(ibad) == 0 and len(jbad1) == 0: return []
else:
lbad = l[ibad]
jbad = np.unique(np.searchsorted(l1, lbad)) - 1
jbad = np.union1d(jbad, jbad1)
return jbad
l1 = np.linspace(0,l[-1],min_steps)
for _ in xrange(20):
x1 = mu.interp2d(l1, l, x)
bi = bad_inds(x1, l1)
if len(bi) == 0:
return np.interp(l1, l, times), x1
else:
l1 = np.union1d(l1, (l1[bi] + l1[bi+1]) / 2 )
raise Exception("couldn't subdivide enough. something funny is going on. check your input data")
def traj_collisions(sim_env, full_traj, collision_dist_threshold, n=100):
"""
Returns the set of collisions.
manip = Manipulator or list of indices
"""
traj, dof_inds = full_traj
traj_up = mu.interp2d(np.linspace(0,1,n), np.linspace(0,1,len(traj)), traj)
cc = trajoptpy.GetCollisionChecker(sim_env.env)
with openravepy.RobotStateSaver(sim_env.robot):
sim_env.robot.SetActiveDOFs(dof_inds)
col_times = []
for (i,row) in enumerate(traj_up):
sim_env.robot.SetActiveDOFValues(row)
col_now = cc.BodyVsAll(sim_env.robot)
#with util.suppress_stdout():
# col_now2 = cc.PlotCollisionGeometry()
col_now = [cn for cn in col_now if cn.GetDistance() < collision_dist_threshold]
if col_now:
#print [cn.GetDistance() for cn in col_now]
col_times.append(i)
#print "trajopt.CollisionChecker: ", len(col_now)
#print col_now2
return col_times
def observe_cloud2(self, radius, axis_upsample=0, radius_sample=0, angle_sample=0):
axis_pts = self.rope.GetControlPoints()
indices = range(len(axis_pts))
if axis_upsample != 0:
lengths = np.r_[0, self.rope.GetHalfHeights() * 2]
summed_lengths = np.cumsum(lengths)
assert len(lengths) == len(axis_pts)
indices = np.interp(np.linspace(0, summed_lengths[-1], axis_upsample*len(lengths)), summed_lengths, indices)
axis_pts = math_utils.interp2d(np.linspace(0, summed_lengths[-1], axis_upsample*len(lengths)), summed_lengths, axis_pts)
half_heights = self.rope.GetHalfHeights()
rotates = self.rope.GetRotations()
translates = self.rope.GetTranslations()
new_pts = []
for r in range(1, radius_sample+1):
d = r * radius / float(radius_sample)
for a in range(angle_sample):
local_rotate = matrixFromAxisAngle([a/float(angle_sample)*2*np.pi,0,0])[:3,:3]
for index in indices:
node_id = np.min([np.floor(index), len(self.rope.GetNodes()) - 1])
h = half_heights[node_id]
v = np.array([-h + 2*h*(index-node_id),d,0])
rotate = rotates[node_id]
translate = translates[node_id]
v = rotate.dot(local_rotate.dot(v)) + translate
new_pts.append(v)
for pt in axis_pts:
new_pts.append(pt)
return np.array(new_pts)
def interp_hmats(newtimes, oldtimes, oldhmats):
oldposes = openravepy.poseFromMatrices(oldhmats)
newposes = np.empty((len(newtimes), 7))
newposes[:,4:7] = mu.interp2d(newtimes, oldtimes, oldposes[:,4:7])
newposes[:,0:4] = interp_quats(newtimes, oldtimes, oldposes[:,0:4])
newhmats = openravepy.matrixFromPoses(newposes)
return newhmats
def traj_collisions(sim_env, full_traj, collision_dist_threshold, n=100):
"""
Returns the set of collisions.
manip = Manipulator or list of indices
"""
traj, dof_inds = full_traj
traj_up = mu.interp2d(np.linspace(0, 1, n), np.linspace(0, 1, len(traj)),
traj)
cc = trajoptpy.GetCollisionChecker(sim_env.env)
with openravepy.RobotStateSaver(sim_env.robot):
sim_env.robot.SetActiveDOFs(dof_inds)
col_times = []
for (i, row) in enumerate(traj_up):
sim_env.robot.SetActiveDOFValues(row)
col_now = cc.BodyVsAll(sim_env.robot)
#with util.suppress_stdout():
# col_now2 = cc.PlotCollisionGeometry()
col_now = [
cn for cn in col_now
if cn.GetDistance() < collision_dist_threshold
]
if col_now:
#print [cn.GetDistance() for cn in col_now]
col_times.append(i)
#print "trajopt.CollisionChecker: ", len(col_now)
#print col_now2
return col_times
def observe_cloud(self, upsample=0):
pts = self.rope.GetControlPoints()
if upsample == 0:
return pts
lengths = np.r_[0, self.rope.GetHalfHeights() * 2]
summed_lengths = np.cumsum(lengths)
assert len(lengths) == len(pts)
return math_utils.interp2d(np.linspace(0, summed_lengths[-1], upsample), summed_lengths, pts)
def bad_inds(x1, t1):
ibad = np.flatnonzero( (np.abs(mu.interp2d(l, l1, x1) - x) > tol).any(axis=1) )
jbad1 = np.flatnonzero((np.abs(x1[1:] - x1[:-1]) > max_change[None,:]).any(axis=1))
if len(ibad) == 0 and len(jbad1) == 0: return []
else:
lbad = l[ibad]
jbad = np.unique(np.searchsorted(l1, lbad)) - 1
jbad = np.union1d(jbad, jbad1)
return jbad
def follow_joint_trajectory(self, traj):
traj = np.r_[np.atleast_2d(self.get_joint_positions()), traj]
for i in [2,4,6]:
traj[:,i] = np.unwrap(traj[:,i])
times = retiming.retime_with_vel_limits(traj, self.vel_limits)
times_up = np.arange(0,times[-1],.1)
traj_up = mu.interp2d(times_up, times, traj)
vels = ku.get_velocities(traj_up, times_up, .001)
self.follow_timed_joint_trajectory(traj_up, vels, times_up)
def follow_joint_trajectory(self, traj):
traj = np.r_[np.atleast_2d(self.get_joint_positions()), traj]
for i in [2,4,6]:
traj[:,i] = np.unwrap(traj[:,i])
times = retiming.retime_with_vel_limits(traj, self.vel_limits)
times_up = np.arange(0,times[-1],.1)
traj_up = mu.interp2d(times_up, times, traj)
vels = resampling.get_velocities(traj_up, times_up, .001)
self.follow_timed_joint_trajectory(traj_up, vels, times_up)
def observe_cloud(self, upsample=0):
pts = self.rope.GetControlPoints()
if upsample == 0:
return pts
lengths = np.r_[0, self.rope.GetHalfHeights() * 2]
summed_lengths = np.cumsum(lengths)
assert len(lengths) == len(pts)
upsampled_pts = math_utils.interp2d(
np.linspace(0, summed_lengths[-1], upsample * len(lengths)),
summed_lengths, pts)
return upsampled_pts
def interp_hmats(newtimes, oldtimes, oldhmats):
oldposes = openravepy.poseFromMatrices(oldhmats)
for i in xrange(1, len(oldposes)):
qp = oldposes[i-1,0:4]
qc = oldposes[i,0:4]
if np.linalg.norm(qp + qc) < np.linalg.norm(qp-qc):
oldposes[i,0:4] = -qc
newposes = np.empty((len(newtimes), 7))
newposes[:,4:7] = mu.interp2d(newtimes, oldtimes, oldposes[:,4:7])
newposes[:,0:4] = interp_quats(newtimes, oldtimes, oldposes[:,0:4])
newhmats = openravepy.matrixFromPoses(newposes)
return newhmats
def retime_traj(robot, inds, traj, max_cart_vel=.02, upsample_time=.1):
"""retime a trajectory so that it executes slowly enough for the simulation"""
cart_traj = np.empty((len(traj), 6))
leftarm, rightarm = robot.GetManipulator("leftarm"), robot.GetManipulator("rightarm")
with robot:
for i in range(len(traj)):
robot.SetDOFValues(traj[i], inds)
cart_traj[i,:3] = leftarm.GetTransform()[:3,3]
cart_traj[i,3:] = rightarm.GetTransform()[:3,3]
times = retiming.retime_with_vel_limits(cart_traj, np.repeat(max_cart_vel, 6))
times_up = np.linspace(0, times[-1], times[-1]/upsample_time) if times[-1] > upsample_time else times
traj_up = math_utils.interp2d(times_up, times, traj)
return traj_up
def observe_cloud2(self,
radius,
axis_upsample=0,
radius_sample=0,
angle_sample=0):
axis_pts = self.rope.GetControlPoints()
indices = range(len(axis_pts))
if axis_upsample != 0:
lengths = np.r_[0, self.rope.GetHalfHeights() * 2]
summed_lengths = np.cumsum(lengths)
assert len(lengths) == len(axis_pts)
indices = np.interp(
np.linspace(0, summed_lengths[-1],
axis_upsample * len(lengths)), summed_lengths,
indices)
axis_pts = math_utils.interp2d(
np.linspace(0, summed_lengths[-1],
axis_upsample * len(lengths)), summed_lengths,
axis_pts)
half_heights = self.rope.GetHalfHeights()
rotates = self.rope.GetRotations()
translates = self.rope.GetTranslations()
new_pts = []
for r in range(1, radius_sample + 1):
d = r * radius / float(radius_sample)
for a in range(angle_sample):
local_rotate = matrixFromAxisAngle(
[a / float(angle_sample) * 2 * np.pi, 0, 0])[:3, :3]
for index in indices:
node_id = np.min(
[np.floor(index),
len(self.rope.GetNodes()) - 1])
h = half_heights[node_id]
v = np.array([-h + 2 * h * (index - node_id), d, 0])
rotate = rotates[node_id]
translate = translates[node_id]
v = rotate.dot(local_rotate.dot(v)) + translate
new_pts.append(v)
for pt in axis_pts:
new_pts.append(pt)
return np.array(new_pts)
def exec_traj_maybesim(bodypart2traj, speed_factor=0.5):
if args.animation:
"""
dof_inds = []
trajs = []
for (part_name, traj) in bodypart2traj.items():
manip_name = {"larm":"leftarm","rarm":"rightarm"}[part_name]
dof_inds.extend(Globals.robot.GetManipulator(manip_name).GetArmIndices())
trajs.append(traj)
full_traj = np.concatenate(trajs, axis=1)
Globals.robot.SetActiveDOFs(dof_inds)
animate_traj.animate_traj(full_traj, Globals.robot, restore=False,pause=True)
"""
name2part = {"lgrip":Globals.pr2.lgrip,
"rgrip":Globals.pr2.rgrip,
"larm":Globals.pr2.larm,
"rarm":Globals.pr2.rarm,
"base":Globals.pr2.base}
dof_inds = []
trajs = []
vel_limits = []
for (part_name, traj) in bodypart2traj.items():
manip_name = {"larm":"leftarm","rarm":"rightarm"}[part_name]
vel_limits.extend(name2part[part_name].vel_limits)
dof_inds.extend(Globals.robot.GetManipulator(manip_name).GetArmIndices())
if traj.ndim == 1: traj = traj.reshape(-1,1)
trajs.append(traj)
trajectories = np.concatenate(trajs, 1)
print trajectories.shape
times = retiming.retime_with_vel_limits(trajectories, np.array(vel_limits))
times_up = np.linspace(0, times[-1], int(np.ceil(times[-1]/.1)))
full_traj = mu.interp2d(times_up, times, trajectories)
print full_traj.shape
Globals.robot.SetActiveDOFs(dof_inds)
animate_traj.animate_traj(full_traj, Globals.robot, restore=False,pause=True)
#return True
if args.execution:
pr2_trajectories.follow_body_traj(Globals.pr2, bodypart2traj, speed_factor=speed_factor)
return True
def simulate_demo(new_xyz, seg_info, animate=False):
Globals.robot.SetDOFValues(PR2_L_POSTURES["side"], Globals.robot.GetManipulator("leftarm").GetArmIndices())
Globals.robot.SetDOFValues(mirror_arm_joints(PR2_L_POSTURES["side"]), Globals.robot.GetManipulator("rightarm").GetArmIndices())
redprint("Generating end-effector trajectory")
handles = []
old_xyz = np.squeeze(seg_info["cloud_xyz"])
handles.append(Globals.env.plot3(old_xyz,5, (1,0,0)))
handles.append(Globals.env.plot3(new_xyz,5, (0,0,1)))
old_xyz = clouds.downsample(old_xyz, DS_SIZE)
new_xyz = clouds.downsample(new_xyz, DS_SIZE)
link_names = ["%s_gripper_tool_frame"%lr for lr in ('lr')]
hmat_list = [(lr, seg_info[ln]['hmat']) for lr, ln in zip('lr', link_names)]
lr2eetraj = warp_hmats(old_xyz, new_xyz, hmat_list)[0]
miniseg_starts, miniseg_ends = split_trajectory_by_gripper(seg_info)
success = True
print colorize.colorize("mini segments:", "red"), miniseg_starts, miniseg_ends
for (i_miniseg, (i_start, i_end)) in enumerate(zip(miniseg_starts, miniseg_ends)):
################################
redprint("Generating joint trajectory for part %i"%(i_miniseg))
# figure out how we're gonna resample stuff
lr2oldtraj = {}
for lr in 'lr':
manip_name = {"l":"leftarm", "r":"rightarm"}[lr]
old_joint_traj = asarray(seg_info[manip_name][i_start:i_end+1])
#print (old_joint_traj[1:] - old_joint_traj[:-1]).ptp(axis=0), i_start, i_end
if arm_moved(old_joint_traj):
lr2oldtraj[lr] = old_joint_traj
if len(lr2oldtraj) > 0:
old_total_traj = np.concatenate(lr2oldtraj.values(), 1)
JOINT_LENGTH_PER_STEP = .1
_, timesteps_rs = unif_resample(old_total_traj, JOINT_LENGTH_PER_STEP)
####
### Generate fullbody traj
bodypart2traj = {}
for (lr,old_joint_traj) in lr2oldtraj.items():
manip_name = {"l":"leftarm", "r":"rightarm"}[lr]
old_joint_traj_rs = mu.interp2d(timesteps_rs, np.arange(len(old_joint_traj)), old_joint_traj)
ee_link_name = "%s_gripper_tool_frame"%lr
new_ee_traj = lr2eetraj[lr][i_start:i_end+1]
new_ee_traj_rs = resampling.interp_hmats(timesteps_rs, np.arange(len(new_ee_traj)), new_ee_traj)
print "planning trajectory following"
with util.suppress_stdout():
new_joint_traj = planning.plan_follow_traj(Globals.robot, manip_name,
Globals.robot.GetLink(ee_link_name), new_ee_traj_rs,old_joint_traj_rs)[0]
part_name = {"l":"larm", "r":"rarm"}[lr]
bodypart2traj[part_name] = new_joint_traj
################################
redprint("Executing joint trajectory for part %i using arms '%s'"%(i_miniseg, bodypart2traj.keys()))
for lr in 'lr':
gripper_open = binarize_gripper(seg_info["%s_gripper_joint"%lr][i_start])
prev_gripper_open = binarize_gripper(seg_info["%s_gripper_joint"%lr][i_start-1]) if i_start != 0 else False
if not set_gripper_maybesim(lr, gripper_open, prev_gripper_open):
redprint("Grab %s failed" % lr)
success = False
if not success: break
if len(bodypart2traj) > 0:
success &= sim_traj_maybesim(bodypart2traj, animate=True)
if not success: break
Globals.sim.settle(animate=animate)
Globals.robot.SetDOFValues(PR2_L_POSTURES["side"], Globals.robot.GetManipulator("leftarm").GetArmIndices())
Globals.robot.SetDOFValues(mirror_arm_joints(PR2_L_POSTURES["side"]), Globals.robot.GetManipulator("rightarm").GetArmIndices())
Globals.sim.release_rope('l')
Globals.sim.release_rope('r')
return success
def compute_trans_traj(sim_env,
new_xyz,
seg_info,
ignore_infeasibility=True,
animate=False,
interactive=False):
sim_util.reset_arms_to_side(sim_env)
redprint("Generating end-effector trajectory")
old_xyz = np.squeeze(seg_info["cloud_xyz"])
old_xyz = clouds.downsample(old_xyz, DS_SIZE)
new_xyz = clouds.downsample(new_xyz, DS_SIZE)
link_names = ["%s_gripper_tool_frame" % lr for lr in ('lr')]
hmat_list = [(lr, seg_info[ln]['hmat'])
for lr, ln in zip('lr', link_names)]
if GlobalVars.gripper_weighting:
interest_pts = get_closing_pts(seg_info)
else:
interest_pts = None
lr2eetraj, _, old_xyz_warped = warp_hmats_tfm(old_xyz, new_xyz, hmat_list,
interest_pts)
handles = []
if animate:
handles.append(sim_env.env.plot3(old_xyz, 5, (1, 0, 0)))
handles.append(sim_env.env.plot3(new_xyz, 5, (0, 0, 1)))
handles.append(sim_env.env.plot3(old_xyz_warped, 5, (0, 1, 0)))
miniseg_starts, miniseg_ends, _ = sim_util.split_trajectory_by_gripper(
seg_info, thresh=GRIPPER_ANGLE_THRESHOLD)
success = True
feasible = True
misgrasp = False
print colorize.colorize("mini segments:",
"red"), miniseg_starts, miniseg_ends
full_trajs = []
prev_vals = {lr: None for lr in 'lr'}
for (i_miniseg, (i_start,
i_end)) in enumerate(zip(miniseg_starts, miniseg_ends)):
################################
redprint("Generating joint trajectory for part %i" % (i_miniseg))
# figure out how we're gonna resample stuff
# Use inverse kinematics to get trajectory for initializing TrajOpt,
# since demonstrations library does not contain joint angle data for
# left and right arms
ee_hmats = {}
for lr in 'lr':
ee_link_name = "%s_gripper_tool_frame" % lr
# TODO: Change # of timesteps for resampling?
ee_hmats[ee_link_name] = resampling.interp_hmats(
np.arange(i_end + 1 - i_start), np.arange(i_end + 1 - i_start),
lr2eetraj[lr][i_start:i_end + 1])
lr2oldtraj = get_old_joint_traj_ik(sim_env, ee_hmats, prev_vals,
i_start, i_end)
#lr2oldtraj = {}
#for lr in 'lr':
# manip_name = {"l":"leftarm", "r":"rightarm"}[lr]
# old_joint_traj = asarray(seg_info[manip_name][i_start:i_end+1])
# #print (old_joint_traj[1:] - old_joint_traj[:-1]).ptp(axis=0), i_start, i_end
# if sim_util.arm_moved(old_joint_traj):
# lr2oldtraj[lr] = old_joint_traj
if len(lr2oldtraj) > 0:
old_total_traj = np.concatenate(lr2oldtraj.values(), 1)
JOINT_LENGTH_PER_STEP = .1
_, timesteps_rs = sim_util.unif_resample(old_total_traj,
JOINT_LENGTH_PER_STEP)
####
### Generate fullbody traj
bodypart2traj = {}
for (lr, old_joint_traj) in lr2oldtraj.items():
manip_name = {"l": "leftarm", "r": "rightarm"}[lr]
old_joint_traj_rs = mu.interp2d(timesteps_rs,
np.arange(len(old_joint_traj)),
old_joint_traj)
ee_link_name = "%s_gripper_tool_frame" % lr
new_ee_traj = lr2eetraj[lr][i_start:i_end + 1]
new_ee_traj_rs = resampling.interp_hmats(
timesteps_rs, np.arange(len(new_ee_traj)), new_ee_traj)
print "planning trajectory following"
new_joint_traj, pose_errs = planning.plan_follow_traj(
sim_env.robot, manip_name, sim_env.robot.GetLink(ee_link_name),
new_ee_traj_rs, old_joint_traj_rs)
prev_vals[lr] = new_joint_traj[-1]
part_name = {"l": "larm", "r": "rarm"}[lr]
bodypart2traj[part_name] = new_joint_traj
################################
redprint("Executing joint trajectory for part %i using arms '%s'" %
(i_miniseg, bodypart2traj.keys()))
full_traj = sim_util.getFullTraj(sim_env, bodypart2traj)
full_trajs.append(full_traj)
for lr in 'lr':
gripper_open = sim_util.binarize_gripper(
seg_info["%s_gripper_joint" % lr][i_start],
GRIPPER_ANGLE_THRESHOLD)
prev_gripper_open = sim_util.binarize_gripper(
seg_info["%s_gripper_joint" % lr][i_start - 1],
GRIPPER_ANGLE_THRESHOLD) if i_start != 0 else False
if not sim_util.set_gripper_maybesim(sim_env, lr, gripper_open,
prev_gripper_open):
redprint("Grab %s failed" % lr)
misgrasp = True
success = False
if not success: break
if len(full_traj[0]) > 0:
if not eval_util.traj_is_safe(sim_env, full_traj,
COLLISION_DIST_THRESHOLD):
redprint("Trajectory not feasible")
feasible = False
if feasible or ignore_infeasibility:
success &= sim_util.sim_full_traj_maybesim(
sim_env,
full_traj,
animate=animate,
interactive=interactive)
else:
success = False
if not success: break
sim_env.sim.settle(animate=animate)
sim_env.sim.release_rope('l')
sim_env.sim.release_rope('r')
sim_util.reset_arms_to_side(sim_env)
if animate:
sim_env.viewer.Step()
return success, feasible, misgrasp, full_trajs
def main():
import IPython
demofile = h5py.File(args.h5file, 'r')
trajoptpy.SetInteractive(args.interactive)
if args.log:
LOG_DIR = osp.join(osp.expanduser("~/Data/do_task_logs"), datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
os.mkdir(LOG_DIR)
LOG_COUNT = 0
if args.execution:
rospy.init_node("exec_task",disable_signals=True)
Globals.pr2 = PR2.PR2()
Globals.env = Globals.pr2.env
Globals.robot = Globals.pr2.robot
else:
Globals.env = openravepy.Environment()
Globals.env.StopSimulation()
Globals.env.Load("robots/pr2-beta-static.zae")
Globals.robot = Globals.env.GetRobots()[0]
if not args.fake_data_segment:
grabber = cloudprocpy.CloudGrabber()
grabber.startRGBD()
Globals.viewer = trajoptpy.GetViewer(Globals.env)
#####################
while True:
redprint("Acquire point cloud")
if args.fake_data_segment:
fake_seg = demofile[args.fake_data_segment]
new_xyz = np.squeeze(fake_seg["cloud_xyz"])
hmat = openravepy.matrixFromAxisAngle(args.fake_data_transform[3:6])
hmat[:3,3] = args.fake_data_transform[0:3]
new_xyz = new_xyz.dot(hmat[:3,:3].T) + hmat[:3,3][None,:]
r2r = ros2rave.RosToRave(Globals.robot, asarray(fake_seg["joint_states"]["name"]))
r2r.set_values(Globals.robot, asarray(fake_seg["joint_states"]["position"][0]))
#Globals.pr2.head.set_pan_tilt(0,1.2)
#Globals.pr2.rarm.goto_posture('side')
#Globals.pr2.larm.goto_posture('side')
#Globals.pr2.join_all()
#time.sleep(2)
else:
#Globals.pr2.head.set_pan_tilt(0,1.2)
#Globals.pr2.rarm.goto_posture('side')
#Globals.pr2.larm.goto_posture('side')
#Globals.pr2.join_all()
#time.sleep(2)
Globals.pr2.update_rave()
rgb, depth = grabber.getRGBD()
T_w_k = berkeley_pr2.get_kinect_transform(Globals.robot)
new_xyz = cloud_proc_func(rgb, depth, T_w_k)
print "got new xyz"
redprint(new_xyz)
#grab_end(new_xyz)
if args.log:
LOG_COUNT += 1
import cv2
cv2.imwrite(osp.join(LOG_DIR,"rgb%i.png"%LOG_COUNT), rgb)
cv2.imwrite(osp.join(LOG_DIR,"depth%i.png"%LOG_COUNT), depth)
np.save(osp.join(LOG_DIR,"xyz%i.npy"%LOG_COUNT), new_xyz)
################################
redprint("Finding closest demonstration")
if args.select_manual:
seg_name = find_closest_manual(demofile, new_xyz)
else:
seg_name = find_closest_auto(demofile, new_xyz)
seg_info = demofile[seg_name]
redprint("closest demo: %s"%(seg_name))
if "done" in seg_name:
redprint("DONE!")
break
if args.log:
with open(osp.join(LOG_DIR,"neighbor%i.txt"%LOG_COUNT),"w") as fh: fh.write(seg_name)
################################
### Old end effector forces at r_gripper_tool_frame (eliminating the torques for now)
old_eefs = seg_info['end_effector_forces'][:,0:3,:]
redprint("Generating end-effector trajectory")
handles = []
old_xyz = np.squeeze(seg_info["cloud_xyz"])
scaled_old_xyz, src_params = registration.unit_boxify(old_xyz)
scaled_new_xyz, targ_params = registration.unit_boxify(new_xyz)
f,_ = registration.tps_rpm_bij(scaled_old_xyz, scaled_new_xyz, plot_cb = tpsrpm_plot_cb,
plotting=5 if args.animation else 0,rot_reg=np.r_[1e-4,1e-4,1e-1], n_iter=50, reg_init=10, reg_final=.1)
f = registration.unscale_tps(f, src_params, targ_params)
old_xyz_transformed = f.transform_points(old_xyz)
#handles.append(Globals.env.plot3(old_xyz_transformed,5, np.array([(0,0,1,1) for i in old_xyz_transformed])))
handles.extend(plotting_openrave.draw_grid(Globals.env, f.transform_points, old_xyz.min(axis=0)-np.r_[0,0,.1], old_xyz.max(axis=0)+np.r_[0,0,.1], xres = .1, yres = .1, zres = .04))
link2eetraj = {}
for lr in 'r':
link_name = "%s_gripper_tool_frame"%lr
old_ee_traj = asarray(seg_info[link_name]["hmat"])
new_ee_traj = f.transform_hmats(old_ee_traj)
link2eetraj[link_name] = new_ee_traj
#print old_ee_traj
old_ee_pos = old_ee_traj[:,0:3,3]
#print old_ee_pos
# End effector forces as oppossed to end effector trajectories
dfdxs = f.compute_jacobian(old_ee_pos)
new_eefs = []
for i in xrange(len(old_eefs)):
dfdx = np.matrix(dfdxs[i])
old_eef = np.matrix(old_eefs[i])
new_eefs.append(dfdx * old_eef)
old_eefs = asarray(old_eefs)[:,:,0]
new_eefs = asarray(new_eefs)[:,:,0]
force_data = {}
force_data['old_eefs'] = old_eefs
force_data['new_eefs'] = new_eefs
force_file = open("trial.pickle", 'wa')
pickle.dump(force_data, force_file)
force_file.close()
new_ee_traj_x = new_ee_traj
miniseg_starts, miniseg_ends = split_trajectory_by_gripper(seg_info)
success = True
print colorize.colorize("mini segments:", "red"), miniseg_starts, miniseg_ends
for (i_miniseg, (i_start, i_end)) in enumerate(zip(miniseg_starts, miniseg_ends)):
if args.execution=="real": Globals.pr2.update_rave()
################################
redprint("Generating joint trajectory for segment %s, part %i"%(seg_name, i_miniseg))
# figure out how we're gonna resample stuff
lr2oldtraj = {}
for lr in 'r':
manip_name = {"l":"leftarm", "r":"rightarm"}[lr]
old_joint_traj = asarray(seg_info[manip_name][i_start:i_end+1])
#print (old_joint_traj[1:] - old_joint_traj[:-1]).ptp(axis=0), i_start, i_end
#if arm_moved(old_joint_traj): NOT SURE WHY BUT THIS IS RETURNING FALSE
lr2oldtraj[lr] = old_joint_traj
if args.visualize:
r2r = ros2rave.RosToRave(Globals.robot, asarray(seg_info["joint_states"]["name"]))
r2r.set_values(Globals.robot, asarray(seg_info["joint_states"]["position"][0]))
for i in range(0, lr2oldtraj['r'].shape[0], 10):
handles = []
handles.append(Globals.env.plot3(new_xyz,5,np.array([(0,1,0,1) for x in new_xyz])))
handles.append(Globals.env.plot3(old_xyz,5,np.array([(1,0,0,1) for x in old_xyz])))
handles.append(Globals.env.drawlinestrip(old_ee_traj[:,:3,3], 2, (1,0,0,1)))
# Setting robot arm to joint trajectory
r_vals = lr2oldtraj['r'][i,:]
Globals.robot.SetDOFValues(r_vals, Globals.robot.GetManipulator('rightarm').GetArmIndices())
# Plotting forces from r_gripper_tool_frame
hmats = Globals.robot.GetLinkTransformations()
trans, rot = conv.hmat_to_trans_rot(hmats[-3])
f_start = np.array([0,0,0]) + trans
f_end = np.array(old_eefs[i])/100 + trans
handles.append(Globals.env.drawlinestrip(np.array([f_start, f_end]), 10, (1,0,0,1)))
redprint(i)
Globals.viewer.Step()
if len(lr2oldtraj) > 0:
old_total_traj = np.concatenate(lr2oldtraj.values(), 1)
JOINT_LENGTH_PER_STEP = .1
# FIRST RESAMPLING HAPPENS HERE: JOINT_LENGTH
_, timesteps_rs = unif_resample(old_total_traj, JOINT_LENGTH_PER_STEP) # Timesteps only, can use to inter eefs for first time
####
new_eefs_segment = asarray(new_eefs[i_start:i_end+1,:]) # Extract miniseg, and re-sample
if args.no_ds:
new_eefs_segment_rs = new_eefs_segment
else:
new_eefs_segment_rs = mu.interp2d(timesteps_rs, np.arange(len(new_eefs_segment)), new_eefs_segment)
### Generate fullbody traj
bodypart2traj = {}
for (lr,old_joint_traj) in lr2oldtraj.items():
manip_name = {"l":"leftarm", "r":"rightarm"}[lr]
ee_link_name = "%s_gripper_tool_frame"%lr
new_ee_traj = link2eetraj[ee_link_name][i_start:i_end+1]
if args.no_ds:
old_joint_traj_rs = old_joint_traj
new_ee_traj_rs = new_ee_traj
ds_inds = np.arange(0, len(new_ee_traj_rs), args.trajopt_ds)
new_ee_traj_rs = new_ee_traj_rs[ds_inds]
old_joint_traj_rs = old_joint_traj_rs[ds_inds]
new_joint_traj = planning.plan_follow_traj(Globals.robot, manip_name,
Globals.robot.GetLink(ee_link_name), new_ee_traj_rs,old_joint_traj_rs)
new_joint_traj = mu.interp2d(np.arange(len(old_joint_traj)), np.arange(0, len(new_joint_traj) * args.trajopt_ds, args.trajopt_ds), new_joint_traj)
else:
old_joint_traj_rs = mu.interp2d(timesteps_rs, np.arange(len(old_joint_traj)), old_joint_traj)
new_ee_traj_rs = resampling.interp_hmats(timesteps_rs, np.arange(len(new_ee_traj)), new_ee_traj)
new_joint_traj = planning.plan_follow_traj(Globals.robot, manip_name,
Globals.robot.GetLink(ee_link_name), new_ee_traj_rs,old_joint_traj_rs)
if args.execution: Globals.pr2.update_rave()
part_name = {"l":"larm", "r":"rarm"}[lr]
bodypart2traj[part_name] = new_joint_traj
redprint("Joint trajectory has length: " + str(len(bodypart2traj[part_name])))
redprint("Executing joint trajectory for segment %s, part %i using arms '%s'"%(seg_name, i_miniseg, bodypart2traj.keys()))
if args.pid:
if not args.fake_data_segment: # If running on PR2, add initial state as waypoint and rough interpolate
# Add initial position
(arm_position, arm_velocity, arm_effort) = robot_states.call_return_joint_states(robot_states.arm_joint_names)
traj_length = bodypart2traj['rarm'].shape[0]
complete_joint_traj = np.zeros((traj_length+1, 7)) # To include initial state as a way point
complete_joint_traj[1:,:] = bodypart2traj['rarm']
complete_joint_traj[0,:] = arm_position
bodypart2traj['rarm'] = complete_joint_traj
# Add initial eff
J = np.matrix(np.resize(np.array(robot_states.call_return_jacobian()), (6, 7))) # Jacobian
eff = robot_states.compute_end_effector_force(J, arm_effort).T
eff = np.array(eff)[0]
init_force = eff[:3]
complete_force_traj = np.zeros((traj_length+1, 3))
complete_force_traj[1:,:] = new_eefs_segment_rs
complete_force_traj[0,:] = init_force
else:
complete_force_traj = new_eefs_segment_rs
# SECOND RESAMPLING HAPPENS HERE: JOINT VELOCITIES
if args.no_ds:
traj = bodypart2traj['rarm']
stamps = asarray(seg_info['stamps'])
times = np.array([stamps[i_end] - stamps[i_start]])
force = complete_force_traj
else:
times, times_up, traj = pr2_trajectories.return_timed_traj(Globals.pr2, bodypart2traj) # use times and times_up to interpolate the second time
force = mu.interp2d(times_up, times, complete_force_traj)
#Globals.robot.SetDOFValues(asarray(fake_seg["joint_states"]["position"][0]))
if args.visualize:
r2r = ros2rave.RosToRave(Globals.robot, asarray(seg_info["joint_states"]["name"]))
r2r.set_values(Globals.robot, asarray(seg_info["joint_states"]["position"][0]))
for i in range(0, traj.shape[0], 10):
handles = []
handles.append(Globals.env.plot3(new_xyz,5,np.array([(0,1,0,1) for x in new_xyz])))
handles.append(Globals.env.plot3(old_xyz,5,np.array([(1,0,0,1) for x in old_xyz])))
handles.append(Globals.env.drawlinestrip(old_ee_traj[:,:3,3], 2, (1,0,0,1)))
handles.append(Globals.env.drawlinestrip(new_ee_traj[:,:3,3], 2, (0,1,0,1)))
handles.append(Globals.env.drawlinestrip(new_ee_traj_rs[:,:3,3], 2, (0,0,1,1)))
# Setting robot arm to joint trajectory
r_vals = traj[i,:]
Globals.robot.SetDOFValues(r_vals, Globals.robot.GetManipulator('rightarm').GetArmIndices())
# Plotting forces from r_gripper_tool_frame
hmats = Globals.robot.GetLinkTransformations()
trans, rot = conv.hmat_to_trans_rot(hmats[-3])
f_start = np.array([0,0,0]) + trans
f_end = np.array(force[i])/100 + trans
handles.append(Globals.env.drawlinestrip(np.array([f_start, f_end]), 10, (1,0,0,1)))
redprint(i)
Globals.viewer.Step()
traj = np.array(np.matrix(traj).T) # 7 x n
redprint(traj)
traj = np.resize(traj, (1, traj.shape[1]*7)) #resize the data to 1x7n (n being the number of steps)
force = np.array(np.matrix(force).T) # 3 x n
force = np.resize(force, (1, force.shape[1]*3)) #resize the data to 1x3n
#[traj, force, secs]
traj_force_secs = np.zeros((1, traj.shape[1] + force.shape[1] + 2))
traj_force_secs[0,0:traj.shape[1]] = traj
traj_force_secs[0,traj.shape[1]:traj.shape[1]+force.shape[1]] = force
traj_force_secs[0,traj.shape[1]+force.shape[1]] = times[len(times)-1]
traj_force_secs[0,traj.shape[1]+force.shape[1]+1] = args.use_force
IPython.embed()
msg = Float64MultiArray()
msg.data = traj_force_secs[0].tolist()
pub = rospy.Publisher("/joint_positions_forces_secs", Float64MultiArray)
redprint("Press enter to start trajectory")
raw_input()
time.sleep(1)
pub.publish(msg)
time.sleep(1)
else:
#if not success: break
if len(bodypart2traj) > 0:
exec_traj_maybesim(bodypart2traj)
def follow_body_traj(pr2,
bodypart2traj,
wait=True,
base_frame="/base_footprint",
speed_factor=1):
rospy.loginfo("following trajectory with bodyparts %s",
" ".join(bodypart2traj.keys()))
name2part = {
"lgrip": pr2.lgrip,
"rgrip": pr2.rgrip,
"larm": pr2.larm,
"rarm": pr2.rarm,
"base": pr2.base
}
for partname in bodypart2traj:
if partname not in name2part:
raise Exception("invalid part name %s" % partname)
#### Go to initial positions #######
for (name, part) in name2part.items():
if name in bodypart2traj:
part_traj = bodypart2traj[name]
if name == "lgrip" or name == "rgrip":
part.set_angle(np.squeeze(part_traj)[0])
elif name == "larm" or name == "rarm":
part.goto_joint_positions(part_traj[0])
elif name == "base":
part.goto_pose(part_traj[0], base_frame)
pr2.join_all()
#### Construct total trajectory so we can retime it #######
n_dof = 0
trajectories = []
vel_limits = []
acc_limits = []
bodypart2inds = {}
for (name, part) in name2part.items():
if name in bodypart2traj:
traj = bodypart2traj[name]
if traj.ndim == 1: traj = traj.reshape(-1, 1)
trajectories.append(traj)
vel_limits.extend(part.vel_limits)
acc_limits.extend(part.acc_limits)
bodypart2inds[name] = range(n_dof, n_dof + part.n_joints)
n_dof += part.n_joints
trajectories = np.concatenate(trajectories, 1)
vel_limits = np.array(vel_limits) * speed_factor
times = retiming.retime_with_vel_limits(trajectories, vel_limits)
times_up = np.linspace(0, times[-1], int(np.ceil(times[-1] / .1)))
traj_up = mu.interp2d(times_up, times, trajectories)
#### Send all part trajectories ###########
for (name, part) in name2part.items():
if name in bodypart2traj:
part_traj = traj_up[:, bodypart2inds[name]]
if name == "lgrip" or name == "rgrip":
part.follow_timed_trajectory(times_up, part_traj.flatten())
elif name == "larm" or name == "rarm":
vels = ku.get_velocities(part_traj, times_up, .001)
part.follow_timed_joint_trajectory(part_traj, vels, times_up)
elif name == "base":
part.follow_timed_trajectory(times_up, part_traj, base_frame)
if wait: pr2.join_all()
return True
def follow_body_traj(pr2, bodypart2traj, wait=True, base_frame="/base_footprint", speed_factor=1):
rospy.loginfo("following trajectory with bodyparts %s", " ".join(bodypart2traj.keys()))
name2part = {"lgrip": pr2.lgrip, "rgrip": pr2.rgrip, "larm": pr2.larm, "rarm": pr2.rarm, "base": pr2.base}
for partname in bodypart2traj:
if partname not in name2part:
raise Exception("invalid part name %s" % partname)
#### Go to initial positions #######
for (name, part) in name2part.items():
if name in bodypart2traj:
part_traj = bodypart2traj[name]
if name == "lgrip" or name == "rgrip":
part.set_angle(np.squeeze(part_traj)[0])
elif name == "larm" or name == "rarm":
part.goto_joint_positions(part_traj[0])
elif name == "base":
part.goto_pose(part_traj[0], base_frame)
pr2.join_all()
#### Construct total trajectory so we can retime it #######
n_dof = 0
trajectories = []
vel_limits = []
acc_limits = []
bodypart2inds = {}
for (name, part) in name2part.items():
if name in bodypart2traj:
traj = bodypart2traj[name]
if traj.ndim == 1:
traj = traj.reshape(-1, 1)
trajectories.append(traj)
vel_limits.extend(part.vel_limits)
acc_limits.extend(part.acc_limits)
bodypart2inds[name] = range(n_dof, n_dof + part.n_joints)
n_dof += part.n_joints
trajectories = np.concatenate(trajectories, 1)
vel_limits = np.array(vel_limits) * speed_factor
times = retiming.retime_with_vel_limits(trajectories, vel_limits)
times_up = np.linspace(0, times[-1], int(np.ceil(times[-1] / 0.1)))
traj_up = mu.interp2d(times_up, times, trajectories)
#### Send all part trajectories ###########
for (name, part) in name2part.items():
if name in bodypart2traj:
part_traj = traj_up[:, bodypart2inds[name]]
if name == "lgrip" or name == "rgrip":
part.follow_timed_trajectory(times_up, part_traj.flatten())
elif name == "larm" or name == "rarm":
vels = ku.get_velocities(part_traj, times_up, 0.001)
part.follow_timed_joint_trajectory(part_traj, vels, times_up)
elif name == "base":
part.follow_timed_trajectory(times_up, part_traj, base_frame)
if wait:
pr2.join_all()
return True
def main():
demofile = h5py.File(args.h5file, 'r')
trajoptpy.SetInteractive(args.interactive)
if args.execution:
rospy.init_node("exec_task",disable_signals=True)
Globals.pr2 = PR2.PR2()
Globals.env = Globals.pr2.env
Globals.robot = Globals.pr2.robot
else:
Globals.env = openravepy.Environment()
Globals.env.StopSimulation()
Globals.env.Load("robots/pr2-beta-static.zae")
Globals.robot = Globals.env.GetRobots()[0]
if not args.fake_data_segment:
grabber = cloudprocpy.CloudGrabber()
grabber.startRGBD()
Globals.viewer = trajoptpy.GetViewer(Globals.env)
#####################
started_bag = False
started_video = False
localtime = time.localtime()
time_string = time.strftime("%Y-%m-%d-%H-%M-%S", localtime)
os.chdir(osp.dirname(args.new_demo))
if not osp.exists(args.new_demo):
yn = yes_or_no("master file does not exist. create?")
basename = raw_input("what is the base name?\n").strip()
if yn:
with open(args.new_demo,'w') as fh: fh.write("""
name: %s
h5path: %s
bags:
"""%(basename, basename+".h5"))
else:
print "exiting."
exit(0)
with open(args.new_demo, "r") as fh: master_info = yaml.load(fh)
if master_info["bags"] is None: master_info["bags"] = []
for suffix in itertools.chain("", (str(i) for i in itertools.count())):
demo_name = args.demo_prefix + suffix
if not any(bag["demo_name"] == demo_name for bag in master_info["bags"]):
break
print demo_name
timestampfile = demo_name+"timestamps.txt"
fht = open(timestampfile,"w")
try:
bag_cmd = "rosbag record /joint_states /joy -O %s"%demo_name
#print colorize(bag_cmd, "green")
bag_handle = subprocess.Popen(bag_cmd, shell=True)
time.sleep(1)
poll_result = bag_handle.poll()
print "poll result", poll_result
if poll_result is not None:
raise Exception("problem starting video recording")
else: started_bag = True
video_cmd = "record_rgbd_video --out=%s --downsample=%i"%(demo_name, args.downsample)
#print colorize(video_cmd, "green")
video_handle = subprocess.Popen(video_cmd, shell=True)
started_video = True
#grab_end(new_xyz)
fht.write('look:' + str(rospy.get_rostime().secs))
################################
redprint("Finding closest demonstration")
seg_name = find_closest_manual(demofile, None)
seg_info = demofile[seg_name]
redprint("Generating end-effector trajectory")
old_xyz = np.squeeze(seg_info["cloud_xyz"])
scaled_old_xyz, src_params = registration.unit_boxify(old_xyz)
link2eetraj = {}
for lr in 'lr':
link_name = "%s_gripper_tool_frame"%lr
old_ee_traj = asarray(seg_info[link_name]["hmat"])
miniseg_starts, miniseg_ends = split_trajectory_by_gripper(seg_info)
success = True
for (i_miniseg, (i_start, i_end)) in enumerate(zip(miniseg_starts, miniseg_ends)):
if args.execution=="real": Globals.pr2.update_rave()
################################
redprint("Generating joint trajectory for segment %s, part %i"%(seg_name, i_miniseg))
# figure out how we're gonna resample stuff
lr2oldtraj = {}
for lr in 'lr':
manip_name = {"l":"leftarm", "r":"rightarm"}[lr]
old_joint_traj = asarray(seg_info[manip_name][i_start:i_end+1])
#print (old_joint_traj[1:] - old_joint_traj[:-1]).ptp(axis=0), i_start, i_end
lr2oldtraj[lr] = old_joint_traj
### Generate fullbody traj
bodypart2traj = {}
for (lr,old_joint_traj) in lr2oldtraj.items():
manip_name = {"l":"leftarm", "r":"rightarm"}[lr]
#old_joint_traj_rs = mu.interp2d(timesteps_rs, np.arange(len(old_joint_traj)), old_joint_traj)
ee_link_name = "%s_gripper_tool_frame"%lr
#new_ee_traj = link2eetraj[ee_link_name][i_start:i_end+1]
#new_ee_traj_rs = resampling.interp_hmats(timesteps_rs, np.arange(len(new_ee_traj)), new_ee_traj)
if args.execution: Globals.pr2.update_rave()
#new_joint_traj = planning.plan_follow_traj(Globals.robot, manip_name,
# Globals.robot.GetLink(ee_link_name), new_ee_traj_rs,old_joint_traj_rs)
part_name = {"l":"larm", "r":"rarm"}[lr]
bodypart2traj[part_name] = old_joint_traj
traj = {}
for lr in 'lr':
part_name = {"l":"larm", "r":"rarm"}[lr]
traj[lr] = bodypart2traj[part_name]
if i_miniseg ==0:
redprint("Press enter to use current position as starting point")
raw_input()
arm_positions = {}
(arm_position, arm_velocity, arm_effort) = robot_states.call_return_joint_states(robot_states.r_arm_joint_names)
arm_positions['r'] = arm_position
diff_r = np.array(arm_position - traj['r'][0,:])
maximum_r = max(abs(diff_r))
(arm_position, arm_velocity, arm_effort) = robot_states.call_return_joint_states(robot_states.l_arm_joint_names)
arm_positions['l'] = arm_position
diff_l = np.array(arm_position - traj['l'][0,:])
maximum_l = max(abs(diff_l))
maximum = max(maximum_l, maximum_r)
speed = (20.0/360.0*2*(np.pi))
time_needed = maximum / speed
initial_pos_traj = {}
for lr in 'lr':
part_name = {"l":"larm", "r":"rarm"}[lr]
initial_pos_traj[part_name] = mu.interp2d(np.arange(0, time_needed, 0.01), np.array([0,time_needed]), np.array([arm_positions[lr], traj[lr][0,:]]))
#initial_traj_length = initial_pos_traj.shape[0]
#traj[lr] = np.concatenate((initial_pos_traj, traj_part[lr]), axis=0)
#=======================send to controller======================
length_total = initial_pos_traj["larm"].shape[0]
qs_l = np.resize(initial_pos_traj["larm"], (1, initial_pos_traj["larm"].shape[0]*7))[0] #resize the data to 1x7n (n being the number of steps)
qs_r = np.resize(initial_pos_traj["rarm"], (1, initial_pos_traj["rarm"].shape[0]*7))[0]
#F = np.array(np.matrix(F).T) # 6 x n
F = np.zeros((length_total,6))
F = np.resize(F, (1, F.shape[0]*6))[0] #resize the data to 1x3n
gripper = np.zeros((length_total,1))
gripper = np.resize(gripper, (1, gripper.shape[0]*1))[0]
# Controller code in joint space
pgains = np.asarray([2400.0, 1200.0, 1000.0, 700.0, 300.0, 300.0, 300.0])
dgains = np.asarray([18.0, 10.0, 6.0, 4.0, 6.0, 4.0, 4.0])
# Gains as Kps and Kvs for testing
Kps = []
Kvs = []
for i in range(length_total):
Kps.append(np.zeros((6,6)))
Kvs.append(np.zeros((6,6)))
toAddJkpJ = np.diag(np.asarray([-2400.0, -1200.0, -1000.0, -700.0, -300.0, -300.0, -300.0]))
toAddJkvJ = np.diag(np.asarray([-18.0, -10.0, -6.0, -4.0, -6.0, -4.0, -4.0]))
#toAddJkvJ = np.diag(np.asarray([0, 0, 0, 0, 0, 0, 0]))
#length = complete_force_traj.shape[0]
JKpJ = np.asarray([toAddJkpJ for i in range(length_total)])
JKpJ = np.resize(JKpJ, (1, 49*length_total))[0]
JKvJ = np.asarray([toAddJkvJ for i in range(length_total)])
JKvJ = np.resize(JKvJ, (1, 49*length_total))[0]
# [traj, Kp, Kv, F, use_force, seconds]
Kps = np.resize(Kps, (1, 36 * length_total))[0]
Kvs = np.resize(Kvs, (1, 36 * length_total))[0]
#JKpJ = np.resize(JKpJ, (1, 49 * num_steps))[0]
#JKvJ = np.resize(JKvJ, (1, 49 * num_steps))[0]
stamps = asarray(seg_info['stamps'])
data = np.zeros((1, length_total*(7+49+49+6+36+36+7+49+49+6+36+36+1)+2))
data[0][0:length_total*7] = qs_r
data[0][length_total*7:length_total*(7+49)] = JKpJ
data[0][length_total*(7+49):length_total*(7+49+49)] = JKvJ
data[0][length_total*(7+49+49):length_total*(7+49+49+6)] = F
data[0][length_total*(7+49+49+6):length_total*(7+49+49+6+36)] = Kps
data[0][length_total*(7+49+49+6+36):length_total*(7+49+49+6+36+36)] = Kvs
data[0][length_total*(7+49+49+6+36+36):length_total*(7+49+49+6+36+36+7)] = qs_l
data[0][length_total*(7+49+49+6+36+36+7):length_total*(7+49+49+6+36+36+7+49)] = JKpJ
data[0][length_total*(7+49+49+6+36+36+7+49):length_total*(7+49+49+6+36+36+7+49+49)] = JKvJ
data[0][length_total*(7+49+49+6+36+36+7+49+49):length_total*(7+49+49+6+36+36+7+49+49+6)] = F
data[0][length_total*(7+49+49+6+36+36+7+49+49+6):length_total*(7+49+49+6+36+36+7+49+49+6+36)] = Kps
data[0][length_total*(7+49+49+6+36+36+7+49+49+6+36):length_total*(7+49+49+6+36+36+7+49+49+6+36+36)] = Kvs
data[0][length_total*(7+49+49+6+36+36+7+49+49+6+36+36):length_total*(7+49+49+6+36+36+7+49+49+6+36+36+1)] = gripper
data[0][-2] = 0
data[0][-1] = stamps[i_end] - stamps[i_start]
msg = Float64MultiArray()
msg.data = data[0].tolist()
pub = rospy.Publisher("/controller_data", Float64MultiArray)
redprint("Press enter to start trajectory")
raw_input()
time.sleep(1)
pub.publish(msg)
time.sleep(1)
#===================end send to controller=======================
raw_input("CAME TO START POSITION")
time.sleep(1)
fht.write('\nstart:' + str(rospy.get_rostime().secs))
################################
redprint("Executing joint trajectory for segment %s, part %i using arms '%s'"%(seg_name, i_miniseg, bodypart2traj.keys()))
if not args.useHenry:
for lr in 'lr':
success &= set_gripper_maybesim(lr, binarize_gripper(seg_info["%s_gripper_joint"%lr][i_start]))
# Doesn't actually check if grab occurred, unfortunately
print('1')
fht.write('\nstart:' + str(rospy.get_rostime().secs))
print('2')
if len(bodypart2traj) > 0:
success &= exec_traj_maybesim(bodypart2traj)
print('3')
fht.write('\nstop:' + str(rospy.get_rostime().secs))
print('4')
else:
for lr in 'lr':
redprint("Press enter to set gripper")
raw_input()
set_gripper_maybesim(lr, binarize_gripper(seg_info["%s_gripper_joint"%lr][i_start]))
# Doesn't actually check if grab occurred, unfortunately
if bodypart2traj!={}:
length = i_end - i_start + 1
length_total = length
traj_r = traj['r']
qs_r = np.resize(traj_r, (1, traj_r.shape[0]*7))[0] #resize the data to 1x7n (n being the number of steps)
traj_l = traj['l']
qs_l = np.resize(traj_l, (1, traj_l.shape[0]*7))[0] #resize the data to 1x7n (n being the number of steps)
#F = np.array(np.matrix(F).T) # 6 x n
F = np.zeros((length_total,6))
F = np.resize(F, (1, F.shape[0]*6))[0] #resize the data to 1x3n
gripper = np.zeros((length_total,1))
gripper = np.resize(gripper, (1, gripper.shape[0]*1))[0]
# Controller code in joint space
pgains = np.asarray([2400.0, 1200.0, 1000.0, 700.0, 300.0, 300.0, 300.0])
dgains = np.asarray([18.0, 10.0, 6.0, 4.0, 6.0, 4.0, 4.0])
# Gains as Kps and Kvs for testing
Kps = []
Kvs = []
for i in range(length_total):
Kps.append(np.zeros((6,6)))
Kvs.append(np.zeros((6,6)))
toAddJkpJ = np.diag(np.asarray([-2400.0, -1200.0, -1000.0, -700.0, -300.0, -300.0, -300.0]))
toAddJkvJ = np.diag(np.asarray([-18.0, -10.0, -6.0, -4.0, -6.0, -4.0, -4.0]))
#toAddJkvJ = np.diag(np.asarray([0, 0, 0, 0, 0, 0, 0]))
#length = complete_force_traj.shape[0]
JKpJ = np.asarray([toAddJkpJ for i in range(length_total)])
JKpJ = np.resize(JKpJ, (1, 49*length_total))[0]
JKvJ = np.asarray([toAddJkvJ for i in range(length_total)])
JKvJ = np.resize(JKvJ, (1, 49*length_total))[0]
# [traj, Kp, Kv, F, use_force, seconds]
Kps = np.resize(Kps, (1, 36 * length_total))[0]
Kvs = np.resize(Kvs, (1, 36 * length_total))[0]
#JKpJ = np.resize(JKpJ, (1, 49 * num_steps))[0]
#JKvJ = np.resize(JKvJ, (1, 49 * num_steps))[0]
stamps = asarray(seg_info['stamps'])
data = np.zeros((1, length_total*(7+49+49+6+36+36+7+49+49+6+36+36+1)+2))
data[0][0:length_total*7] = qs_r
data[0][length_total*7:length_total*(7+49)] = JKpJ
data[0][length_total*(7+49):length_total*(7+49+49)] = JKvJ
data[0][length_total*(7+49+49):length_total*(7+49+49+6)] = F
data[0][length_total*(7+49+49+6):length_total*(7+49+49+6+36)] = Kps
data[0][length_total*(7+49+49+6+36):length_total*(7+49+49+6+36+36)] = Kvs
data[0][length_total*(7+49+49+6+36+36):length_total*(7+49+49+6+36+36+7)] = qs_l
data[0][length_total*(7+49+49+6+36+36+7):length_total*(7+49+49+6+36+36+7+49)] = JKpJ
data[0][length_total*(7+49+49+6+36+36+7+49):length_total*(7+49+49+6+36+36+7+49+49)] = JKvJ
data[0][length_total*(7+49+49+6+36+36+7+49+49):length_total*(7+49+49+6+36+36+7+49+49+6)] = F
data[0][length_total*(7+49+49+6+36+36+7+49+49+6):length_total*(7+49+49+6+36+36+7+49+49+6+36)] = Kps
data[0][length_total*(7+49+49+6+36+36+7+49+49+6+36):length_total*(7+49+49+6+36+36+7+49+49+6+36+36)] = Kvs
data[0][length_total*(7+49+49+6+36+36+7+49+49+6+36+36):length_total*(7+49+49+6+36+36+7+49+49+6+36+36+1)] = gripper
data[0][-2] = 0
data[0][-1] = stamps[i_end] - stamps[i_start]
msg = Float64MultiArray()
msg.data = data[0].tolist()
pub = rospy.Publisher("/controller_data", Float64MultiArray)
redprint("Press enter to start trajectory")
raw_input()
time.sleep(1)
pub.publish(msg)
time.sleep(1)
#if not success: break
print("Segment %s result: %s"%(seg_name, success))
print("exit loop")
for i in range(100):
time.sleep(1)
except KeyboardInterrupt:
redprint("=================================DONE================================")
raw_input()
raw_input()
raw_input()
fht.write('\nstop:' + str(rospy.get_rostime().secs))
fht.write('\ndone:' + str(rospy.get_rostime().secs))
fht.close()
time.sleep(3)
if started_bag:
print "stopping bag"
bag_handle.send_signal(signal.SIGINT)
#bag_handle.wait()
started_bag = False
if started_video:
print "stopping video"
video_handle.send_signal(signal.SIGINT)
#video_handle.wait()
started_video = False
bagfilename = demo_name+".bag"
annfilename = demo_name+".ann.yaml"
call_and_print("generate_ann.py %s %s %s"%(bagfilename, annfilename, timestampfile),check=False)
with open(args.new_demo,"a") as fh1:
fh1.write("\n"
"- bag_file: %(bagfilename)s\n"
" annotation_file: %(annfilename)s\n"
" video_dir: %(videodir)s\n"
" demo_name: %(demoname)s"%dict(bagfilename=bagfilename, annfilename=annfilename, videodir=demo_name, demoname=demo_name))
return
def main():
import IPython
demofile = h5py.File(args.h5file, 'r')
trajoptpy.SetInteractive(args.interactive)
if args.log:
LOG_DIR = osp.join(osp.expanduser("~/Data/do_task_logs"), datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
os.mkdir(LOG_DIR)
LOG_COUNT = 0
if args.execution:
rospy.init_node("exec_task",disable_signals=True)
Globals.pr2 = PR2.PR2()
Globals.env = Globals.pr2.env
Globals.robot = Globals.pr2.robot
else:
Globals.env = openravepy.Environment()
Globals.env.StopSimulation()
Globals.env.Load("robots/pr2-beta-static.zae")
Globals.robot = Globals.env.GetRobots()[0]
if not args.eval.fake_data_segment:
grabber = cloudprocpy.CloudGrabber()
grabber.startRGBD()
Globals.viewer = trajoptpy.GetViewer(Globals.env)
#####################
while True:
redprint("Acquire point cloud")
if args.eval.fake_data_segment:
fake_seg = demofile[args.eval.fake_data_segment]
new_xyz = np.squeeze(fake_seg["cloud_xyz"])
hmat = openravepy.matrixFromAxisAngle(args.eval.fake_data_transform[3:6])
hmat[:3,3] = args.eval.fake_data_transform[0:3]
new_xyz = new_xyz.dot(hmat[:3,:3].T) + hmat[:3,3][None,:]
r2r = ros2rave.RosToRave(Globals.robot, asarray(fake_seg["joint_states"]["name"]))
r2r.set_values(Globals.robot, asarray(fake_seg["joint_states"]["position"][0]))
#Globals.pr2.head.set_pan_tilt(0,1.2)
#Globals.pr2.rarm.goto_posture('side')
#Globals.pr2.larm.goto_posture('side')
#Globals.pr2.join_all()
#time.sleep(2)
else:
#Globals.pr2.head.set_pan_tilt(0,1.2)
#Globals.pr2.rarm.goto_posture('side')
#Globals.pr2.larm.goto_posture('side')
#Globals.pr2.join_all()
#time.sleep(2)
Globals.pr2.update_rave()
rgb, depth = grabber.getRGBD()
T_w_k = berkeley_pr2.get_kinect_transform(Globals.robot)
new_xyz = cloud_proc_func(rgb, depth, T_w_k)
print "got new xyz"
redprint(new_xyz)
#grab_end(new_xyz)
if args.log:
LOG_COUNT += 1
import cv2
cv2.imwrite(osp.join(LOG_DIR,"rgb%i.png"%LOG_COUNT), rgb)
cv2.imwrite(osp.join(LOG_DIR,"depth%i.png"%LOG_COUNT), depth)
np.save(osp.join(LOG_DIR,"xyz%i.npy"%LOG_COUNT), new_xyz)
################################
redprint("Finding closest demonstration")
if args.select_manual:
seg_name = find_closest_manual(demofile, new_xyz)
else:
seg_name = find_closest_auto(demofile, new_xyz)
seg_info = demofile[seg_name]
redprint("closest demo: %s"%(seg_name))
if "done" in seg_name:
redprint("DONE!")
break
if args.log:
with open(osp.join(LOG_DIR,"neighbor%i.txt"%LOG_COUNT),"w") as fh: fh.write(seg_name)
################################
### Old end effector forces at r_gripper_tool_frame (including torques)
old_forces = seg_info['end_effector_forces'][:,0:3,:]
old_torques = seg_info['end_effector_forces'][:,3:6,:]
redprint("Generating end-effector trajectory")
handles = []
old_xyz = np.squeeze(seg_info["cloud_xyz"])
scaled_old_xyz, src_params = registration.unit_boxify(old_xyz)
scaled_new_xyz, targ_params = registration.unit_boxify(new_xyz)
f,_ = registration.tps_rpm_bij(scaled_old_xyz, scaled_new_xyz, plot_cb = tpsrpm_plot_cb,
plotting=5 if args.animation else 0,rot_reg=np.r_[1e-4,1e-4,1e-1], n_iter=50, reg_init=10, reg_final=.1)
f = registration.unscale_tps(f, src_params, targ_params)
old_xyz_transformed = f.transform_points(old_xyz)
#handles.append(Globals.env.plot3(old_xyz_transformed,5, np.array([(0,0,1,1) for i in old_xyz_transformed])))
handles.extend(plotting_openrave.draw_grid(Globals.env, f.transform_points, old_xyz.min(axis=0)-np.r_[0,0,.1], old_xyz.max(axis=0)+np.r_[0,0,.1], xres = .1, yres = .1, zres = .04))
link2eetraj = {}
for lr in 'r':
link_name = "%s_gripper_tool_frame"%lr
old_ee_traj = asarray(seg_info[link_name]["hmat"])
new_ee_traj = f.transform_hmats(old_ee_traj)
link2eetraj[link_name] = new_ee_traj
#print old_ee_traj
old_ee_pos = old_ee_traj[:,0:3,3]
#print old_ee_pos
# End effector forces as oppossed to end effector trajectories
dfdxs = f.compute_jacobian(old_ee_pos)
old_eefs = []
new_eefs = []
for i in xrange(len(old_forces)):
dfdx = np.matrix(dfdxs[i])
old_force = np.matrix(old_forces[i])
old_torque = np.matrix(old_torques[i])
new_force = (dfdx * old_force)
new_torque = (dfdx * old_torque)
old_eefs.append(np.vstack((old_force, old_torque)))
new_eefs.append(np.vstack((new_force, new_torque)))
old_eefs = np.asarray(old_eefs)[:,:,0]
new_eefs = np.asarray(new_eefs)[:,:,0]
force_data = {}
force_data['old_eefs'] = old_eefs
force_data['new_eefs'] = new_eefs
force_file = open("trial.pickle", 'wa')
pickle.dump(force_data, force_file)
force_file.close()
new_ee_traj_x = new_ee_traj
miniseg_starts, miniseg_ends = split_trajectory_by_gripper(seg_info)
success = True
print colorize.colorize("mini segments:", "red"), miniseg_starts, miniseg_ends
for (i_miniseg, (i_start, i_end)) in enumerate(zip(miniseg_starts, miniseg_ends)):
if args.execution=="real": Globals.pr2.update_rave()
################################
redprint("Generating joint trajectory for segment %s, part %i"%(seg_name, i_miniseg))
# figure out how we're gonna resample stuff
lr2oldtraj = {}
for lr in 'r':
manip_name = {"l":"leftarm", "r":"rightarm"}[lr]
old_joint_traj = asarray(seg_info[manip_name][i_start:i_end+1])
#print (old_joint_traj[1:] - old_joint_traj[:-1]).ptp(axis=0), i_start, i_end
#if arm_moved(old_joint_traj): NOT SURE WHY BUT THIS IS RETURNING FALSE
lr2oldtraj[lr] = old_joint_traj
if args.visualize:
r2r = ros2rave.RosToRave(Globals.robot, asarray(seg_info["joint_states"]["name"]))
r2r.set_values(Globals.robot, asarray(seg_info["joint_states"]["position"][0]))
for i in range(0, lr2oldtraj['r'].shape[0], 10):
handles = []
handles.append(Globals.env.plot3(new_xyz,5,np.array([(0,1,0,1) for x in new_xyz])))
handles.append(Globals.env.plot3(old_xyz,5,np.array([(1,0,0,1) for x in old_xyz])))
handles.append(Globals.env.drawlinestrip(old_ee_traj[:,:3,3], 2, (1,0,0,1)))
# Setting robot arm to joint trajectory
r_vals = lr2oldtraj['r'][i,:]
Globals.robot.SetDOFValues(r_vals, Globals.robot.GetManipulator('rightarm').GetArmIndices())
# Plotting forces from r_gripper_tool_frame
hmats = Globals.robot.GetLinkTransformations()
trans, rot = conv.hmat_to_trans_rot(hmats[-3])
f_start = np.array([0,0,0]) + trans
#IPython.embed()
f_end = np.array(old_forces[i].T)[0]/100 + trans
handles.append(Globals.env.drawlinestrip(np.array([f_start, f_end]), 10, (1,0,0,1)))
redprint(i)
Globals.viewer.Step()
if len(lr2oldtraj) > 0:
old_total_traj = np.concatenate(lr2oldtraj.values(), 1)
JOINT_LENGTH_PER_STEP = .1
# FIRST RESAMPLING HAPPENS HERE: JOINT_LENGTH
_, timesteps_rs = unif_resample(old_total_traj, JOINT_LENGTH_PER_STEP) # Timesteps only, can use to inter eefs for first time
####
new_eefs_segment = asarray(new_eefs[i_start:i_end+1,:]) # Extract miniseg, and re-sample
if args.no_ds:
new_eefs_segment_rs = new_eefs_segment
else:
new_eefs_segment_rs = mu.interp2d(timesteps_rs, np.arange(len(new_eefs_segment)), new_eefs_segment)
### Generate fullbody traj
bodypart2traj = {}
for (lr,old_joint_traj) in lr2oldtraj.items():
manip_name = {"l":"leftarm", "r":"rightarm"}[lr]
ee_link_name = "%s_gripper_tool_frame"%lr
new_ee_traj = link2eetraj[ee_link_name][i_start:i_end+1]
if args.no_ds:
old_joint_traj_rs = old_joint_traj
new_ee_traj_rs = new_ee_traj
ds_inds = np.arange(0, len(new_ee_traj_rs), args.trajopt_ds)
new_ee_traj_rs = new_ee_traj_rs[ds_inds]
old_joint_traj_rs = old_joint_traj_rs[ds_inds]
new_joint_traj = planning.plan_follow_traj(Globals.robot, manip_name,
Globals.robot.GetLink(ee_link_name), new_ee_traj_rs,old_joint_traj_rs)
new_joint_traj = mu.interp2d(np.arange(len(old_joint_traj)), np.arange(0, len(new_joint_traj) * args.trajopt_ds, args.trajopt_ds), new_joint_traj)
else:
old_joint_traj_rs = mu.interp2d(timesteps_rs, np.arange(len(old_joint_traj)), old_joint_traj)
new_ee_traj_rs = resampling.interp_hmats(timesteps_rs, np.arange(len(new_ee_traj)), new_ee_traj)
new_joint_traj = planning.plan_follow_traj(Globals.robot, manip_name,
Globals.robot.GetLink(ee_link_name), new_ee_traj_rs,old_joint_traj_rs)
if args.execution: Globals.pr2.update_rave()
part_name = {"l":"larm", "r":"rarm"}[lr]
bodypart2traj[part_name] = new_joint_traj
redprint("Joint trajectory has length: " + str(len(bodypart2traj[part_name])))
redprint("Executing joint trajectory for segment %s, part %i using arms '%s'"%(seg_name, i_miniseg, bodypart2traj.keys()))
#for lr in 'r':
# set_gripper_maybesim(lr, binarize_gripper(seg_info["%s_gripper_joint"%lr][i_start]))
if args.pid:
# Add trajectory to arrive at to initial state
redprint("Press enter to use current position")
raw_input()
(arm_position, arm_velocity, arm_effort) = robot_states.call_return_joint_states(robot_states.r_arm_joint_names)
diff = np.array(arm_position - bodypart2traj['rarm'][0,:])
maximum = max(abs(diff))
speed = (300.0/360.0*2*(np.pi))
time_needed = maximum / speed
initial_pos_traj = mu.interp2d(np.arange(0, time_needed, 0.01), np.array([0,time_needed]), np.array([arm_position, bodypart2traj['rarm'][0,:]]))
# length of the intial trajectory, use this length for padding PD gains
initial_traj_length = initial_pos_traj.shape[0]
initial_force_traj = np.array([np.zeros(6) for i in range(initial_traj_length)])
temptraj = bodypart2traj['rarm']
bodypart2traj['rarm'] = np.concatenate((initial_pos_traj, temptraj), axis=0)
complete_force_traj = np.concatenate((initial_force_traj, new_eefs), axis=0)
# SECOND RESAMPLING HAPPENS HERE: JOINT VELOCITIES
if args.no_ds:
traj = bodypart2traj['rarm']
stamps = asarray(seg_info['stamps'])
times = np.array([stamps[i_end] - stamps[i_start]])
F = complete_force_traj
else:
times, times_up, traj = pr2_trajectories.return_timed_traj(Globals.pr2, bodypart2traj) # use times and times_up to interpolate the second time
F = mu.interp2d(times_up, times, complete_force_traj)
#Globals.robot.SetDOFValues(asarray(fake_seg["joint_states"]["position"][0]))
if args.visualize:
r2r = ros2rave.RosToRave(Globals.robot, asarray(seg_info["joint_states"]["name"]))
r2r.set_values(Globals.robot, asarray(seg_info["joint_states"]["position"][0]))
for i in range(0, traj.shape[0], 10):
handles = []
handles.append(Globals.env.plot3(new_xyz,5,np.array([(0,1,0,1) for x in new_xyz])))
handles.append(Globals.env.plot3(old_xyz,5,np.array([(1,0,0,1) for x in old_xyz])))
handles.append(Globals.env.drawlinestrip(old_ee_traj[:,:3,3], 2, (1,0,0,1)))
handles.append(Globals.env.drawlinestrip(new_ee_traj[:,:3,3], 2, (0,1,0,1)))
handles.append(Globals.env.drawlinestrip(new_ee_traj_rs[:,:3,3], 2, (0,0,1,1)))
# Setting robot arm to joint trajectory
r_vals = traj[i,:]
Globals.robot.SetDOFValues(r_vals, Globals.robot.GetManipulator('rightarm').GetArmIndices())
# Plotting forces from r_gripper_tool_frame
hmats = Globals.robot.GetLinkTransformations()
trans, rot = conv.hmat_to_trans_rot(hmats[-3])
f_start = np.array([0,0,0]) + trans
f_end = np.array(old_forces[i].T)[0]/100 + trans
handles.append(Globals.env.drawlinestrip(np.array([f_start, f_end]), 10, (1,0,0,1)))
redprint(i)
Globals.viewer.Idle()
# Controller code in joint space
traj_length = traj.shape[0]
pgains = np.asarray([2400.0, 1200.0, 1000.0, 700.0, 300.0, 300.0, 300.0])
dgains = np.asarray([18.0, 10.0, 6.0, 4.0, 6.0, 4.0, 4.0])
m = np.array([3.33, 1.16, 0.1, 0.25, 0.133, 0.0727, 0.0727]) # masses in joint space (feed forward)
vel_factor = 1e-3
#new costs
covariances, means = analyze_data.run_analyze_data(args)
CostsNew = []
counterCovs = 0
endTraj = False
covThiss = []
for covMat in covariances:
covThis = np.zeros((18,18))
covThis[0:6,0:6] = covMat[0:6,0:6]
covThis[12:18,12:18] = covMat[6:12, 6:12]
covThis[0:6, 12:18] = covMat[0:6, 6:12]
covThis[12:18, 0:6] = covMat[6:12, 0:6]
covThis[6:12, 6:12] = np.eye(6)*vel_factor
covThis = np.diag(np.diag(covThis))
covThiss.append(covThis)
#if len(covThiss) <= 69 and len(covThiss) >= 47:
# covThis[12:18,12:18] = np.diag([0.13, 0.06, 0.07, 0.005, 0.01, 0.004])
for j in range(args.eval.downsample_traj):
invCov = np.linalg.inv(covThis)
CostsNew.append(invCov)
counterCovs = counterCovs + 1
if counterCovs >= traj_length:
endTraj = True
break
if endTraj:
break
allCs = []
x = np.ones(6) * 1
v = np.ones(6) * 1e-3
a = np.ones(6) * 1e-6
Ct = np.diag(np.hstack((x, v, a))) # in end effector space
Ms = []
num_steps = i_end - i_start + 1
stamps = asarray(seg_info['stamps'][i_start:i_end+1])
arm = Globals.robot.GetManipulator("rightarm")
jacobians = []
for i in range(traj.shape[0]):
r_vals = traj[i,:]
Globals.robot.SetDOFValues(r_vals, Globals.robot.GetManipulator('rightarm').GetArmIndices())
jacobians.append(np.vstack((arm.CalculateJacobian(), arm.CalculateAngularVelocityJacobian())))
jacobians = asarray(jacobians)
#jacobiansx = asarray(seg_info["jacobians"][i_start:i_end+1])
for t in range(num_steps):
M = np.zeros((18, 21))
J = jacobians[t]
M[0:6,0:7] = J
M[6:12,7:14] = J
mdiag = np.diag(m) # Convert joint feedforward values into diagonal array
mdiag_inv = np.linalg.inv(mdiag)
M[12:18,14:21] = np.linalg.inv((J.dot(mdiag_inv).dot(np.transpose(J)))).dot(J).dot(mdiag_inv)
Ms.append(M)
for t in range(num_steps):
topad = np.zeros((21,21))
topad[0:7,0:7] = np.diag(pgains) * 0.1
topad[7:14,7:14] = np.diag(dgains) * 0.1
topad[14:21,14:21] = np.eye(7) * 0.1
#allCs.append(np.transpose(Ms[t]).dot(Ct).dot(Ms[t]) + topad)
allCs.append(np.transpose(Ms[t]).dot(CostsNew[t]).dot(Ms[t]) + topad)
Kps = []
Kvs = []
Ks = []
Qt = None
Vs = []
for t in range(num_steps-1, -1, -1):
if Qt is None:
Qt = allCs[t]
else:
Ft = np.zeros((14, 21))
for j in range(14):
Ft[j][j] = 1.0
deltat = abs(stamps[t+1] - stamps[t])
#print(deltat)
for j in range(7):
Ft[j][j+7] = deltat
for j in range(7):
Ft[j+7][j+14] = deltat/m[j]
for j in range(7):
Ft[j][j+14] = ((deltat)**2)/m[j]
Qt = allCs[t] + (np.transpose(Ft).dot(Vs[num_steps-2-t]).dot(Ft))
Qxx = Qt[0:14, 0:14]
Quu = Qt[14:21, 14:21]
Qxu = Qt[0:14, 14:21]
Qux = Qt[14:21, 0:14]
Quuinv = np.linalg.inv(Quu)
Vt = Qxx - Qxu.dot(Quuinv).dot(Qux)
Vt = 0.5*(Vt + np.transpose(Vt))
Kt = -1*(Quuinv.dot(Qux))
Ks.append(Kt)
Kps.append(Kt[:, 0:7])
Kvs.append(Kt[:, 7:14])
Vs.append(Vt)
Ks = Ks[::-1]
Kps = Kps[::-1]
Kvs = Kvs[::-1]
JKpJ = np.asarray(Kps)
JKvJ = np.asarray(Kvs)
total_length = num_steps + initial_traj_length
# Pad initial traj with PD gains
pgainsdiag = np.diag(np.asarray([-2400.0, -1200.0, -1000.0, -700.0, -300.0, -300.0, -300.0]))
dgainsdiag = np.diag(np.asarray([-18.0, -10.0, -6.0, -4.0, -6.0, -4.0, -4.0]))
addkp = np.asarray([pgainsdiag for i in range(initial_traj_length)])
addkv = np.asarray([dgainsdiag for i in range(initial_traj_length)])
JKpJ = np.concatenate((addkp, JKpJ), axis=0)
JKvJ = np.concatenate((addkv, JKvJ), axis=0)
Kps = []
Kvs = []
for i in range(num_steps + initial_traj_length):
Kps.append(np.zeros((6,6)))
Kvs.append(np.zeros((6,6)))
Kps = np.asarray(Kps)
Kvs = np.asarray(Kvs)
# Gains as JKpJ and JKvJ for testing
if args.pdgains:
JKpJ = np.asarray([pgainsdiag for i in range(total_length)])
JKvJ = np.asarray([dgainsdiag for i in range(total_length)])
qs = traj
IPython.embed()
Kps = np.resize(Kps, (1, 36 * total_length))[0]
Kvs = np.resize(Kvs, (1, 36 * total_length))[0]
JKvJ = np.resize(JKvJ, (1, 49*total_length))[0]
JKpJ = np.resize(JKpJ, (1, 49*total_length))[0]
# For use with new controller
qs = np.resize(qs, (1, qs.shape[0]*7))[0] #resize the data to 1x7n (n being the number of steps)
F = np.resize(F, (1, F.shape[0]*6))[0] #resize the data to 1x6n
# [traj, Kp, Kv, F, use_force, seconds]
data = np.zeros((1, len(qs) + len(JKpJ) + len(JKvJ) + len(F) + len(Kps) + len(Kvs) + 2))
data[0][0:len(qs)] = qs
data[0][len(qs):len(qs)+len(JKpJ)] = JKpJ
data[0][len(qs)+len(JKpJ):len(qs)+len(JKpJ)+len(JKvJ)] = JKvJ
data[0][len(qs)+len(JKpJ)+len(JKvJ):len(qs)+len(JKpJ)+len(JKvJ)+len(F)] = F
data[0][len(qs)+len(JKpJ)+len(JKvJ)+len(F):len(qs)+len(JKpJ)+len(JKvJ)+len(F)+len(Kps)] = Kps
data[0][len(qs)+len(JKpJ)+len(JKvJ)+len(F)+len(Kps):len(qs)+len(JKpJ)+len(JKvJ)+len(F)+len(Kps)+len(Kvs)] = Kvs
data[0][-2] = args.use_force
data[0][-1] = stamps[i_end] - stamps[i_start] + (initial_traj_length * 0.01)
# For use with old controller
"""
qs = np.array(np.matrix(traj).T) # 7 x n
qs = np.resize(qs, (1, qs.shape[1]*7))[0] #resize the data to 1x7n (n being the number of steps)
F = np.array(np.matrix(F).T) # 6 x n
F = F[0:3,:]
F = np.resize(F, (1, F.shape[1]*3))[0] #resize the data to 1x3n
data = np.zeros((1, len(qs) + len(F) + 2))
data[0][0:len(qs)] = qs
data[0][len(qs):len(qs)+len(F)] = F
data[0][-1] = args.use_force
data[0][-2] = stamps[i_end] - stamps[i_start]
"""
msg = Float64MultiArray()
msg.data = data[0].tolist()
pub = rospy.Publisher("/controller_data", Float64MultiArray)
redprint("Press enter to start trajectory")
raw_input()
time.sleep(1)
pub.publish(msg)
time.sleep(1)
else:
#if not success: break
if len(bodypart2traj) > 0:
exec_traj_maybesim(bodypart2traj)
def main():
demofile = h5py.File(args.h5file, 'r')
trajoptpy.SetInteractive(args.interactive)
if args.log:
LOG_DIR = osp.join(osp.expanduser("~/Data/do_task_logs"), datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
os.mkdir(LOG_DIR)
LOG_COUNT = 0
if args.execution:
rospy.init_node("exec_task",disable_signals=True)
Globals.pr2 = PR2.PR2()
Globals.env = Globals.pr2.env
Globals.robot = Globals.pr2.robot
else:
Globals.env = openravepy.Environment()
Globals.env.StopSimulation()
Globals.env.Load("robots/pr2-beta-static.zae")
Globals.robot = Globals.env.GetRobots()[0]
if not args.fake_data_segment:
grabber = cloudprocpy.CloudGrabber()
grabber.startRGBD()
Globals.viewer = trajoptpy.GetViewer(Globals.env)
#####################
while True:
redprint("Acquire point cloud")
if args.fake_data_segment:
fake_seg = demofile[args.fake_data_segment]
new_xyz = np.squeeze(fake_seg["cloud_xyz"])
hmat = openravepy.matrixFromAxisAngle(args.fake_data_transform[3:6])
hmat[:3,3] = args.fake_data_transform[0:3]
new_xyz = new_xyz.dot(hmat[:3,:3].T) + hmat[:3,3][None,:]
r2r = ros2rave.RosToRave(Globals.robot, asarray(fake_seg["joint_states"]["name"]))
r2r.set_values(Globals.robot, asarray(fake_seg["joint_states"]["position"][0]))
else:
Globals.pr2.head.set_pan_tilt(0,1.2)
Globals.pr2.rarm.goto_posture('side')
Globals.pr2.larm.goto_posture('side')
Globals.pr2.join_all()
time.sleep(.5)
Globals.pr2.update_rave()
rgb, depth = grabber.getRGBD()
T_w_k = berkeley_pr2.get_kinect_transform(Globals.robot)
new_xyz = cloud_proc_func(rgb, depth, T_w_k)
#grab_end(new_xyz)
if args.log:
LOG_COUNT += 1
import cv2
cv2.imwrite(osp.join(LOG_DIR,"rgb%i.png"%LOG_COUNT), rgb)
cv2.imwrite(osp.join(LOG_DIR,"depth%i.png"%LOG_COUNT), depth)
np.save(osp.join(LOG_DIR,"xyz%i.npy"%LOG_COUNT), new_xyz)
################################
redprint("Finding closest demonstration")
if args.select_manual:
seg_name = find_closest_manual(demofile, new_xyz)
else:
seg_name = find_closest_auto(demofile, new_xyz)
seg_info = demofile[seg_name]
redprint("closest demo: %s"%(seg_name))
if "done" in seg_name:
redprint("DONE!")
break
if args.log:
with open(osp.join(LOG_DIR,"neighbor%i.txt"%LOG_COUNT),"w") as fh: fh.write(seg_name)
################################
redprint("Generating end-effector trajectory")
handles = []
old_xyz = np.squeeze(seg_info["cloud_xyz"])
handles.append(Globals.env.plot3(old_xyz,5, (1,0,0)))
handles.append(Globals.env.plot3(new_xyz,5, (0,0,1)))
scaled_old_xyz, src_params = registration.unit_boxify(old_xyz)
scaled_new_xyz, targ_params = registration.unit_boxify(new_xyz)
f,_ = registration.tps_rpm_bij(scaled_old_xyz, scaled_new_xyz, plot_cb = tpsrpm_plot_cb,
plotting=5 if args.animation else 0,rot_reg=np.r_[1e-4,1e-4,1e-1], n_iter=50, reg_init=10, reg_final=.1)
f = registration.unscale_tps(f, src_params, targ_params)
handles.extend(plotting_openrave.draw_grid(Globals.env, f.transform_points, old_xyz.min(axis=0)-np.r_[0,0,.1], old_xyz.max(axis=0)+np.r_[0,0,.1], xres = .1, yres = .1, zres = .04))
link2eetraj = {}
for lr in 'lr':
link_name = "%s_gripper_tool_frame"%lr
old_ee_traj = asarray(seg_info[link_name]["hmat"])
new_ee_traj = f.transform_hmats(old_ee_traj)
link2eetraj[link_name] = new_ee_traj
handles.append(Globals.env.drawlinestrip(old_ee_traj[:,:3,3], 2, (1,0,0,1)))
handles.append(Globals.env.drawlinestrip(new_ee_traj[:,:3,3], 2, (0,1,0,1)))
miniseg_starts, miniseg_ends = split_trajectory_by_gripper(seg_info)
success = True
print colorize.colorize("mini segments:", "red"), miniseg_starts, miniseg_ends
for (i_miniseg, (i_start, i_end)) in enumerate(zip(miniseg_starts, miniseg_ends)):
if args.execution=="real": Globals.pr2.update_rave()
################################
redprint("Generating joint trajectory for segment %s, part %i"%(seg_name, i_miniseg))
# figure out how we're gonna resample stuff
lr2oldtraj = {}
for lr in 'lr':
manip_name = {"l":"leftarm", "r":"rightarm"}[lr]
old_joint_traj = asarray(seg_info[manip_name][i_start:i_end+1])
#print (old_joint_traj[1:] - old_joint_traj[:-1]).ptp(axis=0), i_start, i_end
if arm_moved(old_joint_traj):
lr2oldtraj[lr] = old_joint_traj
if len(lr2oldtraj) > 0:
old_total_traj = np.concatenate(lr2oldtraj.values(), 1)
JOINT_LENGTH_PER_STEP = .1
_, timesteps_rs = unif_resample(old_total_traj, JOINT_LENGTH_PER_STEP)
####
### Generate fullbody traj
bodypart2traj = {}
for (lr,old_joint_traj) in lr2oldtraj.items():
manip_name = {"l":"leftarm", "r":"rightarm"}[lr]
old_joint_traj_rs = mu.interp2d(timesteps_rs, np.arange(len(old_joint_traj)), old_joint_traj)
ee_link_name = "%s_gripper_tool_frame"%lr
new_ee_traj = link2eetraj[ee_link_name][i_start:i_end+1]
new_ee_traj_rs = resampling.interp_hmats(timesteps_rs, np.arange(len(new_ee_traj)), new_ee_traj)
if args.execution: Globals.pr2.update_rave()
new_joint_traj = planning.plan_follow_traj(Globals.robot, manip_name,
Globals.robot.GetLink(ee_link_name), new_ee_traj_rs,old_joint_traj_rs)
part_name = {"l":"larm", "r":"rarm"}[lr]
bodypart2traj[part_name] = new_joint_traj
################################
redprint("Executing joint trajectory for segment %s, part %i using arms '%s'"%(seg_name, i_miniseg, bodypart2traj.keys()))
for lr in 'lr':
success &= set_gripper_maybesim(lr, binarize_gripper(seg_info["%s_gripper_joint"%lr][i_start]))
# Doesn't actually check if grab occurred, unfortunately
if not success: break
if len(bodypart2traj) > 0:
success &= exec_traj_maybesim(bodypart2traj)
if not success: break
redprint("Segment %s result: %s"%(seg_name, success))
if args.fake_data_segment: break
def compute_dt_code(ctl_pts, plotting=False):
"""Takes rope control points (Nx3 array), closes the loop, and computes the Dowker-Thistlethwaite code for the knot.
The z-value for the points are used for determining over/undercrossings.
Follows procedure outlined here: http://katlas.math.toronto.edu/wiki/DT_(Dowker-Thistlethwaite)_Codes
"""
# First, close the loop by introducing extra points under the table and toward the robot (by subtracting z and x values)
# first_pt, last_pt = ctl_pts[0], ctl_pts[-1]
# flipped = False
# if first_pt[1] > last_pt[1]:
# first_pt, last_pt = last_pt, first_pt
# flipped = True
# min_z = ctl_pts[:,2].min()
# extra_first_pt, extra_last_pt = first_pt + [-.1, -.1, min_z-1], last_pt + [-.1, .1, min_z-1]
# if flipped:
# extra_pts = [extra_first_pt, extra_first_pt + [-1, 0, 0], extra_last_pt + [-1, 0, 0], extra_last_pt, last_pt]
# else:
# extra_pts = [extra_last_pt, extra_last_pt + [-1, 0, 0], extra_first_pt + [-1, 0, 0], extra_first_pt, first_pt]
# ctl_pts = np.append(ctl_pts, extra_pts, axis=0)
if plotting:
import trajoptpy, openravepy
env = openravepy.Environment()
viewer = trajoptpy.GetViewer(env)
handles = []
handles.append(env.plot3(ctl_pts, 5, [0, 0, 1]))
viewer.Idle()
# Upsampling loop: upsample until every segment has at most one crossing
need_upsample_ind = None
upsample_iters = 0
max_upsample_iters = 10
while True:
counter = 1
crossings = defaultdict(list)
# Walk along rope: for each segment, compute intersections with all other segments
for i in range(len(ctl_pts) - 1):
curr_seg = ctl_pts[i:i+2,:]
intersections, ts, us = intersect_segs(ctl_pts[:,:2], curr_seg[:,:2])
if len(intersections) == 0:
continue
if len(intersections) != 1:
LOG.debug('warning: more than one intersection for segment %d, now upsampling', i)
need_upsample_ind = i
break
# for each intersection, determine and record over/undercrossing
i_int = intersections[0]
if plotting:
handles.append(env.drawlinestrip(ctl_pts[i_int:i_int+2], 5, [1, 0, 0]))
int_point_rope = ctl_pts[i_int] + ts[i_int]*(ctl_pts[i_int+1] - ctl_pts[i_int])
int_point_curr_seg = curr_seg[0] + us[i_int]*(curr_seg[1] - curr_seg[0])
#assert np.allclose(int_point_rope[:2], int_point_curr_seg[:2])
above = int_point_curr_seg[2] > int_point_rope[2]
crossings[tuple(sorted((i, i_int)))].append(-counter if counter % 2 == 0 and above else counter)
counter += 1
if plotting: viewer.Idle()
# upsample if necessary
if need_upsample_ind is not None and upsample_iters < max_upsample_iters:
spacing = np.linspace(0, 1, len(ctl_pts))
new_spacing = np.insert(spacing, need_upsample_ind+1, (spacing[need_upsample_ind]+spacing[need_upsample_ind+1])/2.)
ctl_pts = math_utils.interp2d(new_spacing, spacing, ctl_pts)
upsample_iters += 1
need_upsample = None
continue
break
# Extract relevant part of crossing data to produce DT code
out = []
for pair in crossings.itervalues():
assert len(pair) == 2
odd = [p for p in pair if p % 2 == 1][0]
even = [p for p in pair if p % 2 == 0][0]
out.append((odd, even))
out.sort()
dt_code = [-o[1] for o in out]
return dt_code
def main():
demofile = h5py.File(args.h5file, 'r')
trajoptpy.SetInteractive(args.interactive)
if args.log:
LOG_DIR = osp.join(osp.expanduser("~/Data/do_task_logs"),
datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
os.mkdir(LOG_DIR)
LOG_COUNT = 0
if args.execution:
rospy.init_node("exec_task", disable_signals=True)
Globals.pr2 = PR2.PR2()
Globals.env = Globals.pr2.env
Globals.robot = Globals.pr2.robot
else:
Globals.env = openravepy.Environment()
Globals.env.StopSimulation()
Globals.env.Load("robots/pr2-beta-static.zae")
Globals.robot = Globals.env.GetRobots()[0]
if not args.fake_data_segment:
grabber = cloudprocpy.CloudGrabber()
grabber.startRGBD()
Globals.viewer = trajoptpy.GetViewer(Globals.env)
#####################
while True:
redprint("Acquire point cloud")
if args.fake_data_segment:
fake_seg = demofile[args.fake_data_segment]
new_xyz = np.squeeze(fake_seg["cloud_xyz"])
hmat = openravepy.matrixFromAxisAngle(
args.fake_data_transform[3:6])
hmat[:3, 3] = args.fake_data_transform[0:3]
new_xyz = new_xyz.dot(hmat[:3, :3].T) + hmat[:3, 3][None, :]
r2r = ros2rave.RosToRave(Globals.robot,
asarray(fake_seg["joint_states"]["name"]))
r2r.set_values(Globals.robot,
asarray(fake_seg["joint_states"]["position"][0]))
else:
Globals.pr2.head.set_pan_tilt(0, 1.2)
Globals.pr2.rarm.goto_posture('side')
Globals.pr2.larm.goto_posture('side')
Globals.pr2.join_all()
time.sleep(.5)
Globals.pr2.update_rave()
rgb, depth = grabber.getRGBD()
T_w_k = berkeley_pr2.get_kinect_transform(Globals.robot)
new_xyz = cloud_proc_func(rgb, depth, T_w_k)
#grab_end(new_xyz)
if args.log:
LOG_COUNT += 1
import cv2
cv2.imwrite(osp.join(LOG_DIR, "rgb%i.png" % LOG_COUNT), rgb)
cv2.imwrite(osp.join(LOG_DIR, "depth%i.png" % LOG_COUNT), depth)
np.save(osp.join(LOG_DIR, "xyz%i.npy" % LOG_COUNT), new_xyz)
################################
redprint("Finding closest demonstration")
if args.select_manual:
seg_name = find_closest_manual(demofile, new_xyz)
else:
seg_name = find_closest_auto(demofile, new_xyz)
seg_info = demofile[seg_name]
redprint("closest demo: %s" % (seg_name))
if "done" in seg_name:
redprint("DONE!")
break
if args.log:
with open(osp.join(LOG_DIR, "neighbor%i.txt" % LOG_COUNT),
"w") as fh:
fh.write(seg_name)
################################
redprint("Generating end-effector trajectory")
handles = []
old_xyz = np.squeeze(seg_info["cloud_xyz"])
handles.append(Globals.env.plot3(old_xyz, 5, (1, 0, 0)))
handles.append(Globals.env.plot3(new_xyz, 5, (0, 0, 1)))
scaled_old_xyz, src_params = registration.unit_boxify(old_xyz)
scaled_new_xyz, targ_params = registration.unit_boxify(new_xyz)
f, _ = registration.tps_rpm_bij(scaled_old_xyz,
scaled_new_xyz,
plot_cb=tpsrpm_plot_cb,
plotting=5 if args.animation else 0,
rot_reg=np.r_[1e-4, 1e-4, 1e-1],
n_iter=50,
reg_init=10,
reg_final=.1)
f = registration.unscale_tps(f, src_params, targ_params)
handles.extend(
plotting_openrave.draw_grid(Globals.env,
f.transform_points,
old_xyz.min(axis=0) - np.r_[0, 0, .1],
old_xyz.max(axis=0) + np.r_[0, 0, .1],
xres=.1,
yres=.1,
zres=.04))
link2eetraj = {}
for lr in 'lr':
link_name = "%s_gripper_tool_frame" % lr
old_ee_traj = asarray(seg_info[link_name]["hmat"])
new_ee_traj = f.transform_hmats(old_ee_traj)
link2eetraj[link_name] = new_ee_traj
handles.append(
Globals.env.drawlinestrip(old_ee_traj[:, :3, 3], 2,
(1, 0, 0, 1)))
handles.append(
Globals.env.drawlinestrip(new_ee_traj[:, :3, 3], 2,
(0, 1, 0, 1)))
miniseg_starts, miniseg_ends = split_trajectory_by_gripper(seg_info)
success = True
print colorize.colorize("mini segments:",
"red"), miniseg_starts, miniseg_ends
for (i_miniseg,
(i_start, i_end)) in enumerate(zip(miniseg_starts, miniseg_ends)):
if args.execution == "real": Globals.pr2.update_rave()
################################
redprint("Generating joint trajectory for segment %s, part %i" %
(seg_name, i_miniseg))
# figure out how we're gonna resample stuff
lr2oldtraj = {}
for lr in 'lr':
manip_name = {"l": "leftarm", "r": "rightarm"}[lr]
old_joint_traj = asarray(seg_info[manip_name][i_start:i_end +
1])
#print (old_joint_traj[1:] - old_joint_traj[:-1]).ptp(axis=0), i_start, i_end
if arm_moved(old_joint_traj):
lr2oldtraj[lr] = old_joint_traj
if len(lr2oldtraj) > 0:
old_total_traj = np.concatenate(lr2oldtraj.values(), 1)
JOINT_LENGTH_PER_STEP = .1
_, timesteps_rs = unif_resample(old_total_traj,
JOINT_LENGTH_PER_STEP)
####
### Generate fullbody traj
bodypart2traj = {}
for (lr, old_joint_traj) in lr2oldtraj.items():
manip_name = {"l": "leftarm", "r": "rightarm"}[lr]
old_joint_traj_rs = mu.interp2d(timesteps_rs,
np.arange(len(old_joint_traj)),
old_joint_traj)
ee_link_name = "%s_gripper_tool_frame" % lr
new_ee_traj = link2eetraj[ee_link_name][i_start:i_end + 1]
new_ee_traj_rs = resampling.interp_hmats(
timesteps_rs, np.arange(len(new_ee_traj)), new_ee_traj)
if args.execution: Globals.pr2.update_rave()
new_joint_traj = planning.plan_follow_traj(
Globals.robot, manip_name,
Globals.robot.GetLink(ee_link_name), new_ee_traj_rs,
old_joint_traj_rs)
part_name = {"l": "larm", "r": "rarm"}[lr]
bodypart2traj[part_name] = new_joint_traj
################################
redprint(
"Executing joint trajectory for segment %s, part %i using arms '%s'"
% (seg_name, i_miniseg, bodypart2traj.keys()))
for lr in 'lr':
success &= set_gripper_maybesim(
lr,
binarize_gripper(seg_info["%s_gripper_joint" %
lr][i_start]))
# Doesn't actually check if grab occurred, unfortunately
if not success: break
if len(bodypart2traj) > 0:
success &= exec_traj_maybesim(bodypart2traj)
if not success: break
redprint("Segment %s result: %s" % (seg_name, success))
if args.fake_data_segment: break
def compute_trans_traj(sim_env, new_xyz, seg_info, ignore_infeasibility=True, animate=False, interactive=False):
sim_util.reset_arms_to_side(sim_env)
redprint("Generating end-effector trajectory")
old_xyz = np.squeeze(seg_info["cloud_xyz"])
old_xyz = clouds.downsample(old_xyz, DS_SIZE)
new_xyz = clouds.downsample(new_xyz, DS_SIZE)
link_names = ["%s_gripper_tool_frame"%lr for lr in ('lr')]
hmat_list = [(lr, seg_info[ln]['hmat']) for lr, ln in zip('lr', link_names)]
if GlobalVars.gripper_weighting:
interest_pts = get_closing_pts(seg_info)
else:
interest_pts = None
lr2eetraj, _, old_xyz_warped = warp_hmats_tfm(old_xyz, new_xyz, hmat_list, interest_pts)
handles = []
if animate:
handles.append(sim_env.env.plot3(old_xyz,5, (1,0,0)))
handles.append(sim_env.env.plot3(new_xyz,5, (0,0,1)))
handles.append(sim_env.env.plot3(old_xyz_warped,5, (0,1,0)))
miniseg_starts, miniseg_ends, _ = sim_util.split_trajectory_by_gripper(seg_info, thresh = GRIPPER_ANGLE_THRESHOLD)
success = True
feasible = True
misgrasp = False
print colorize.colorize("mini segments:", "red"), miniseg_starts, miniseg_ends
full_trajs = []
prev_vals = {lr:None for lr in 'lr'}
for (i_miniseg, (i_start, i_end)) in enumerate(zip(miniseg_starts, miniseg_ends)):
################################
redprint("Generating joint trajectory for part %i"%(i_miniseg))
# figure out how we're gonna resample stuff
# Use inverse kinematics to get trajectory for initializing TrajOpt,
# since demonstrations library does not contain joint angle data for
# left and right arms
ee_hmats = {}
for lr in 'lr':
ee_link_name = "%s_gripper_tool_frame"%lr
# TODO: Change # of timesteps for resampling?
ee_hmats[ee_link_name] = resampling.interp_hmats(np.arange(i_end+1-i_start), np.arange(i_end+1-i_start), lr2eetraj[lr][i_start:i_end+1])
lr2oldtraj = get_old_joint_traj_ik(sim_env, ee_hmats, prev_vals, i_start, i_end)
#lr2oldtraj = {}
#for lr in 'lr':
# manip_name = {"l":"leftarm", "r":"rightarm"}[lr]
# old_joint_traj = asarray(seg_info[manip_name][i_start:i_end+1])
# #print (old_joint_traj[1:] - old_joint_traj[:-1]).ptp(axis=0), i_start, i_end
# if sim_util.arm_moved(old_joint_traj):
# lr2oldtraj[lr] = old_joint_traj
if len(lr2oldtraj) > 0:
old_total_traj = np.concatenate(lr2oldtraj.values(), 1)
JOINT_LENGTH_PER_STEP = .1
_, timesteps_rs = sim_util.unif_resample(old_total_traj, JOINT_LENGTH_PER_STEP)
####
### Generate fullbody traj
bodypart2traj = {}
for (lr,old_joint_traj) in lr2oldtraj.items():
manip_name = {"l":"leftarm", "r":"rightarm"}[lr]
old_joint_traj_rs = mu.interp2d(timesteps_rs, np.arange(len(old_joint_traj)), old_joint_traj)
ee_link_name = "%s_gripper_tool_frame"%lr
new_ee_traj = lr2eetraj[lr][i_start:i_end+1]
new_ee_traj_rs = resampling.interp_hmats(timesteps_rs, np.arange(len(new_ee_traj)), new_ee_traj)
print "planning trajectory following"
new_joint_traj, pose_errs = planning.plan_follow_traj(sim_env.robot, manip_name,
sim_env.robot.GetLink(ee_link_name), new_ee_traj_rs,old_joint_traj_rs)
prev_vals[lr] = new_joint_traj[-1]
part_name = {"l":"larm", "r":"rarm"}[lr]
bodypart2traj[part_name] = new_joint_traj
################################
redprint("Executing joint trajectory for part %i using arms '%s'"%(i_miniseg, bodypart2traj.keys()))
full_traj = sim_util.getFullTraj(sim_env, bodypart2traj)
full_trajs.append(full_traj)
for lr in 'lr':
gripper_open = sim_util.binarize_gripper(seg_info["%s_gripper_joint"%lr][i_start], GRIPPER_ANGLE_THRESHOLD)
prev_gripper_open = sim_util.binarize_gripper(seg_info["%s_gripper_joint"%lr][i_start-1], GRIPPER_ANGLE_THRESHOLD) if i_start != 0 else False
if not sim_util.set_gripper_maybesim(sim_env, lr, gripper_open, prev_gripper_open):
redprint("Grab %s failed" % lr)
misgrasp = True
success = False
if not success: break
if len(full_traj[0]) > 0:
if not eval_util.traj_is_safe(sim_env, full_traj, COLLISION_DIST_THRESHOLD):
redprint("Trajectory not feasible")
feasible = False
if feasible or ignore_infeasibility:
success &= sim_util.sim_full_traj_maybesim(sim_env, full_traj, animate=animate, interactive=interactive)
else:
success = False
if not success: break
sim_env.sim.settle(animate=animate)
sim_env.sim.release_rope('l')
sim_env.sim.release_rope('r')
sim_util.reset_arms_to_side(sim_env)
if animate:
sim_env.viewer.Step()
return success, feasible, misgrasp, full_trajs
def main():
import IPython
demofile = h5py.File(args.h5file, 'r')
trajoptpy.SetInteractive(args.interactive)
if args.log:
LOG_DIR = osp.join(osp.expanduser("~/Data/do_task_logs"), datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
os.mkdir(LOG_DIR)
LOG_COUNT = 0
if args.execution:
rospy.init_node("exec_task",disable_signals=True)
Globals.pr2 = PR2.PR2()
Globals.env = Globals.pr2.env
Globals.robot = Globals.pr2.robot
else:
Globals.env = openravepy.Environment()
Globals.env.StopSimulation()
Globals.env.Load("robots/pr2-beta-static.zae")
Globals.robot = Globals.env.GetRobots()[0]
if not args.fake_data_segment:
grabber = cloudprocpy.CloudGrabber()
grabber.startRGBD()
Globals.viewer = trajoptpy.GetViewer(Globals.env)
#####################
while True:
redprint("Acquire point cloud")
if args.fake_data_segment:
fake_seg = demofile[args.fake_data_segment]
new_xyz = np.squeeze(fake_seg["cloud_xyz"])
hmat = openravepy.matrixFromAxisAngle(args.fake_data_transform[3:6])
hmat[:3,3] = args.fake_data_transform[0:3]
new_xyz = new_xyz.dot(hmat[:3,:3].T) + hmat[:3,3][None,:]
r2r = ros2rave.RosToRave(Globals.robot, asarray(fake_seg["joint_states"]["name"]))
r2r.set_values(Globals.robot, asarray(fake_seg["joint_states"]["position"][0]))
#Globals.pr2.head.set_pan_tilt(0,1.2)
#Globals.pr2.rarm.goto_posture('side')
#Globals.pr2.larm.goto_posture('side')
#Globals.pr2.join_all()
#time.sleep(2)
else:
#Globals.pr2.head.set_pan_tilt(0,1.2)
#Globals.pr2.rarm.goto_posture('side')
#Globals.pr2.larm.goto_posture('side')
#Globals.pr2.join_all()
#time.sleep(2)
Globals.pr2.update_rave()
rgb, depth = grabber.getRGBD()
T_w_k = berkeley_pr2.get_kinect_transform(Globals.robot)
new_xyz = cloud_proc_func(rgb, depth, T_w_k)
print "got new xyz"
redprint(new_xyz)
#grab_end(new_xyz)
if args.log:
LOG_COUNT += 1
import cv2
cv2.imwrite(osp.join(LOG_DIR,"rgb%i.png"%LOG_COUNT), rgb)
cv2.imwrite(osp.join(LOG_DIR,"depth%i.png"%LOG_COUNT), depth)
np.save(osp.join(LOG_DIR,"xyz%i.npy"%LOG_COUNT), new_xyz)
################################
redprint("Finding closest demonstration")
if args.select_manual:
seg_name = find_closest_manual(demofile, new_xyz)
else:
seg_name = find_closest_auto(demofile, new_xyz)
seg_info = demofile[seg_name]
redprint("closest demo: %s"%(seg_name))
if "done" in seg_name:
redprint("DONE!")
break
if args.log:
with open(osp.join(LOG_DIR,"neighbor%i.txt"%LOG_COUNT),"w") as fh: fh.write(seg_name)
################################
### Old end effector forces at r_gripper_tool_frame (including torques)
old_forces = seg_info['end_effector_forces'][:,0:3,:]
old_torques = seg_info['end_effector_forces'][:,3:6,:]
redprint("Generating end-effector trajectory")
handles = []
old_xyz = np.squeeze(seg_info["cloud_xyz"])
scaled_old_xyz, src_params = registration.unit_boxify(old_xyz)
scaled_new_xyz, targ_params = registration.unit_boxify(new_xyz)
f,_ = registration.tps_rpm_bij(scaled_old_xyz, scaled_new_xyz, plot_cb = tpsrpm_plot_cb,
plotting=5 if args.animation else 0,rot_reg=np.r_[1e-4,1e-4,1e-1], n_iter=50, reg_init=10, reg_final=.1)
f = registration.unscale_tps(f, src_params, targ_params)
old_xyz_transformed = f.transform_points(old_xyz)
#handles.append(Globals.env.plot3(old_xyz_transformed,5, np.array([(0,0,1,1) for i in old_xyz_transformed])))
handles.extend(plotting_openrave.draw_grid(Globals.env, f.transform_points, old_xyz.min(axis=0)-np.r_[0,0,.1], old_xyz.max(axis=0)+np.r_[0,0,.1], xres = .1, yres = .1, zres = .04))
link2eetraj = {}
for lr in 'r':
link_name = "%s_gripper_tool_frame"%lr
old_ee_traj = asarray(seg_info[link_name]["hmat"])
new_ee_traj = f.transform_hmats(old_ee_traj)
link2eetraj[link_name] = new_ee_traj
#print old_ee_traj
old_ee_pos = old_ee_traj[:,0:3,3]
#print old_ee_pos
# End effector forces as oppossed to end effector trajectories
dfdxs = f.compute_jacobian(old_ee_pos)
old_eefs = []
new_eefs = []
for i in xrange(len(old_forces)):
dfdx = np.matrix(dfdxs[i])
old_force = np.matrix(old_forces[i])
old_torque = np.matrix(old_torques[i])
new_force = (dfdx * old_force)
new_torque = (dfdx * old_torque)
old_eefs.append(np.vstack((old_force, old_torque)))
new_eefs.append(np.vstack((new_force, new_torque)))
old_eefs = np.asarray(old_eefs)[:,:,0]
new_eefs = np.asarray(new_eefs)[:,:,0]
force_data = {}
force_data['old_eefs'] = old_eefs
force_data['new_eefs'] = new_eefs
force_file = open("trial.pickle", 'wa')
pickle.dump(force_data, force_file)
force_file.close()
new_ee_traj_x = new_ee_traj
miniseg_starts, miniseg_ends = split_trajectory_by_gripper(seg_info)
success = True
print colorize.colorize("mini segments:", "red"), miniseg_starts, miniseg_ends
for (i_miniseg, (i_start, i_end)) in enumerate(zip(miniseg_starts, miniseg_ends)):
if args.execution=="real": Globals.pr2.update_rave()
################################
redprint("Generating joint trajectory for segment %s, part %i"%(seg_name, i_miniseg))
# figure out how we're gonna resample stuff
lr2oldtraj = {}
for lr in 'r':
manip_name = {"l":"leftarm", "r":"rightarm"}[lr]
old_joint_traj = asarray(seg_info[manip_name][i_start:i_end+1])
#print (old_joint_traj[1:] - old_joint_traj[:-1]).ptp(axis=0), i_start, i_end
#if arm_moved(old_joint_traj): NOT SURE WHY BUT THIS IS RETURNING FALSE
lr2oldtraj[lr] = old_joint_traj
if args.visualize:
r2r = ros2rave.RosToRave(Globals.robot, asarray(seg_info["joint_states"]["name"]))
r2r.set_values(Globals.robot, asarray(seg_info["joint_states"]["position"][0]))
for i in range(0, lr2oldtraj['r'].shape[0], 10):
handles = []
handles.append(Globals.env.plot3(new_xyz,5,np.array([(0,1,0,1) for x in new_xyz])))
handles.append(Globals.env.plot3(old_xyz,5,np.array([(1,0,0,1) for x in old_xyz])))
handles.append(Globals.env.drawlinestrip(old_ee_traj[:,:3,3], 2, (1,0,0,1)))
# Setting robot arm to joint trajectory
r_vals = lr2oldtraj['r'][i,:]
Globals.robot.SetDOFValues(r_vals, Globals.robot.GetManipulator('rightarm').GetArmIndices())
# Plotting forces from r_gripper_tool_frame
hmats = Globals.robot.GetLinkTransformations()
trans, rot = conv.hmat_to_trans_rot(hmats[-3])
f_start = np.array([0,0,0]) + trans
#IPython.embed()
f_end = np.array(old_forces[i].T)[0]/100 + trans
handles.append(Globals.env.drawlinestrip(np.array([f_start, f_end]), 10, (1,0,0,1)))
redprint(i)
Globals.viewer.Step()
if len(lr2oldtraj) > 0:
old_total_traj = np.concatenate(lr2oldtraj.values(), 1)
JOINT_LENGTH_PER_STEP = .1
# FIRST RESAMPLING HAPPENS HERE: JOINT_LENGTH
_, timesteps_rs = unif_resample(old_total_traj, JOINT_LENGTH_PER_STEP) # Timesteps only, can use to inter eefs for first time
####
new_eefs_segment = asarray(new_eefs[i_start:i_end+1,:]) # Extract miniseg, and re-sample
if args.no_ds:
new_eefs_segment_rs = new_eefs_segment
else:
new_eefs_segment_rs = mu.interp2d(timesteps_rs, np.arange(len(new_eefs_segment)), new_eefs_segment)
### Generate fullbody traj
bodypart2traj = {}
for (lr,old_joint_traj) in lr2oldtraj.items():
manip_name = {"l":"leftarm", "r":"rightarm"}[lr]
ee_link_name = "%s_gripper_tool_frame"%lr
new_ee_traj = link2eetraj[ee_link_name][i_start:i_end+1]
if args.no_ds:
old_joint_traj_rs = old_joint_traj
new_ee_traj_rs = new_ee_traj
ds_inds = np.arange(0, len(new_ee_traj_rs), args.trajopt_ds)
new_ee_traj_rs = new_ee_traj_rs[ds_inds]
old_joint_traj_rs = old_joint_traj_rs[ds_inds]
new_joint_traj = planning.plan_follow_traj(Globals.robot, manip_name,
Globals.robot.GetLink(ee_link_name), new_ee_traj_rs,old_joint_traj_rs)
new_joint_traj = mu.interp2d(np.arange(len(old_joint_traj)), np.arange(0, len(new_joint_traj) * args.trajopt_ds, args.trajopt_ds), new_joint_traj)
else:
old_joint_traj_rs = mu.interp2d(timesteps_rs, np.arange(len(old_joint_traj)), old_joint_traj)
new_ee_traj_rs = resampling.interp_hmats(timesteps_rs, np.arange(len(new_ee_traj)), new_ee_traj)
new_joint_traj = planning.plan_follow_traj(Globals.robot, manip_name,
Globals.robot.GetLink(ee_link_name), new_ee_traj_rs,old_joint_traj_rs)
if args.execution: Globals.pr2.update_rave()
part_name = {"l":"larm", "r":"rarm"}[lr]
bodypart2traj[part_name] = new_joint_traj
redprint("Joint trajectory has length: " + str(len(bodypart2traj[part_name])))
redprint("Executing joint trajectory for segment %s, part %i using arms '%s'"%(seg_name, i_miniseg, bodypart2traj.keys()))
redprint("Press enter to set gripper")
raw_input()
#for lr in 'r':
# set_gripper_maybesim(lr, binarize_gripper(seg_info["%s_gripper_joint"%lr][i_start]))
if args.pid:
if not args.fake_data_segment: # If running on PR2, add initial state as waypoint and rough interpolate
# Add initial position
(arm_position, arm_velocity, arm_effort) = robot_states.call_return_joint_states(robot_states.arm_joint_names)
traj_length = bodypart2traj['rarm'].shape[0]
complete_joint_traj = np.zeros((traj_length+1, 7)) # To include initial state as a way point
complete_joint_traj[1:,:] = bodypart2traj['rarm']
complete_joint_traj[0,:] = arm_position
bodypart2traj['rarm'] = complete_joint_traj
# Add initial eff
J = np.matrix(np.resize(np.array(robot_states.call_return_jacobian()), (6, 7))) # Jacobian
eff = robot_states.compute_end_effector_force(J, arm_effort).T
eff = np.array(eff)[0]
init_force = eff[:3]
complete_force_traj = np.zeros((traj_length+1, 3))
complete_force_traj[1:,:] = new_eefs_segment_rs
complete_force_traj[0,:] = init_force
else:
complete_force_traj = new_eefs_segment_rs
# SECOND RESAMPLING HAPPENS HERE: JOINT VELOCITIES
if args.no_ds:
traj = bodypart2traj['rarm']
stamps = asarray(seg_info['stamps'])
times = np.array([stamps[i_end] - stamps[i_start]])
F = complete_force_traj
else:
times, times_up, traj = pr2_trajectories.return_timed_traj(Globals.pr2, bodypart2traj) # use times and times_up to interpolate the second time
F = mu.interp2d(times_up, times, complete_force_traj)
#Globals.robot.SetDOFValues(asarray(fake_seg["joint_states"]["position"][0]))
if args.visualize:
r2r = ros2rave.RosToRave(Globals.robot, asarray(seg_info["joint_states"]["name"]))
r2r.set_values(Globals.robot, asarray(seg_info["joint_states"]["position"][0]))
for i in range(0, traj.shape[0], 10):
handles = []
handles.append(Globals.env.plot3(new_xyz,5,np.array([(0,1,0,1) for x in new_xyz])))
handles.append(Globals.env.plot3(old_xyz,5,np.array([(1,0,0,1) for x in old_xyz])))
handles.append(Globals.env.drawlinestrip(old_ee_traj[:,:3,3], 2, (1,0,0,1)))
handles.append(Globals.env.drawlinestrip(new_ee_traj[:,:3,3], 2, (0,1,0,1)))
handles.append(Globals.env.drawlinestrip(new_ee_traj_rs[:,:3,3], 2, (0,0,1,1)))
# Setting robot arm to joint trajectory
r_vals = traj[i,:]
Globals.robot.SetDOFValues(r_vals, Globals.robot.GetManipulator('rightarm').GetArmIndices())
# Plotting forces from r_gripper_tool_frame
hmats = Globals.robot.GetLinkTransformations()
trans, rot = conv.hmat_to_trans_rot(hmats[-3])
f_start = np.array([0,0,0]) + trans
f_end = np.array(old_forces[i].T)[0]/100 + trans
handles.append(Globals.env.drawlinestrip(np.array([f_start, f_end]), 10, (1,0,0,1)))
redprint(i)
Globals.viewer.Step()
qs = np.array(np.matrix(traj).T) # 7 x n
qs = np.resize(traj, (1, qs.shape[1]*7))[0] #resize the data to 1x7n (n being the number of steps)
F = np.array(np.matrix(F).T) # 6 x n
F = np.resize(F, (1, F.shape[1]*6))[0] #resize the data to 1x3n
# Controller code
allCs = []
x = np.ones(6) * 1
v = np.ones(6) * 1e-3
a = np.ones(6) * 1e-6
Ct = np.diag(np.hstack((x, v, a)))
num_steps = i_end - i_start + 1
#need to figure out the stamps correctly
stamps = asarray(seg_info['stamps'][i_start:i_end+1])
for t in range(num_steps-1, -1, -1):
allCs.append(Ct)
m = np.ones(6)
Kps = []
Kvs = []
Ks = []
Qt = None
Vs = []
for t in range(num_steps-1, -1, -1):
if Qt is None:
Qt = allCs[t]
else:
Ft = np.zeros((12, 18))
for j in range(12):
Ft[j][j] = 1.0
deltat = abs(stamps[t+1] - stamps[t])
#print(deltat)
for j in range(6):
Ft[j][j+6] = deltat
for j in range(6):
Ft[j+6][j+12] = deltat/m[j]
for j in range(6):
Ft[j][j+12] = ((deltat)**2)/m[j]
Qt = allCs[t] + (np.transpose(Ft).dot(Vs[num_steps-2-t]).dot(Ft))
Qxx = Qt[0:12, 0:12]
Quu = Qt[12:18, 12:18]
Qxu = Qt[0:12, 12:18]
Qux = Qt[12:18, 0:12]
Quuinv = np.linalg.inv(Quu)
Vt = Qxx - Qxu.dot(Quuinv).dot(Qux)
Kt = -1*(Quuinv.dot(Qux))
Ks.append(Kt)
Kps.append(Kt[:, 0:6])
Kvs.append(Kt[:, 6:12])
Vs.append(Vt)
Ks = Ks[::-1]
Kps = Kps[::-1]
Kvs = Kvs[::-1]
JKpJ = []
JKvJ = []
toAddJkpJ = np.diag(np.asarray([-2400.0, -1200.0, -1000.0, -700.0, -300.0, -300.0, -300.0]))
toAddJkvJ = np.diag(np.asarray([-18.0, -10.0, -6.0, -4.0, -6.0, -4.0, -4.0]))
JacobianOlds = asarray(seg_info["jacobians"][i_start:i_end+1])
for i in range(i_end- i_start + 1):
J = JacobianOlds[i]
psuedoJ = np.linalg.inv(np.transpose(J).dot(J)).dot(np.transpose(J))
#JKpJ.append(np.transpose(J).dot(Kps[i]).dot(J) + toAddJkpJ*0.001)
#JKvJ.append(np.transpose(J).dot(Kvs[i]).dot(J) + toAddJkvJ*0.001)
JKpJ.append(psuedoJ.dot(Kps[i]).dot(J) + toAddJkpJ*0.001)
JKvJ.append(psuedoJ.dot(Kvs[i]).dot(J) + toAddJkvJ*0.001)
if args.useJK:
Kps = []
Kvs = []
for i in range(i_end- i_start + 1):
Kps.append(np.zeros((6,6)))
Kvs.append(np.zeros((6,6)))
else:
JKpJ = []
JKvJ = []
for i in range(i_end- i_start + 1):
JKpJ.append(np.zeros((7,7)))
JKvJ.append(np.zeros((7,7)))
Kps = np.asarray(Kps)
Kvs = np.asarray(Kvs)
JKpJ = np.asarray(JKpJ)
JKvJ = np.asarray(JKvJ)
IPython.embed()
# # Temp Kps and Kvs for testing
"""
toAddJkpJ = np.diag(np.asarray([-2400.0, -1200.0, -1000.0, -700.0, -300.0, -300.0, -300.0]))
toAddJkvJ = np.diag(np.asarray([-18.0, -10.0, -6.0, -4.0, -6.0, -4.0, -4.0]))
length = complete_force_traj.shape[0]
JKpJ = np.asarray([toAddJkpJ for i in range(length)])
JKpJ = np.resize(JKpJ, (1, 49*length))[0]
JKvJ = np.asarray([toAddJkvJ for i in range(length)])
JKvJ = np.resize(JKvJ, (1, 49*length))[0]
"""
# [traj, Kp, Kv, F, use_force, seconds]
Kps = np.resize(Kps, (1, 36 * num_steps))[0]
Kvs = np.resize(Kvs, (1, 36 * num_steps))[0]
JKpJ = np.resize(JKpJ, (1, 49 * num_steps))[0]
JKvJ = np.resize(JKvJ, (1, 49 * num_steps))[0]
data = np.zeros((1, len(qs) + len(JKpJ) + len(JKvJ) + len(F) + len(Kps) + len(Kvs) + 2))
data[0][0:len(qs)] = qs
data[0][len(qs):len(qs)+len(JKpJ)] = JKpJ
data[0][len(qs)+len(JKpJ):len(qs)+len(JKpJ)+len(JKvJ)] = JKvJ
data[0][len(qs)+len(JKpJ)+len(JKvJ):len(qs)+len(JKpJ)+len(JKvJ)+len(F)] = F
data[0][len(qs)+len(JKpJ)+len(JKvJ)+len(F):len(qs)+len(JKpJ)+len(JKvJ)+len(F)+len(Kps)] = Kps
data[0][len(qs)+len(JKpJ)+len(JKvJ)+len(F)+len(Kps):len(qs)+len(JKpJ)+len(JKvJ)+len(F)+len(Kps)+len(Kvs)] = Kvs
data[0][-2] = args.use_force
data[0][-1] = stamps[i_end] - stamps[i_start]
msg = Float64MultiArray()
msg.data = data[0].tolist()
pub = rospy.Publisher("/controller_data", Float64MultiArray)
redprint("Press enter to start trajectory")
raw_input()
time.sleep(1)
pub.publish(msg)
time.sleep(1)
else:
#if not success: break
if len(bodypart2traj) > 0:
exec_traj_maybesim(bodypart2traj)
def interp_quats(newtimes, oldtimes, oldquats):
"should actually do slerp"
quats_unnormed = mu.interp2d(newtimes, oldtimes, oldquats)
return mu.normr(quats_unnormed)
def interp_quats(newtimes, oldtimes, oldquats):
"should actually do slerp"
quats_unnormed = mu.interp2d(newtimes, oldtimes, oldquats)
return mu.normr(quats_unnormed)
def loop_body(demofile, choose_segment, knot, animate, task_params, curr_step=None):
"""Do the body of the main task execution loop (ie. do a segment).
Arguments:
curr_step is 0 indexed
choose_segment is a function that returns the key in the demofile to the segment
knot is the knot the rope is checked against
new_xyz is the new pointcloud
task_params is used for the only_original_segments argument
return None or {'found_knot': found_knot, 'segment': segment, 'link2eetraj': link2eetraj, 'new_xyz': new_xyz}
"""
#TODO -- Return the new trajectory and state info to be used for bootstrapping (knot_success, new_xyz, link2eetraj,
#TODO segment)
#TODO -- End condition
#TODO -- max_segments logic
redprint("Acquire point cloud")
move_sim_arms_to_side()
#TODO -- Possibly refactor this section to be before the loop
new_xyz = Globals.sim.observe_cloud()
new_xyz_upsampled = Globals.sim.observe_cloud(upsample=120)
print "loop_body only_original_segments", task_params.only_original_segments
segment = choose_segment(demofile, new_xyz, task_params.only_original_segments)
if segment is None:
return None
seg_info = demofile[segment]
handles = []
old_xyz = np.squeeze(seg_info["cloud_xyz"])
handles.append(Globals.env.plot3(new_xyz, 5, (0, 0, 1)))
#TODO: test that old_xyz is now bigger if from a derived segment
#old_xyz = clouds.downsample(old_xyz, DS_SIZE)
if not "derived" in seg_info.keys():
old_xyz = downsample(old_xyz, DS_SIZE)
print "derived, so downsamping"
#new_xyz = clouds.downsample(new_xyz, DS_SIZE)
#new_xyz = downsample_if_big(new_xyz, DS_SIZE)
handles.append(Globals.env.plot3(old_xyz, 5, (1, 0, 0)))
print "new_xyz cloud size", new_xyz.shape
print "old_xyz cloud size", old_xyz.shape
scaled_old_xyz, src_params = registration.unit_boxify(old_xyz)
scaled_new_xyz, targ_params = registration.unit_boxify(new_xyz)
#TODO: get rid of g
f, g = registration.tps_rpm_bij(scaled_old_xyz, scaled_new_xyz, plot_cb=tpsrpm_plot_cb,
plotting=5 if animate else 0, rot_reg=np.r_[1e-4, 1e-4, 1e-1], n_iter=50,
reg_init=10, reg_final=.01, old_xyz=old_xyz, new_xyz=new_xyz)
f = registration.unscale_tps(f, src_params, targ_params)
g = registration.unscale_tps(g, src_params, targ_params)
#Globals.exec_log(curr_step, "gen_traj.f", f)
#handles.extend(plotting_openrave.draw_grid(Globals.env, f.transform_points, old_xyz.min(axis=0) - np.r_[0, 0, .1],
# old_xyz.max(axis=0) + np.r_[0, 0, .1], xres=.1, yres=.1, zres=.04))
handles.extend(plotting_openrave.draw_grid(Globals.env, f.transform_points, old_xyz.min(axis=0) - np.r_[0, 0, .1],
old_xyz.max(axis=0) + np.r_[0, 0, .02], xres=.01, yres=.01, zres=.04))
#handles.extend(plotting_openrave.draw_grid(Globals.env, g.transform_points, old_xyz.min(axis=0) - np.r_[0, 0, .1],
# old_xyz.max(axis=0) + np.r_[0, 0, .02], xres=.01, yres=.01, zres=.04))
link2eetraj = {}
#link2eetraj is a hash of gripper fram to new trajectory
#Transform the gripper trajectory here
for lr in 'lr':
link_name = "%s_gripper_tool_frame" % lr
#old_ee_traj is the old gripper trajectory
old_ee_traj = asarray(seg_info[link_name]["hmat"])
#new_ee_traj is the transformed gripper trajectory
new_ee_traj = f.transform_hmats(old_ee_traj)
link2eetraj[link_name] = new_ee_traj
#Draw the old and new gripper trajectories as lines
handles.append(Globals.env.drawlinestrip(old_ee_traj[:, :3, 3], 2, (1, 0, 0, 1)))
handles.append(Globals.env.drawlinestrip(new_ee_traj[:, :3, 3], 2, (0, 1, 0, 1)))
#Globals.exec_log(curr_step, "gen_traj.link2eetraj", link2eetraj)
miniseg_starts, miniseg_ends = split_trajectory_by_gripper(seg_info)
success = True
#print colorize.colorize("mini segments:", "red"), miniseg_starts, miniseg_ends
#TODO: modify for no body
for (i_miniseg, (i_start, i_end)) in enumerate(zip(miniseg_starts, miniseg_ends)):
################################
redprint("Generating joint trajectory for segment %s, part %i" % (segment, i_miniseg))
# figure out how we're gonna resample stuff
lr2oldtraj = {}
for lr in 'lr':
manip_name = {"l": "leftarm", "r": "rightarm"}[lr]
#old_joint_traj is the old trajectory inside the minisegment
old_joint_traj = asarray(seg_info[manip_name][i_start: i_end + 1])
#print (old_joint_traj[1:] - old_joint_traj[:-1]).ptp(axis=0), i_start, i_end
if arm_moved(old_joint_traj):
lr2oldtraj[lr] = old_joint_traj
if len(lr2oldtraj) > 0:
old_total_traj = np.concatenate(lr2oldtraj.values(), 1)
JOINT_LENGTH_PER_STEP = .1
_, timesteps_rs = unif_resample(old_total_traj, JOINT_LENGTH_PER_STEP)
####
### Generate fullbody traj
bodypart2traj = {}
for (lr, old_joint_traj) in lr2oldtraj.items():
manip_name = {"l": "leftarm", "r": "rightarm"}[lr]
old_joint_traj_rs = mu.interp2d(timesteps_rs, np.arange(len(old_joint_traj)), old_joint_traj)
ee_link_name = "%s_gripper_tool_frame" % lr
new_ee_traj = link2eetraj[ee_link_name][i_start: i_end + 1]
new_ee_traj_rs = resampling.interp_hmats(timesteps_rs, np.arange(len(new_ee_traj)), new_ee_traj)
#Call the planner (eg. trajopt)
with dhm_u.suppress_stdout():
new_joint_traj = planning.plan_follow_traj(Globals.robot, manip_name,
Globals.robot.GetLink(ee_link_name), new_ee_traj_rs,
old_joint_traj_rs)
part_name = {"l": "larm", "r": "rarm"}[lr]
bodypart2traj[part_name] = new_joint_traj
### Execute the gripper ###
redprint("Executing joint trajectory for segment %s, part %i using arms '%s'" % (
segment, i_miniseg, bodypart2traj.keys()))
for lr in 'lr':
gripper_open = binarize_gripper(seg_info["%s_gripper_joint" % lr][i_start])
prev_gripper_open = binarize_gripper(
seg_info["%s_gripper_joint" % lr][i_start - 1]) if i_start != 0 else False
if not set_gripper_sim(lr, gripper_open, prev_gripper_open, animate):
redprint("Grab %s failed" % lr)
success = False
if not success:
break
# Execute the robot trajectory
if len(bodypart2traj) > 0:
success &= exec_traj_sim(bodypart2traj, animate)
#Globals.exec_log(curr_step, "execute_traj.miniseg_%d.sim_rope_nodes_after_traj" % i_miniseg, Globals.sim.rope.GetNodes())
if not success:
break
Globals.sim.settle(animate=animate)
if Globals.exec_log:
Globals.exec_log(curr_step, "execute_traj.sim_rope_nodes_after_full_traj", Globals.sim.rope.GetNodes())
from rapprentice import knot_identification
knot_name = knot_identification.identify_knot(Globals.sim.rope.GetControlPoints())
found_knot = False
if knot_name is not None:
if knot_name == knot or knot == "any":
redprint("Identified knot: %s. Success!" % knot_name)
#Globals.exec_log(curr_step, "result", True, description="identified knot %s" % knot_name)
found_knot = True
else:
redprint("Identified knot: %s, but expected %s. Continuing." % (knot_name, knot))
else:
redprint("Not a knot. Continuing.")
redprint("Segment %s result: %s" % (segment, success))
return {'found_knot': found_knot, 'segment': segment, 'link2eetraj': link2eetraj, 'new_xyz': new_xyz_upsampled}