def replay_on_holdout(args, sim_env):
holdoutfile = h5py.File(args.holdoutfile, 'r')
tasks = eval_util.get_specified_tasks(args.tasks, args.taskfile, args.i_start, args.i_end)
loadresultfile = h5py.File(args.loadresultfile, 'r')
loadresult_items = eval_util.get_holdout_items(loadresultfile, tasks)
num_successes = 0
num_total = 0
for i_task, demo_id_rope_nodes in loadresult_items:
print "task %s" % i_task
sim_util.reset_arms_to_side(sim_env)
redprint("Replace rope")
rope_nodes, rope_params, _, _ = eval_util.load_task_results_init(args.loadresultfile, i_task)
# uncomment if the results file don't have the right rope nodes
#rope_nodes = demo_id_rope_nodes["rope_nodes"][:]
if args.replay_rope_params:
rope_params = args.replay_rope_params
# don't call replace_rope and sim.settle() directly. use time machine interface for deterministic results!
time_machine = sim_util.RopeSimTimeMachine(rope_nodes, sim_env)
if args.animation:
sim_env.viewer.Step()
eval_util.save_task_results_init(args.resultfile, sim_env, i_task, rope_nodes, rope_params)
for i_step in range(len(loadresultfile[i_task]) - (1 if 'init' in loadresultfile[i_task] else 0)):
print "task %s step %i" % (i_task, i_step)
sim_util.reset_arms_to_side(sim_env)
redprint("Observe point cloud")
new_xyz = sim_env.sim.observe_cloud()
eval_stats = eval_util.EvalStats()
best_action, full_trajs, q_values, trans, rots = eval_util.load_task_results_step(args.loadresultfile, sim_env, i_task, i_step)
time_machine.set_checkpoint('depth_0_%i'%i_step, sim_env)
time_machine.restore_from_checkpoint('depth_0_%i'%i_step, sim_env, sim_util.get_rope_params(rope_params))
eval_stats.success, eval_stats.feasible, eval_stats.misgrasp, full_trajs = simulate_demo_traj(sim_env, new_xyz, GlobalVars.actions[best_action], full_trajs, animate=args.animation, interactive=args.interactive)
print "BEST ACTION:", best_action
replay_trans, replay_rots = sim_util.get_rope_transforms(sim_env)
if np.linalg.norm(trans - replay_trans) > 0 or np.linalg.norm(rots - replay_rots) > 0:
yellowprint("The rope transforms of the replay rope doesn't match the ones in the original result file by %f and %f" % (np.linalg.norm(trans - replay_trans), np.linalg.norm(rots - replay_rots)))
else:
yellowprint("Reproducible results OK")
eval_util.save_task_results_step(args.resultfile, sim_env, i_task, i_step, eval_stats, best_action, full_trajs, q_values)
if is_knot(sim_env.sim.rope.GetControlPoints()):
break;
if is_knot(sim_env.sim.rope.GetControlPoints()):
num_successes += 1
num_total += 1
redprint('REPLAY Successes / Total: ' + str(num_successes) + '/' + str(num_total))
def load_simulation(args, sim_env):
sim_env.env = openravepy.Environment()
sim_env.env.StopSimulation()
sim_env.env.Load("robots/pr2-beta-static.zae")
sim_env.robot = sim_env.env.GetRobots()[0]
actions = h5py.File(args.actionfile, 'r')
fakedatafile = h5py.File(args.fakedatafile, 'r')
GlobalVars.init_tfm = fakedatafile['init_tfm'][()]
init_rope_xyz, _ = sim_util.load_fake_data_segment(sim_env, fakedatafile, args.fake_data_segment, args.fake_data_transform) # this also sets the torso (torso_lift_joint) to the height in the data
# Set table height to correct height of first rope in holdout set
holdoutfile = h5py.File(args.holdoutfile, 'r')
first_holdout = holdoutfile[holdoutfile.keys()[0]]
init_rope_xyz = first_holdout['rope_nodes'][:]
if 'frame' not in first_holdout or first_holdout['frame'][()] != 'r':
init_rope_xyz = init_rope_xyz.dot(GlobalVars.init_tfm[:3,:3].T) + GlobalVars.init_tfm[:3,3][None,:]
table_height = init_rope_xyz[:,2].mean() - .02 # Before: .02
table_xml = sim_util.make_table_xml(translation=[1, 0, table_height], extents=[.85, .55, .01])
sim_env.env.LoadData(table_xml)
if 'bookshelve' in args.obstacles:
sim_env.env.Load("data/bookshelves.env.xml")
if 'boxes' in args.obstacles:
sim_env.env.LoadData(sim_util.make_box_xml("box0", [.7,.43,table_height+(.01+.12)], [.12,.12,.12]))
sim_env.env.LoadData(sim_util.make_box_xml("box1", [.74,.47,table_height+(.01+.12*2+.08)], [.08,.08,.08]))
cc = trajoptpy.GetCollisionChecker(sim_env.env)
for gripper_link in [link for link in sim_env.robot.GetLinks() if 'gripper' in link.GetName()]:
cc.ExcludeCollisionPair(gripper_link, sim_env.env.GetKinBody('table').GetLinks()[0])
sim_util.reset_arms_to_side(sim_env)
if args.animation:
sim_env.viewer = trajoptpy.GetViewer(sim_env.env)
if args.animation > 1 and os.path.isfile(args.window_prop_file) and os.path.isfile(args.camera_matrix_file):
print "loading window and camera properties"
window_prop = np.loadtxt(args.window_prop_file)
camera_matrix = np.loadtxt(args.camera_matrix_file)
try:
sim_env.viewer.SetWindowProp(*window_prop)
sim_env.viewer.SetCameraManipulatorMatrix(camera_matrix)
except:
print "SetWindowProp and SetCameraManipulatorMatrix are not defined. Pull and recompile Trajopt."
else:
print "move viewer to viewpoint that isn't stupid"
print "then hit 'p' to continue"
sim_env.viewer.Idle()
print "saving window and camera properties"
try:
window_prop = sim_env.viewer.GetWindowProp()
camera_matrix = sim_env.viewer.GetCameraManipulatorMatrix()
np.savetxt(args.window_prop_file, window_prop, fmt='%d')
np.savetxt(args.camera_matrix_file, camera_matrix)
except:
print "GetWindowProp and GetCameraManipulatorMatrix are not defined. Pull and recompile Trajopt."
def eval_demo_playback(args, sim_env):
actionfile = GlobalVars.actions
num_successes = 0
num_total = 0
actions_names = []
for action_name in actionfile:
actions_names.append(action_name)
i = 0
i_task = 0
i_step = 0
while i < len(actions_names):
(demo_name, seg_name) = actions_names[i].split('-')
if seg_name == 'seg00':
if i > 0:
if is_knot(sim_env.sim.rope.GetControlPoints()):
redprint("KNOT TIED!")
num_successes += 1
num_total += 1
i_task += 1
i_step = 0
print "task %s" % demo_name
sim_util.reset_arms_to_side(sim_env, floating=True)
redprint("Replace rope")
rope_xyz = np.asarray(actionfile[actions_names[i]]['cloud_xyz'])
rope_nodes = rope_initialization.find_path_through_point_cloud(rope_xyz)
# don't call replace_rope and sim.settle() directly. use time machine interface for deterministic results!
time_machine = sim_util.RopeSimTimeMachine(rope_nodes, sim_env, rope_params=sim_util.get_rope_params("thick"), floating=True)
if args.animation:
sim_env.viewer.Step()
print "task %s step %i" % (i_task, i_step)
sim_util.reset_arms_to_side(sim_env, floating=True)
redprint("Observe point cloud")
#new_xyz = sim_env.sim.observe_cloud(GlobalVars.rope_observe_cloud_upsample)
new_xyz = sim_env.sim.observe_cloud2(0.01, GlobalVars.rope_observe_cloud_upsample, 1, 4)
state = ("eval_%i"%get_unique_id(), new_xyz)
action = actions_names[i]
eval_stats = eval_util.EvalStats()
eval_stats.success, eval_stats.feasible, eval_stats.misgrasp, full_trajs = \
compute_trans_traj(sim_env, new_xyz, GlobalVars.actions[action], animate=args.animation, interactive=args.interactive)
i += 1
num_total += 1
redprint('Eval Successes / Total: ' + str(num_successes) + '/' + str(num_total))
def load_simulation(args, sim_env):
sim_env.env = openravepy.Environment()
sim_env.env.StopSimulation()
actions = h5py.File(args.actionfile, 'r')
fakedatafile = h5py.File(args.fakedatafile, 'r')
GlobalVars.init_tfm = fakedatafile['init_tfm'][()]
init_rope_xyz, _ = sim_util.load_fake_data_segment(sim_env, fakedatafile, args.fake_data_segment, args.fake_data_transform, floating=True) # this also sets the torso (torso_lift_joint) to the height in the data
GlobalVars.init_tfm = GlobalVars.init_tfm
# Set table height to correct height of first rope in holdout set
holdoutfile = h5py.File(args.holdoutfile, 'r')
first_holdout = holdoutfile[holdoutfile.keys()[0]]
init_rope_xyz = first_holdout['rope_nodes'][:]
if 'frame' not in first_holdout or first_holdout['frame'][()] != 'r':
init_rope_xyz = init_rope_xyz.dot(GlobalVars.init_tfm[:3,:3].T) + GlobalVars.init_tfm[:3,3][None,:]
table_height = init_rope_xyz[:,2].mean() - .02 # Before: .02
GlobalVars.table_height = table_height
table_xml = sim_util.make_table_xml(translation=[1, 0, table_height], extents=[.85, .55, .01])
sim_env.env.LoadData(table_xml)
sim_util.reset_arms_to_side(sim_env, floating=True)
if args.animation:
sim_env.viewer = trajoptpy.GetViewer(sim_env.env)
table = sim_env.env.GetKinBody('table')
sim_env.viewer.SetTransparency(table, 0.4)
if args.animation > 1 and os.path.isfile(args.window_prop_file) and os.path.isfile(args.camera_matrix_file):
print "loading window and camera properties"
window_prop = np.loadtxt(args.window_prop_file)
camera_matrix = np.loadtxt(args.camera_matrix_file)
try:
sim_env.viewer.SetWindowProp(*window_prop)
sim_env.viewer.SetCameraManipulatorMatrix(camera_matrix)
except:
print "SetWindowProp and SetCameraManipulatorMatrix are not defined. Pull and recompile Trajopt."
else:
print "move viewer to viewpoint that isn't stupid"
print "then hit 'p' to continue"
sim_env.viewer.Idle()
print "saving window and camera properties"
try:
window_prop = sim_env.viewer.GetWindowProp()
camera_matrix = sim_env.viewer.GetCameraManipulatorMatrix()
np.savetxt(args.window_prop_file, window_prop, fmt='%d')
np.savetxt(args.camera_matrix_file, camera_matrix)
except:
print "GetWindowProp and GetCameraManipulatorMatrix are not defined. Pull and recompile Trajopt."
def simulate_demo_traj(sim_env, new_xyz, seg_info, full_trajs, ignore_infeasibility=True, animate=False, interactive=False):
sim_util.reset_arms_to_side(sim_env)
old_xyz = np.squeeze(seg_info["cloud_xyz"])
old_xyz = clouds.downsample(old_xyz, DS_SIZE)
new_xyz = clouds.downsample(new_xyz, DS_SIZE)
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)))
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
for (i_miniseg, (i_start, i_end)) in enumerate(zip(miniseg_starts, miniseg_ends)):
if i_miniseg >= len(full_trajs): break
full_traj = full_trajs[i_miniseg]
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 load_simulation(args, sim_env):
sim_env.env = openravepy.Environment()
sim_env.env.StopSimulation()
sim_env.env.Load("robots/pr2-beta-static.zae")
sim_env.robot = sim_env.env.GetRobots()[0]
actions = h5py.File(args.actionfile, 'r')
fakedatafile = h5py.File(args.fakedatafile, 'r')
GlobalVars.init_tfm = fakedatafile['init_tfm'][()]
init_rope_xyz, _ = sim_util.load_fake_data_segment(
sim_env, fakedatafile, args.fake_data_segment, args.fake_data_transform
) # this also sets the torso (torso_lift_joint) to the height in the data
# Set table height to correct height of first rope in holdout set
holdoutfile = h5py.File(args.holdoutfile, 'r')
first_holdout = holdoutfile[holdoutfile.keys()[0]]
init_rope_xyz = first_holdout['rope_nodes'][:]
if 'frame' not in first_holdout or first_holdout['frame'][()] != 'r':
init_rope_xyz = init_rope_xyz.dot(
GlobalVars.init_tfm[:3, :3].T) + GlobalVars.init_tfm[:3,
3][None, :]
table_height = init_rope_xyz[:, 2].mean() - .02 # Before: .02
table_xml = sim_util.make_table_xml(translation=[1, 0, table_height],
extents=[.85, .55, .01])
sim_env.env.LoadData(table_xml)
if 'bookshelve' in args.obstacles:
sim_env.env.Load("data/bookshelves.env.xml")
if 'boxes' in args.obstacles:
sim_env.env.LoadData(
sim_util.make_box_xml("box0",
[.7, .43, table_height + (.01 + .12)],
[.12, .12, .12]))
sim_env.env.LoadData(
sim_util.make_box_xml(
"box1", [.74, .47, table_height + (.01 + .12 * 2 + .08)],
[.08, .08, .08]))
cc = trajoptpy.GetCollisionChecker(sim_env.env)
for gripper_link in [
link for link in sim_env.robot.GetLinks()
if 'gripper' in link.GetName()
]:
cc.ExcludeCollisionPair(gripper_link,
sim_env.env.GetKinBody('table').GetLinks()[0])
sim_util.reset_arms_to_side(sim_env)
if args.animation:
sim_env.viewer = trajoptpy.GetViewer(sim_env.env)
if args.animation > 1 and os.path.isfile(
args.window_prop_file) and os.path.isfile(
args.camera_matrix_file):
print "loading window and camera properties"
window_prop = np.loadtxt(args.window_prop_file)
camera_matrix = np.loadtxt(args.camera_matrix_file)
try:
sim_env.viewer.SetWindowProp(*window_prop)
sim_env.viewer.SetCameraManipulatorMatrix(camera_matrix)
except:
print "SetWindowProp and SetCameraManipulatorMatrix are not defined. Pull and recompile Trajopt."
else:
print "move viewer to viewpoint that isn't stupid"
print "then hit 'p' to continue"
sim_env.viewer.Idle()
print "saving window and camera properties"
try:
window_prop = sim_env.viewer.GetWindowProp()
camera_matrix = sim_env.viewer.GetCameraManipulatorMatrix()
np.savetxt(args.window_prop_file, window_prop, fmt='%d')
np.savetxt(args.camera_matrix_file, camera_matrix)
except:
print "GetWindowProp and GetCameraManipulatorMatrix are not defined. Pull and recompile Trajopt."
def replay_on_holdout(args, sim_env):
holdoutfile = h5py.File(args.holdoutfile, 'r')
tasks = eval_util.get_specified_tasks(args.tasks, args.taskfile,
args.i_start, args.i_end)
loadresultfile = h5py.File(args.loadresultfile, 'r')
loadresult_items = eval_util.get_holdout_items(loadresultfile, tasks)
num_successes = 0
num_total = 0
for i_task, demo_id_rope_nodes in loadresult_items:
print "task %s" % i_task
sim_util.reset_arms_to_side(sim_env)
redprint("Replace rope")
rope_nodes, rope_params, _, _ = eval_util.load_task_results_init(
args.loadresultfile, i_task)
# uncomment if the results file don't have the right rope nodes
#rope_nodes = demo_id_rope_nodes["rope_nodes"][:]
if args.replay_rope_params:
rope_params = args.replay_rope_params
# don't call replace_rope and sim.settle() directly. use time machine interface for deterministic results!
time_machine = sim_util.RopeSimTimeMachine(rope_nodes, sim_env)
if args.animation:
sim_env.viewer.Step()
eval_util.save_task_results_init(args.resultfile, sim_env, i_task,
rope_nodes, rope_params)
for i_step in range(
len(loadresultfile[i_task]) -
(1 if 'init' in loadresultfile[i_task] else 0)):
print "task %s step %i" % (i_task, i_step)
sim_util.reset_arms_to_side(sim_env)
redprint("Observe point cloud")
new_xyz = sim_env.sim.observe_cloud()
eval_stats = eval_util.EvalStats()
best_action, full_trajs, q_values, trans, rots = eval_util.load_task_results_step(
args.loadresultfile, sim_env, i_task, i_step)
time_machine.set_checkpoint('depth_0_%i' % i_step, sim_env)
time_machine.restore_from_checkpoint(
'depth_0_%i' % i_step, sim_env,
sim_util.get_rope_params(rope_params))
eval_stats.success, eval_stats.feasible, eval_stats.misgrasp, full_trajs = simulate_demo_traj(
sim_env,
new_xyz,
GlobalVars.actions[best_action],
full_trajs,
animate=args.animation,
interactive=args.interactive)
print "BEST ACTION:", best_action
replay_trans, replay_rots = sim_util.get_rope_transforms(sim_env)
if np.linalg.norm(trans - replay_trans) > 0 or np.linalg.norm(
rots - replay_rots) > 0:
yellowprint(
"The rope transforms of the replay rope doesn't match the ones in the original result file by %f and %f"
% (np.linalg.norm(trans - replay_trans),
np.linalg.norm(rots - replay_rots)))
else:
yellowprint("Reproducible results OK")
eval_util.save_task_results_step(args.resultfile, sim_env, i_task,
i_step, eval_stats, best_action,
full_trajs, q_values)
if is_knot(sim_env.sim.rope.GetControlPoints()):
break
if is_knot(sim_env.sim.rope.GetControlPoints()):
num_successes += 1
num_total += 1
redprint('REPLAY Successes / Total: ' + str(num_successes) + '/' +
str(num_total))
def eval_on_holdout(args, sim_env):
feature_fn, _, num_features, actions = select_feature_fn(args)
weightfile = h5py.File(args.weightfile, 'r')
weights = weightfile['weights'][:]
w0 = weightfile['w0'][()] if 'w0' in weightfile else 0
weightfile.close()
assert weights.shape[
0] == num_features, "Dimensions of weights and features don't match. Make sure the right feature is being used"
holdoutfile = h5py.File(args.holdoutfile, 'r')
tasks = eval_util.get_specified_tasks(args.tasks, args.taskfile,
args.i_start, args.i_end)
holdout_items = eval_util.get_holdout_items(holdoutfile, tasks)
num_successes = 0
num_total = 0
for i_task, demo_id_rope_nodes in holdout_items:
print "task %s" % i_task
sim_util.reset_arms_to_side(sim_env)
redprint("Replace rope")
rope_xyz = demo_id_rope_nodes["rope_nodes"][:]
# Transform rope_nodes from the kinect's frame into the frame of the PR2
if 'frame' not in demo_id_rope_nodes or demo_id_rope_nodes['frame'][
()] != 'r':
redprint("Transforming rope into frame of robot")
rope_xyz = rope_xyz.dot(GlobalVars.init_tfm[:3, :3].T
) + GlobalVars.init_tfm[:3, 3][None, :]
rope_nodes = rope_initialization.find_path_through_point_cloud(
rope_xyz)
# don't call replace_rope and sim.settle() directly. use time machine interface for deterministic results!
time_machine = sim_util.RopeSimTimeMachine(rope_nodes, sim_env)
if args.animation:
sim_env.viewer.Step()
eval_util.save_task_results_init(args.resultfile, sim_env, i_task,
rope_nodes, args.exec_rope_params)
for i_step in range(args.num_steps):
print "task %s step %i" % (i_task, i_step)
sim_util.reset_arms_to_side(sim_env)
redprint("Observe point cloud")
new_xyz = sim_env.sim.observe_cloud()
state = ("eval_%i" % get_unique_id(), new_xyz)
redprint("Choosing an action")
q_values = [
q_value_fn(state, action, feature_fn, weights, w0)
for action in actions
]
q_values_root = q_values
time_machine.set_checkpoint('depth_0_%i' % i_step, sim_env)
assert args.lookahead_width >= 1, 'Lookahead branches set to zero will fail to select any action'
agenda = sorted(zip(q_values, actions),
key=lambda v: -v[0])[:args.lookahead_width]
agenda = [
(v, a, 'depth_0_%i' % i_step, a) for (v, a) in agenda
] # state is (value, most recent action, checkpoint id, root action)
best_root_action = None
for depth in range(args.lookahead_depth):
expansion_results = []
for (branch, (q, a, chkpt, r_a)) in enumerate(agenda):
time_machine.restore_from_checkpoint(
chkpt, sim_env,
sim_util.get_rope_params(args.lookahead_rope_params))
cur_xyz = sim_env.sim.observe_cloud()
success, _, _, full_trajs = \
compute_trans_traj(sim_env, cur_xyz, GlobalVars.actions[a], animate=args.animation, interactive=False)
if args.animation:
sim_env.viewer.Step()
if is_knot(sim_env.sim.rope.GetControlPoints()):
best_root_action = r_a
break
result_cloud = sim_env.sim.observe_cloud()
result_chkpt = 'depth_%i_branch_%i_%i' % (depth + 1,
branch, i_step)
if depth != args.lookahead_depth - 1: # don't save checkpoint at the last depth to save computation time
time_machine.set_checkpoint(result_chkpt, sim_env)
expansion_results.append(
(result_cloud, a, success, result_chkpt, r_a))
if best_root_action is not None:
redprint('Knot Found, stopping search early')
break
agenda = []
for (cld, incoming_a, success, chkpt,
r_a) in expansion_results:
if not success:
agenda.append((-np.inf, actions[0], chkpt,
r_a)) # TODO why first action?
continue
next_state = ("eval_%i" % get_unique_id(), cld)
q_values = [(q_value_fn(next_state, action, feature_fn,
weights, w0), action, chkpt, r_a)
for action in actions]
agenda.extend(q_values)
agenda.sort(key=lambda v: -v[0])
agenda = agenda[:args.lookahead_width]
first_root_action = agenda[0][-1]
if all(r_a == first_root_action for (_, _, _, r_a) in agenda):
best_root_action = first_root_action
redprint(
'All best actions have same root, stopping search early'
)
break
if best_root_action is None:
best_root_action = agenda[0][-1]
time_machine.restore_from_checkpoint(
'depth_0_%i' % i_step, sim_env,
sim_util.get_rope_params(args.exec_rope_params))
eval_stats = eval_util.EvalStats()
eval_stats.success, eval_stats.feasible, eval_stats.misgrasp, full_trajs = \
compute_trans_traj(sim_env, new_xyz, GlobalVars.actions[best_root_action], animate=args.animation, interactive=args.interactive)
print "BEST ACTION:", best_root_action
eval_util.save_task_results_step(args.resultfile, sim_env, i_task,
i_step, eval_stats,
best_root_action, full_trajs,
q_values_root)
if is_knot(sim_env.sim.rope.GetControlPoints()):
redprint("KNOT TIED!")
break
if is_knot(sim_env.sim.rope.GetControlPoints()):
num_successes += 1
num_total += 1
redprint('Eval Successes / Total: ' + str(num_successes) + '/' +
str(num_total))
def simulate_demo_traj(sim_env,
new_xyz,
seg_info,
full_trajs,
ignore_infeasibility=True,
animate=False,
interactive=False):
sim_util.reset_arms_to_side(sim_env)
old_xyz = np.squeeze(seg_info["cloud_xyz"])
old_xyz = clouds.downsample(old_xyz, DS_SIZE)
new_xyz = clouds.downsample(new_xyz, DS_SIZE)
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)))
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
for (i_miniseg, (i_start,
i_end)) in enumerate(zip(miniseg_starts, miniseg_ends)):
if i_miniseg >= len(full_trajs): break
full_traj = full_trajs[i_miniseg]
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 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 eval_on_holdout(args, sim_env):
feature_fn, _, num_features, actions = select_feature_fn(args)
weightfile = h5py.File(args.weightfile, 'r')
weights = weightfile['weights'][:]
w0 = weightfile['w0'][()] if 'w0' in weightfile else 0
weightfile.close()
assert weights.shape[0] == num_features, "Dimensions of weights and features don't match. Make sure the right feature is being used"
holdoutfile = h5py.File(args.holdoutfile, 'r')
tasks = eval_util.get_specified_tasks(args.tasks, args.taskfile, args.i_start, args.i_end)
holdout_items = eval_util.get_holdout_items(holdoutfile, tasks)
num_successes = 0
num_total = 0
for i_task, demo_id_rope_nodes in holdout_items:
print "task %s" % i_task
sim_util.reset_arms_to_side(sim_env)
redprint("Replace rope")
rope_xyz = demo_id_rope_nodes["rope_nodes"][:]
# Transform rope_nodes from the kinect's frame into the frame of the PR2
if 'frame' not in demo_id_rope_nodes or demo_id_rope_nodes['frame'][()] != 'r':
redprint("Transforming rope into frame of robot")
rope_xyz = rope_xyz.dot(GlobalVars.init_tfm[:3,:3].T) + GlobalVars.init_tfm[:3,3][None,:]
rope_nodes = rope_initialization.find_path_through_point_cloud(rope_xyz)
# don't call replace_rope and sim.settle() directly. use time machine interface for deterministic results!
time_machine = sim_util.RopeSimTimeMachine(rope_nodes, sim_env)
if args.animation:
sim_env.viewer.Step()
eval_util.save_task_results_init(args.resultfile, sim_env, i_task, rope_nodes, args.exec_rope_params)
for i_step in range(args.num_steps):
print "task %s step %i" % (i_task, i_step)
sim_util.reset_arms_to_side(sim_env)
redprint("Observe point cloud")
new_xyz = sim_env.sim.observe_cloud()
state = ("eval_%i"%get_unique_id(), new_xyz)
redprint("Choosing an action")
q_values = [q_value_fn(state, action, feature_fn, weights, w0) for action in actions]
q_values_root = q_values
time_machine.set_checkpoint('depth_0_%i'%i_step, sim_env)
assert args.lookahead_width>= 1, 'Lookahead branches set to zero will fail to select any action'
agenda = sorted(zip(q_values, actions), key = lambda v: -v[0])[:args.lookahead_width]
agenda = [(v, a, 'depth_0_%i'%i_step, a) for (v, a) in agenda] # state is (value, most recent action, checkpoint id, root action)
best_root_action = None
for depth in range(args.lookahead_depth):
expansion_results = []
for (branch, (q, a, chkpt, r_a)) in enumerate(agenda):
time_machine.restore_from_checkpoint(chkpt, sim_env, sim_util.get_rope_params(args.lookahead_rope_params))
cur_xyz = sim_env.sim.observe_cloud()
success, _, _, full_trajs = \
compute_trans_traj(sim_env, cur_xyz, GlobalVars.actions[a], animate=args.animation, interactive=False)
if args.animation:
sim_env.viewer.Step()
if is_knot(sim_env.sim.rope.GetControlPoints()):
best_root_action = r_a
break
result_cloud = sim_env.sim.observe_cloud()
result_chkpt = 'depth_%i_branch_%i_%i'%(depth+1, branch, i_step)
if depth != args.lookahead_depth-1: # don't save checkpoint at the last depth to save computation time
time_machine.set_checkpoint(result_chkpt, sim_env)
expansion_results.append((result_cloud, a, success, result_chkpt, r_a))
if best_root_action is not None:
redprint('Knot Found, stopping search early')
break
agenda = []
for (cld, incoming_a, success, chkpt, r_a) in expansion_results:
if not success:
agenda.append((-np.inf, actions[0], chkpt, r_a)) # TODO why first action?
continue
next_state = ("eval_%i"%get_unique_id(), cld)
q_values = [(q_value_fn(next_state, action, feature_fn, weights, w0), action, chkpt, r_a) for action in actions]
agenda.extend(q_values)
agenda.sort(key = lambda v: -v[0])
agenda = agenda[:args.lookahead_width]
first_root_action = agenda[0][-1]
if all(r_a == first_root_action for (_, _, _, r_a) in agenda):
best_root_action = first_root_action
redprint('All best actions have same root, stopping search early')
break
if best_root_action is None:
best_root_action = agenda[0][-1]
time_machine.restore_from_checkpoint('depth_0_%i'%i_step, sim_env, sim_util.get_rope_params(args.exec_rope_params))
eval_stats = eval_util.EvalStats()
eval_stats.success, eval_stats.feasible, eval_stats.misgrasp, full_trajs = \
compute_trans_traj(sim_env, new_xyz, GlobalVars.actions[best_root_action], animate=args.animation, interactive=args.interactive)
print "BEST ACTION:", best_root_action
eval_util.save_task_results_step(args.resultfile, sim_env, i_task, i_step, eval_stats, best_root_action, full_trajs, q_values_root)
if is_knot(sim_env.sim.rope.GetControlPoints()):
redprint("KNOT TIED!")
break;
if is_knot(sim_env.sim.rope.GetControlPoints()):
num_successes += 1
num_total += 1
redprint('Eval Successes / Total: ' + str(num_successes) + '/' + str(num_total))
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