def generateBodies(cloud, id):
# BEGIN addtoenv
names = []
if args.geom_type == "mesh":
mesh = generate_mesh(cloud)
name = "simple_mesh_%i" % id
mesh_body = mk.create_trimesh(env,
get_xyz_world_frame(mesh.getCloud()),
np.array(mesh.getFaces()),
name=name)
mesh_body.SetUserData(
"bt_use_trimesh", True
) # Tell collision checker to use the trimesh rather than the convex hull of it
names.append(name)
elif args.geom_type == "cd":
big_mesh = generate_mesh(cloud)
convex_meshes = cloudprocpy.convexDecompHACD(big_mesh, 30)
for (i, mesh) in enumerate(convex_meshes):
name = "mesh_%i_%i" % (id, i)
verts = get_xyz_world_frame(mesh.getCloud())
mk.create_trimesh(env, verts, mesh.getTriangles(), name=name)
randcolor = np.random.rand(3)
print 'vertices'
print mesh.getVertices()
if not isValidMesh(mesh):
print('Mesh invalid. Removing kinbody')
env.RemoveKinBody(env.GetKinBody(name))
continue
env.GetKinBody(name).GetLinks()[0].GetGeometries(
)[0].SetAmbientColor(randcolor)
env.GetKinBody(name).GetLinks()[0].GetGeometries(
)[0].SetDiffuseColor(randcolor)
names.append(name)
# END addtoenv
elif args.geom_type == "spheres":
raise Exception("don't use spheres--there's a performance issue")
cloud_ds = cloudprocpy.downsampleCloud(cloud, .04)
name = "spheres_%i" % id
mk.create_spheres(env, get_xyz_world_frame(cloud_ds), .02, name=name)
names.append(name)
elif args.geom_type == "boxes":
cloud_ds = cloudprocpy.downsampleCloud(cloud, .04)
name = "boxes_%i" % id
mk.create_boxes(env, get_xyz_world_frame(cloud_ds), .02, name=name)
names.append(name)
return names
def generateBodies(cloud, id):
# BEGIN addtoenv
names = []
if args.geom_type == "mesh":
mesh = generate_mesh(cloud)
name = "simple_mesh_%i"%id
mesh_body = mk.create_trimesh(env, get_xyz_world_frame(mesh.getCloud()), np.array(mesh.getFaces()), name=name)
mesh_body.SetUserData("bt_use_trimesh", True) # Tell collision checker to use the trimesh rather than the convex hull of it
names.append(name)
elif args.geom_type == "cd":
big_mesh = generate_mesh(cloud)
convex_meshes = cloudprocpy.convexDecompHACD(big_mesh,30)
for (i,mesh) in enumerate(convex_meshes):
name = "mesh_%i_%i" % (id,i)
verts = get_xyz_world_frame(mesh.getCloud())
mk.create_trimesh(env, verts, mesh.getTriangles(), name=name)
randcolor = np.random.rand(3)
print 'vertices'
print mesh.getVertices()
if not isValidMesh(mesh):
print('Mesh invalid. Removing kinbody')
env.RemoveKinBody(env.GetKinBody(name))
continue
env.GetKinBody(name).GetLinks()[0].GetGeometries()[0].SetAmbientColor(randcolor)
env.GetKinBody(name).GetLinks()[0].GetGeometries()[0].SetDiffuseColor(randcolor)
names.append(name)
# END addtoenv
elif args.geom_type == "spheres":
raise Exception("don't use spheres--there's a performance issue")
cloud_ds = cloudprocpy.downsampleCloud(cloud, .04)
name = "spheres_%i"%id
mk.create_spheres(env, get_xyz_world_frame(cloud_ds), .02, name=name)
names.append(name)
elif args.geom_type == "boxes":
cloud_ds = cloudprocpy.downsampleCloud(cloud, .04)
name = "boxes_%i"%id
mk.create_boxes(env, get_xyz_world_frame(cloud_ds), .02, name=name)
names.append(name)
return names
def generateBodies(self, cloud, id):
# BEGIN addtoenv
names = []
if self.geom_type == "mesh":
mesh = self.generate_mesh(cloud)
name = "simple_mesh_%i"%id
mesh_body = mk.create_trimesh(self.env, self.get_xyz_world_frame(mesh.getCloud()), np.array(mesh.getFaces()), name=name)
mesh_body.SetUserData("bt_use_trimesh", True) # Tell collision checker to use the trimesh rather than the convex hull of it
names.append(name)
elif self.geom_type == "cd":
big_mesh = self.generate_mesh(cloud)
#name = 'big_mesh'
#verts = self.get_xyz_world_frame(big_mesh.getCloud())
#mk.create_trimesh(self.env, verts, big_mesh.getTriangles(), name=name)
convex_meshes = cloudprocpy.convexDecompHACD(big_mesh, self.num_cd_components)
for (i,mesh) in enumerate(convex_meshes):
name = "mesh_%i_%i" % (id,i)
verts = self.get_xyz_world_frame(mesh.getCloud())
mk.create_trimesh(self.env, verts, mesh.getTriangles(), name=name)
randcolor = np.random.rand(3)
self.env.GetKinBody(name).GetLinks()[0].GetGeometries()[0].SetAmbientColor(randcolor)
self.env.GetKinBody(name).GetLinks()[0].GetGeometries()[0].SetDiffuseColor(randcolor)
names.append(name)
# END addtoenv
elif self.geom_type == "spheres":
raise Exception("don't use spheres--there's a performance issue")
cloud_ds = cloudprocpy.downsampleCloud(cloud, .04)
name = "spheres_%i"%id
mk.create_spheres(self.env, self.get_xyz_world_frame(cloud_ds), .02, name=name)
names.append(name)
elif self.geom_type == "boxes":
cloud_ds = cloudprocpy.downsampleCloud(cloud, .04)
name = "boxes_%i"%id
mk.create_boxes(self.env, self.get_xyz_world_frame(cloud_ds), .02, name=name)
names.append(name)
return names
def generateBodies(self, cloud, id):
# BEGIN addtoenv
names = []
if self.geom_type == "mesh":
mesh = self.generate_mesh(cloud)
name = "simple_mesh_%i"%id
mesh_body = mk.create_trimesh(self.env, self.get_xyz_world_frame(mesh.getCloud()), np.array(mesh.getFaces()), name=name)
mesh_body.SetUserData("bt_use_trimesh", True) # Tell collision checker to use the trimesh rather than the convex hull of it
names.append(name)
elif self.geom_type == "cd":
big_mesh = self.generate_mesh(cloud)
convex_meshes = cloudprocpy.convexDecompHACD(big_mesh, self.num_cd_components)
for (i,mesh) in enumerate(convex_meshes):
name = "mesh_%i_%i" % (id,i)
verts = self.get_xyz_world_frame(mesh.getCloud())
mk.create_trimesh(self.env, verts, mesh.getTriangles(), name=name)
randcolor = np.random.rand(3)
self.env.GetKinBody(name).GetLinks()[0].GetGeometries()[0].SetAmbientColor(randcolor)
self.env.GetKinBody(name).GetLinks()[0].GetGeometries()[0].SetDiffuseColor(randcolor)
names.append(name)
# END addtoenv
elif self.geom_type == "spheres":
raise Exception("don't use spheres--there's a performance issue")
cloud_ds = cloudprocpy.downsampleCloud(cloud, .04)
name = "spheres_%i"%id
mk.create_spheres(self.env, self.get_xyz_world_frame(cloud_ds), .02, name=name)
names.append(name)
elif self.geom_type == "boxes":
cloud_ds = cloudprocpy.downsampleCloud(cloud, .04)
name = "boxes_%i"%id
mk.create_boxes(self.env, self.get_xyz_world_frame(cloud_ds), .02, name=name)
names.append(name)
return names
elif args.geom_type == "cd":
big_mesh = generate_mesh(cloud_orig)
convex_meshes = cloudprocpy.convexDecompHACD(big_mesh,30)
for (i,mesh) in enumerate(convex_meshes):
name = "mesh%i"%i
verts = get_xyz_world_frame(mesh.getCloud())
mk.create_trimesh(env, verts, mesh.getTriangles(), name=name)
randcolor = np.random.rand(3)
env.GetKinBody(name).GetLinks()[0].GetGeometries()[0].SetAmbientColor(randcolor)
env.GetKinBody(name).GetLinks()[0].GetGeometries()[0].SetDiffuseColor(randcolor)
# END addtoenv
elif args.geom_type == "spheres":
raise Exception("don't use spheres--there's a performance issue")
cloud_nofloor = remove_floor(cloud_orig)
cloud_ds = cloudprocpy.downsampleCloud(cloud_nofloor, .04)
mk.create_spheres(env, get_xyz_world_frame(cloud_ds), .02)
elif args.geom_type == "boxes":
cloud_nofloor = remove_floor(cloud_orig)
cloud_ds = cloudprocpy.downsampleCloud(cloud_nofloor, .04)
mk.create_boxes(env, get_xyz_world_frame(cloud_ds), .02)
def full_body_drive_and_reach(robot, link_name, xyz_targ, quat_targ):
request = {
"basic_info" : {
"n_steps" : 10,
"manip" : "active",
"start_fixed" : True
},
"costs" : [
convex_meshes = cloudprocpy.convexDecompHACD(big_mesh, 30)
for (i, mesh) in enumerate(convex_meshes):
name = "mesh%i" % i
verts = get_xyz_world_frame(mesh.getCloud())
mk.create_trimesh(env, verts, mesh.getTriangles(), name=name)
randcolor = np.random.rand(3)
env.GetKinBody(name).GetLinks()[0].GetGeometries()[0].SetAmbientColor(
randcolor)
env.GetKinBody(name).GetLinks()[0].GetGeometries()[0].SetDiffuseColor(
randcolor)
# END addtoenv
elif args.geom_type == "spheres":
raise Exception("don't use spheres--there's a performance issue")
cloud_nofloor = remove_floor(cloud_orig)
cloud_ds = cloudprocpy.downsampleCloud(cloud_nofloor, .04)
mk.create_spheres(env, get_xyz_world_frame(cloud_ds), .02)
elif args.geom_type == "boxes":
cloud_nofloor = remove_floor(cloud_orig)
cloud_ds = cloudprocpy.downsampleCloud(cloud_nofloor, .04)
mk.create_boxes(env, get_xyz_world_frame(cloud_ds), .02)
def full_body_drive_and_reach(link_name, xyz_targ, quat_targ):
request = {
"basic_info": {
"n_steps": 10,
"manip": "active",
"start_fixed": True
},
"costs": [{
def main():
demofile = h5py.File(args.h5file, 'r')
trajoptpy.SetInteractive(args.interactive)
rospy.init_node("exec_task",disable_signals=True)
global filename
if args.logging:
name = raw_input ('Enter identifier of this experiment (without spaces): ')
name = name + '.yaml'
import os.path as osp
filename = osp.join('/home/sibi/sandbox/rapprentice/experiments/', name)
if not osp.exists(filename):
with open(filename,'w') as fh:
fh.write("experiment: %s\ninfo: \n"%name)
#thc.start()
if args.execution:
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]
import trajoptpy.make_kinbodies as mk
#Globals.env.Load("/home/sibi/sandbox/rapprentice/objects/table.xml")
addBoxToRave(Globals.env, "table")
Globals.sponge = addBoxToRave(Globals.env, "sponge") # Not sure about this
Globals.needle_tip = mk.create_spheres(Globals.env, [(0,0,0)], radii=0.02, name="needle_tip")
Globals.demo_env = Globals.env.CloneSelf(1)
Globals.demo_env.StopSimulation()
Globals.demo_robot = Globals.demo_env.GetRobot("pr2")
Globals.demo_needle_tip = Globals.demo_env.GetKinBody("needle_tip")
if not args.fake_data_segment:
grabber = cloudprocpy.CloudGrabber()
grabber.startRGBD()
thc.grabber = grabber
#Globals.viewer = trajoptpy.GetViewer(Globals.env)
#####################
#Globals.env.SetViewer('qtcoin')
#threadClass().start()
while True:
redprint("Acquire point cloud")
################################
redprint("Finding closest demonstration")
if args.fake_data_segment:
seg_name = args.fake_data_segment
else:
seg_name = find_closest(demofile)
seg_info = demofile[seg_name]
# redprint("using demo %s, description: %s"%(seg_name, seg_info["description"]))
################################
redprint("Generating end-effector trajectory")
handles = []
if seg_info.get('ar_marker_poses') is None:
use_markers = False
else:
use_markers = True
if use_markers:
old_marker_poses_str = seg_info['ar_marker_poses']
old_marker_poses = {}
for i in old_marker_poses_str:
old_marker_poses[int(i)] = old_marker_poses_str[i]
# TODO: Have a check for only transformations -> rigid transformations
if args.fake_data_segment:
new_keypoints = demofile[args.fake_data_segment]["key_points"]
if use_markers:
new_marker_poses_str = demofile[args.fake_data_segment]["ar_marker_poses"]
new_marker_poses = {}
for i in new_marker_poses_str:
new_marker_poses[int(i)] = new_marker_poses_str[i]
if seg_info['key_points'].keys().sort() != new_keypoints.keys().sort():
print "Keypoints don't match."
exit(1)
else:
if args.execution: Globals.pr2.update_rave()
T_w_k = berkeley_pr2.get_kinect_transform(Globals.robot)
new_keypoints, new_marker_poses = fk.get_keypoints_execution(grabber, seg_info['key_points'].keys(), T_w_k, use_markers)
print "Warping the points for the new situation."
if "l_grab" in seg_info['extra_information'] or "r_grab" in seg_info['extra_information']:
use_ntt_kp = False
else:
use_ntt_kp = True
# Points from keypoints
old_xyz, rigid, kp_mapping = fk.key_points_to_points(seg_info['key_points'], use_ntt_kp)
new_xyz, _, _ = fk.key_points_to_points(new_keypoints, use_ntt_kp)
# Points from markers
if use_markers:
old_common_poses, new_common_poses = find_common_marker_poses(old_marker_poses, new_marker_poses, new_keypoints.keys())
old_m_xyz, rigid_m, _ = fk.key_points_to_points(old_common_poses, False)
new_m_xyz, _, _ = fk.key_points_to_points(new_common_poses, False)
if len(old_m_xyz) > 0 and rigid: rigid = False
elif rigid_m and not old_xyz.any(): rigid = True
# concatenate points
if old_xyz.any() and old_m_xyz.any():
old_xyz = np.r_[old_xyz, old_m_xyz]
elif old_m_xyz.any():
old_xyz = old_m_xyz
if new_xyz.any() and new_m_xyz.any():
new_xyz = np.r_[new_xyz, new_m_xyz]
elif new_m_xyz.any():
new_xyz = new_m_xyz
if new_xyz.any() and new_xyz.shape != (4,4):
#handles.append(Globals.env.plot3(old_xyz,5, (1,0,0,1)))
#handles.append(Globals.env.plot3(old_xyz,5, np.array([(1,0,0) for _ in xrange(old_xyz.shape[0])])))
handles.append(Globals.env.plot3(new_xyz,5, np.array([(0,0,1) for _ in xrange(old_xyz.shape[0])])))
print 'Old points:', old_xyz
print 'New points:', new_xyz
#if not yes_or_no.yes_or_no("Use identity?"):
if rigid:
f = registration.ThinPlateSpline()
rel_tfm = new_xyz.dot(np.linalg.inv(old_xyz))
f.init_rigid_tfm(rel_tfm)
bend_c = 0
rot_c = 0
scale_c = 0
max_error = None
elif len(new_xyz) > 0:
#f.fit(demopoints_m3, newpoints_m3, 10,10)
# TODO - check if this regularization on bending is correct
#if "right_hole_normal" in new_keypoints or "left_hole_normal" in new_keypoints:
# bend_c = 0.1
# rot_c = [1e-5,1e-5,0.1]
#wt = 5
#wt_n = np.ones(len(old_xyz))
#if kp_mapping.get("right_hole_normal"):
# wt_n[kp_mapping["right_hole_normal"][0]] = wt
#if kp_mapping.get("left_hole_normal"):
# wt_n[kp_mapping["left_hole_normal"][0]] = wt
#else:
# bend_c = 0.1
# rot_c = 1e-5#[0,0,1e-5]
# wt_n = None
# f = registration.fit_ThinPlateSpline(old_xyz, new_xyz, bend_coef = bend_c,rot_coef = rot_c)
bend_c = 0.05
rot_c = [1e-3, 1e-3, 1e-3]
scale_c = 0.1
f = registration.fit_ThinPlateSpline_RotReg(old_xyz, new_xyz, bend_c, rot_c, scale_c)
np.set_printoptions(precision=3)
max_error = np.max(np.abs(f.transform_points(old_xyz) - new_xyz))
print "nonlinear part", f.w_ng
print "affine part", f.lin_ag
print "translation part", f.trans_g
print "residual", f.transform_points(old_xyz) - new_xyz
print "max error ", max_error
else:
f = registration.ThinPlateSpline()
bend_c = 0
rot_c = 0
scale_c = 0
max_error = None
# else:
# f = registration.ThinPlateSpline()
# bend_c = 0
# rot_c = 0
#
if old_xyz.any() and old_xyz.shape != (4,4):
tfm_xyz = f.transform_points(old_xyz)
handles.append(Globals.env.plot3(tfm_xyz,5, np.array([(0,1,0) for _ in xrange(tfm_xyz.shape[0])])))
#f = registration.ThinPlateSpline() XXX XXX
if new_xyz.any() and new_xyz.shape != (4,4):
handles.extend(plotting_openrave.draw_grid(Globals.env, f.transform_points, old_xyz.min(axis=0), old_xyz.max(axis=0), xres = .1, yres = .1, zres = .04))
if args.ask:
# if new_xyz.any() and new_xyz.shape != (4,4):
# import visualize
# visualize.plot_mesh_points(f.transform_points, old_xyz, new_xyz)
#lines = plotting_openrave.gen_grid(f.transform_points, np.array([0,-1,0]), np.array([1,1,1]))
#plotting_openrave.plot_lines(lines)
if not yes_or_no.yes_or_no("Continue?"):
continue
miniseg_starts, miniseg_ends = split_trajectory_by_gripper(seg_info)
success = True
print colorize.colorize("mini segments:", "red"), miniseg_starts, miniseg_ends
# Assuming only lgrab or rgrab
use_needle = None
for lr in 'lr':
if "%s_grab"%lr in seg_info['extra_information']:
if "needle_tip_transform" in seg_info['key_points']:
demo_ntfm = np.array(seg_info['key_points']['needle_tip_transform'])
ntfm = np.array(new_keypoints['needle_tip_transform'])
use_needle = lr
elif Globals.rel_ntfm is not None:
T_w_g = Globals.robot.GetLink("%s_gripper_tool_frame"%lr).GetTransform()
T_g_n = Globals.rel_ntfm
ntfm = T_w_g.dot(T_g_n)
T_w_g_demo = Globals.demo_robot.GetLink("%s_gripper_tool_frame"%lr).GetTransform()
T_g_n_demo = Globals.demo_rel_ntfm
demo_ntfm = T_w_g_demo.dot(T_g_n_demo)
use_needle = lr
if use_needle is not None:
setup_needle_grabs(use_needle, seg_info["joint_states"]["name"], seg_info["joint_states"]["look_position"], demo_ntfm, ntfm)
if args.logging:
with open(filename,'a') as fh:
fh.write("- seg_name: %s\n"%seg_name)
fh.write(" tps_info: \n")
if max_error is not None:
res_cost, bend_cost, tot_cost = f.fitting_cost(new_xyz, bend_c)
fh.write(" res_cost: %f\n bend_cost: %f\n tot_cost: %f\n"%(res_cost, bend_cost, tot_cost))
fh.write(" max_tps_error: %f\n"%max_error)
else:
fh.write(" res_cost: -1\n bend_cost: -1\n tot_cost: -1\n max_tps_error: -1\n")
fh.write(" trajopt_info: \n")
for (i_miniseg, (i_start, i_end)) in enumerate(zip(miniseg_starts, miniseg_ends)):
if args.execution: Globals.pr2.update_rave()
# Changing the trajectory according to the end-effector
full_traj = np.c_[seg_info["leftarm"][i_start:i_end+1], seg_info["rightarm"][i_start:i_end+1]]
full_traj = mu.remove_duplicate_rows(full_traj)
_, ds_traj = resampling.adaptive_resample(full_traj, tol=.01, max_change=.1) # about 2.5 degrees, 10 degrees
Globals.robot.SetActiveDOFs(np.r_[Globals.robot.GetManipulator("leftarm").GetArmIndices(), Globals.robot.GetManipulator("rightarm").GetArmIndices()])
Globals.demo_robot.SetActiveDOFs(np.r_[Globals.robot.GetManipulator("leftarm").GetArmIndices(), Globals.robot.GetManipulator("rightarm").GetArmIndices()])
Globals.demo_robot.SetDOFValues(Globals.robot.GetDOFValues())
################################
redprint("Generating joint trajectory for segment %s, part %i"%(seg_name, i_miniseg))
bodypart2traj = {}
arms_used = ""
if args.logging:
with open(filename, 'a') as fh:
fh.write(" mini_seg%i: \n"%i_miniseg)
for lr in 'lr':
# if "%s_grab"%lr in seg_info['extra_information']:
# if "needle_tip_transform" in seg_info['key_points']:
# demo_ntfm = np.array(seg_info['key_points']['needle_tip_transform'])
# ntfm = np.array(new_keypoints['needle_tip_transform'])
# use_needle = True
# elif Globals.rel_ntfm is not None:
# T_w_g = Globals.robot.GetLink("%s_gripper_tool_frame"%lr).GetTransform()
# T_g_n = Globals.rel_ntfm
# ntfm = T_w_g.dot(T_g_n)
# T_w_g_demo = Globals.demo_robot.GetLink("%s_gripper_tool_frame"%lr).GetTransform()
# T_g_n_demo = Globals.demo_rel_ntfm
# demo_ntfm = T_w_g_demo.dot(T_g_n_demo)
# use_needle = True
# else:
# use_needle = False
# else:
# use_needle = False
if use_needle == lr:
Globals.sponge.Enable(False)
arm_traj = {'l':ds_traj[:,:7], 'r':ds_traj[:,7:]}[lr]
old_ee_traj = setup_needle_traj (lr, arm_traj)
link = Globals.needle_tip.GetLinks()[0]
redprint("Using needle for trajectory execution...")
else:
arm = {"l":"leftarm", "r":"rightarm"}[lr]
Globals.demo_robot.SetActiveDOFs(Globals.robot.GetManipulator(arm).GetArmIndices())
if seg_name == "8_knot_tie_next0_seg00":
Globals.sponge.Enable(False)
else:
Globals.sponge.Enable(True)
ee_link_name = "%s_gripper_tool_frame"%lr
link = Globals.robot.GetLink(ee_link_name)
#old_ee_traj = np.asarray(seg_info[ee_link_name]["hmat"])
demo_link = Globals.demo_robot.GetLink(ee_link_name)
old_ee_traj = []
arm_traj = {'l':ds_traj[:,:7], 'r':ds_traj[:,7:]}[lr]
for row in arm_traj:
Globals.demo_robot.SetActiveDOFValues(row)
old_ee_traj.append(demo_link.GetTransform())
############ CAN YOU PLOT THIS OLD_EE_TRAJ?
old_ee_traj = np.asarray(old_ee_traj)
old_joint_traj = {'l':ds_traj[:,:7], 'r':ds_traj[:,7:]}[lr]
if lr == 'l':#arm_moved(old_joint_traj):
manip_name = {"l":"leftarm", "r":"rightarm"}[lr]
new_ee_traj = f.transform_hmats(old_ee_traj)
#new_ee_traj = old_ee_traj
#################### CAN YOU PLOT THIS 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)))
#old_poses = [conv.hmat_to_pose(hmat) for hmat in old_ee_traj]
#print new_ee_traj
#new_poses = [conv.hmat_to_pose(hmat) for hmat in new_ee_traj]
update_markers(new_ee_traj, old_ee_traj)
#thc.run()
if args.execution: Globals.pr2.update_rave()
new_joint_traj, costs, cnts, pos_err = plan_follow_traj(Globals.robot, manip_name,
link, new_ee_traj, old_joint_traj, True)
new_joint_traj = unwrap_joint_traj (lr, new_joint_traj)
# (robot, manip_name, ee_link, new_hmats, old_traj):
part_name = {"l":"larm", "r":"rarm"}[lr]
bodypart2traj[part_name] = new_joint_traj
arms_used += lr
if args.logging:
with open(filename, 'a') as fh:
fh.write(" - %s: \n"%lr)
fh.write(" costs: \n")
tot_cost = 0
for c_name, c_val in costs:
fh.write(" - %s: %f\n"%(c_name, c_val))
tot_cost += c_val
fh.write(" - tot_cost: %f\n"%tot_cost)
fh.write(" constraints: \n")
for cnt in cnts:
fh.write(" - %s\n"%str(cnt))
fh.write(" max_pos_error: %f\n"%pos_err)
################################
redprint("Executing joint trajectory for segment %s, part %i using arms '%s'"%(seg_name, i_miniseg, arms_used))
for lr in 'lr':
set_gripper_maybesim(lr, binarize_gripper(seg_info["%s_gripper_joint"%lr][i_start]))
#trajoptpy.GetViewer(Globals.env).Idle()
if len(bodypart2traj) > 0:
exec_traj_maybesim(bodypart2traj, speed_factor=1)
# TODO measure failure condtions
if not success:
break
Globals.robot.ReleaseAllGrabbed()
Globals.demo_robot.ReleaseAllGrabbed()
redprint("Segment %s result: %s"%(seg_name, success))
if args.fake_data_segment: break