def joint_fit_tps_follow_finger_pts_trajs(robot, manip_name, flr2finger_link_names, flr2finger_rel_pts, flr2old_finger_pts_trajs, old_traj, f, closing_pts=None, no_collision_cost_first=False, use_collision_cost=True, start_fixed=False, joint_vel_limits=None, alpha=settings.ALPHA, beta_pos=settings.BETA_POS, gamma=settings.GAMMA): orig_dof_inds = robot.GetActiveDOFIndices() orig_dof_vals = robot.GetDOFValues() n_steps = old_traj.shape[0] dof_inds = sim_util.dof_inds_from_name(robot, manip_name) assert old_traj.shape[1] == len(dof_inds) for flr2old_finger_pts_traj in flr2old_finger_pts_trajs: for old_finger_pts_traj in flr2old_finger_pts_traj.values(): assert len(old_finger_pts_traj) == n_steps assert len(flr2finger_link_names) == len(flr2old_finger_pts_trajs) # expand these (n, d) = f.x_na.shape bend_coefs = np.ones(d) * f.bend_coef if np.isscalar( f.bend_coef) else f.bend_coef rot_coefs = np.ones(d) * f.rot_coef if np.isscalar( f.rot_coef) else f.rot_coef if f.wt_n is None: wt_n = np.ones(n) else: wt_n = f.wt_n if wt_n.ndim == 1: wt_n = wt_n[:, None] if wt_n.shape[1] == 1: wt_n = np.tile(wt_n, (1, d)) if no_collision_cost_first: init_traj, _, (N, init_z), _, _ = joint_fit_tps_follow_finger_pts_trajs( robot, manip_name, flr2finger_link_names, flr2finger_rel_pts, flr2old_finger_pts_trajs, old_traj, f, closing_pts=closing_pts, no_collision_cost_first=False, use_collision_cost=False, start_fixed=start_fixed, joint_vel_limits=joint_vel_limits, alpha=alpha, beta_pos=beta_pos, gamma=gamma) else: init_traj = old_traj.copy() N = f.N init_z = f.z if start_fixed: init_traj = np.r_[robot.GetDOFValues(dof_inds)[None, :], init_traj[1:]] sim_util.unwrap_in_place(init_traj, dof_inds) init_traj += robot.GetDOFValues(dof_inds) - init_traj[0, :] request = { "basic_info": { "n_steps": n_steps, "m_ext": n, "n_ext": d, "manip": manip_name, "start_fixed": start_fixed }, "costs": [{ "type": "joint_vel", "params": { "coeffs": [gamma / (n_steps - 1)] } }, { "type": "tps", "name": "tps", "params": { "x_na": [row.tolist() for row in f.x_na], "y_ng": [row.tolist() for row in f.y_ng], "bend_coefs": bend_coefs.tolist(), "rot_coefs": rot_coefs.tolist(), "wt_n": [row.tolist() for row in wt_n], "N": [row.tolist() for row in N], "alpha": alpha, } }], "constraints": [], "init_info": { "type": "given_traj", "data": [x.tolist() for x in init_traj], "data_ext": [row.tolist() for row in init_z] } } if use_collision_cost: request["costs"].append({ "type": "collision", "params": { "continuous": True, "coeffs": [ 1000 ], # penalty coefficients. list of length one is automatically expanded to a list of length n_timesteps "dist_pen": [ 0.025 ] # robot-obstacle distance that penalty kicks in. expands to length n_timesteps } }) if joint_vel_limits is not None: request["constraints"].append({ "type": "joint_vel_limits", "params": { "vals": joint_vel_limits, "first_step": 0, "last_step": n_steps - 1 } }) if closing_pts is not None: request["costs"].append({ "type": "tps_jac_orth", "params": { "tps_cost_name": "tps", "pts": closing_pts.tolist(), "coeffs": [10.0] * len(closing_pts), } }) for (flr2finger_link_name, flr2old_finger_pts_traj) in zip(flr2finger_link_names, flr2old_finger_pts_trajs): for finger_lr, finger_link_name in flr2finger_link_name.items(): finger_rel_pts = flr2finger_rel_pts[finger_lr] old_finger_pts_traj = flr2old_finger_pts_traj[finger_lr] for (i_step, old_finger_pts) in enumerate(old_finger_pts_traj): if start_fixed and i_step == 0: continue request["costs"].append({ "type": "tps_rel_pts", "params": { "tps_cost_name": "tps", "src_xyzs": old_finger_pts.tolist(), "rel_xyzs": finger_rel_pts.tolist(), "link": finger_link_name, "timestep": i_step, "pos_coeffs": [np.sqrt(beta_pos / n_steps)] * 4, } }) s = json.dumps(request) with openravepy.RobotStateSaver(robot): with util.suppress_stdout(): prob = trajoptpy.ConstructProblem(s, robot.GetEnv( )) # create object that stores optimization problem result = trajoptpy.OptimizeProblem(prob) # do optimization traj = result.GetTraj() f.z = result.GetExt() theta = N.dot(f.z) f.trans_g = theta[0, :] f.lin_ag = theta[1:d + 1, :] f.w_ng = theta[d + 1:] tps_rel_pts_costs = np.sum([ cost_val for (cost_type, cost_val) in result.GetCosts() if cost_type == "tps_rel_pts" ]) tps_rel_pts_err = [] with openravepy.RobotStateSaver(robot): for (flr2finger_link_name, flr2old_finger_pts_traj) in zip(flr2finger_link_names, flr2old_finger_pts_trajs): for finger_lr, finger_link_name in flr2finger_link_name.items(): finger_link = robot.GetLink(finger_link_name) finger_rel_pts = flr2finger_rel_pts[finger_lr] old_finger_pts_traj = flr2old_finger_pts_traj[finger_lr] for (i_step, old_finger_pts) in enumerate(old_finger_pts_traj): if start_fixed and i_step == 0: continue robot.SetDOFValues(traj[i_step], dof_inds) new_hmat = finger_link.GetTransform() tps_rel_pts_err.append( f.transform_points(old_finger_pts) - (new_hmat[:3, 3][None, :] + finger_rel_pts.dot(new_hmat[:3, :3].T))) tps_rel_pts_err = np.concatenate(tps_rel_pts_err, axis=0) tps_rel_pts_costs2 = (beta_pos / n_steps) * np.square( tps_rel_pts_err).sum() # TODO don't square n_steps tps_cost = np.sum([ cost_val for (cost_type, cost_val) in result.GetCosts() if cost_type == "tps" ]) tps_cost2 = alpha * f.get_objective().sum() matching_err = f.transform_points(f.x_na) - f.y_ng joint_vel_cost = np.sum([ cost_val for (cost_type, cost_val) in result.GetCosts() if cost_type == "joint_vel" ]) joint_vel_err = np.diff(traj, axis=0) joint_vel_cost2 = (gamma / (n_steps - 1)) * np.square(joint_vel_err).sum() sim_util.unwrap_in_place(traj, dof_inds) joint_vel_err = np.diff(traj, axis=0) collision_costs = [ cost_val for (cost_type, cost_val) in result.GetCosts() if "collision" in cost_type ] if len(collision_costs) > 0: collision_err = np.asarray(collision_costs) collision_costs = np.sum(collision_costs) tps_jac_orth_cost = [ cost_val for (cost_type, cost_val) in result.GetCosts() if "tps_jac_orth" in cost_type ] if len(tps_jac_orth_cost) > 0: tps_jac_orth_cost = np.sum(tps_jac_orth_cost) f_jacs = f.compute_jacobian(closing_pts) tps_jac_orth_err = [] for jac in f_jacs: tps_jac_orth_err.extend((jac.dot(jac.T) - np.eye(3)).flatten()) tps_jac_orth_err = np.asarray(tps_jac_orth_err) tps_jac_orth_cost2 = np.square(10.0 * tps_jac_orth_err).sum() obj_value = np.sum( [cost_val for (cost_type, cost_val) in result.GetCosts()]) print "{:>15} | {:>10} | {:>10}".format("", "trajopt", "computed") print "{:>15} | {:>10}".format("COSTS", "-" * 23) print "{:>15} | {:>10,.4} | {:>10,.4}".format("joint_vel", joint_vel_cost, joint_vel_cost2) print "{:>15} | {:>10,.4} | {:>10,.4}".format("tps", tps_cost, tps_cost2) if np.isscalar(collision_costs): print "{:>15} | {:>10,.4} | {:>10}".format("collision(s)", collision_costs, "-") print "{:>15} | {:>10,.4} | {:>10,.4}".format("tps_rel_pts(s)", tps_rel_pts_costs, tps_rel_pts_costs2) if np.isscalar(tps_jac_orth_cost): print "{:>15} | {:>10,.4} | {:>10,.4}".format("tps_jac_orth", tps_jac_orth_cost, tps_jac_orth_cost2) print "{:>15} | {:>10,.4} | {:>10}".format("total_obj", obj_value, "-") print "" print "{:>15} | {:>10} | {:>10}".format("", "abs min", "abs max") print "{:>15} | {:>10}".format("ERRORS", "-" * 23) print "{:>15} | {:>10,.4} | {:>10,.4}".format( "joint_vel (deg)", np.rad2deg(np.abs(joint_vel_err).min()), np.rad2deg(np.abs(joint_vel_err).max())) print "{:>15} | {:>10,.4} | {:>10,.4}".format("tps (matching)", np.abs(matching_err).min(), np.abs(matching_err).max()) if np.isscalar(collision_costs): print "{:>15} | {:>10,.4} | {:>10,.4}".format( "collision(s)", np.abs(-collision_err).min(), np.abs(-collision_err).max()) print "{:>15} | {:>10,.4} | {:>10,.4}".format( "tps_rel_pts(s)", np.abs(tps_rel_pts_err).min(), np.abs(tps_rel_pts_err).max()) if np.isscalar(tps_jac_orth_cost): print "{:>15} | {:>10,.4} | {:>10,.4}".format( "tps_jac_orth", np.abs(tps_jac_orth_err).min(), np.abs(tps_jac_orth_err).max()) print "" # make sure this function doesn't change state of the robot assert not np.any(orig_dof_inds - robot.GetActiveDOFIndices()) assert not np.any(orig_dof_vals - robot.GetDOFValues()) return traj, obj_value, tps_rel_pts_costs, tps_cost
def position_base_request(shared, cost_fn_xsg, starting_config, goal_config, n_steps, init_position=[0, 0]): # init_position - initial position of robot's base # seems like nsteps needs to be defined as 1 when the base is the only active DOF initialized # Initialize robot position T = shared.robot.GetTransform() T[:, 3][:2] = init_position shared.robot.SetTransform(T) gripper_before(shared) armids = list(shared.manip.GetArmIndices()) #get arm indices shared.robot.SetDOFValues(starting_config, armids) links = shared.robot.GetLinks() link_idx = links.index(shared.robot.GetLink('r_gripper_palm_link')) linkstrans = shared.robot.GetLinkTransformations() xyz_target = list(linkstrans[link_idx][:3][:, 3]) #get xyz of specific link quat_target = list( Quaternion(matrix=linkstrans[link_idx])) #get quat of specific link if shared.use_base: shared.robot.SetActiveDOFs( np.r_[shared.manip.GetArmIndices()], openravepy.DOFAffine.X + openravepy.DOFAffine.Y) request = { "basic_info": { "n_steps": n_steps, "start_fixed": True # DOF values at first timestep are fixed based on current robot state }, "costs": [ { "type": "joint_vel", "params": { "coeffs": [1] } }, { "type": "collision", "params": { "coeffs": [ 1 ], # penalty coefficients. list of length one is automatically expanded to a list of length n_timesteps "dist_pen": [ 0.025 ] # robot-obstacle distance that penalty kicks in. expands to length n_timesteps } }, ], "constraints": [], "init_info": {} } ##Initialize trajectory as stationary##: request["init_info"]["type"] = "stationary" request["basic_info"][ "manip"] = "active" if shared.use_base else "rightarm" for i in range(n_steps): request["constraints"].append({ "type": "pose", "name": "pose" + str(i), "params": { "pos_coeffs": [6, 6, 6], #[6,6,6], "rot_coeffs": [2, 2, 2], #[2,2,2], "xyz": list(xyz_target), "wxyz": list(quat_target), "link": "r_gripper_palm_link", "timestep": i, } }) s = json.dumps(request) prob = trajoptpy.ConstructProblem(s, shared.env) cost_fn = lambda x: cost_fn_xsg(x, starting_config, goal_config) with openravepy.RobotStateSaver(shared.robot): with util.suppress_stdout(): n_dof = 7 if shared.use_base: n_dof = 9 prob.AddCost(cost_fn, [(n_steps - 1, j) for j in range(n_dof)], "express%i" % (n_steps - 1)) result = trajoptpy.OptimizeProblem(prob) traj = result.GetTraj() dof_inds = sim_util.dof_inds_from_name(shared.robot, shared.manip_name) sim_util.unwrap_in_place(traj, dof_inds) return traj, result
def plan_follow_trajs(robot, manip_name, ee_link_names, ee_trajs, old_traj, no_collision_cost_first=False, use_collision_cost=True, start_fixed=False, joint_vel_limits=None, beta_pos=settings.BETA_POS, beta_rot=settings.BETA_ROT, gamma=settings.GAMMA): orig_dof_inds = robot.GetActiveDOFIndices() orig_dof_vals = robot.GetDOFValues() n_steps = len(ee_trajs[0]) dof_inds = sim_util.dof_inds_from_name(robot, manip_name) assert old_traj.shape[0] == n_steps assert old_traj.shape[1] == len(dof_inds) assert len(ee_link_names) == len(ee_trajs) if no_collision_cost_first: init_traj, _, _ = plan_follow_trajs(robot, manip_name, ee_link_names, ee_trajs, old_traj, no_collision_cost_first=False, use_collision_cost=False, start_fixed=start_fixed, joint_vel_limits=joint_vel_limits, beta_pos=beta_pos, beta_rot=beta_rot, gamma=gamma) else: init_traj = old_traj.copy() if start_fixed: init_traj = np.r_[robot.GetDOFValues(dof_inds)[None, :], init_traj[1:]] sim_util.unwrap_in_place(init_traj, dof_inds) init_traj += robot.GetDOFValues(dof_inds) - init_traj[0, :] request = { "basic_info": { "n_steps": n_steps, "manip": manip_name, "start_fixed": start_fixed }, "costs": [ { "type": "joint_vel", "params": { "coeffs": [gamma / (n_steps - 1)] } }, ], "constraints": [], "init_info": { "type": "given_traj", "data": [x.tolist() for x in init_traj] } } if use_collision_cost: request["costs"].append({ "type": "collision", "params": { "continuous": True, "coeffs": [ 1000 ], # penalty coefficients. list of length one is automatically expanded to a list of length n_timesteps "dist_pen": [ 0.025 ] # robot-obstacle distance that penalty kicks in. expands to length n_timesteps } }) if joint_vel_limits is not None: request["constraints"].append({ "type": "joint_vel_limits", "params": { "vals": joint_vel_limits, "first_step": 0, "last_step": n_steps - 1 } }) for (ee_link_name, ee_traj) in zip(ee_link_names, ee_trajs): poses = [openravepy.poseFromMatrix(hmat) for hmat in ee_traj] for (i_step, pose) in enumerate(poses): if start_fixed and i_step == 0: continue request["costs"].append({ "type": "pose", "params": { "xyz": pose[4:7].tolist(), "wxyz": pose[0:4].tolist(), "link": ee_link_name, "timestep": i_step, "pos_coeffs": [np.sqrt(beta_pos / n_steps)] * 3, "rot_coeffs": [np.sqrt(beta_rot / n_steps)] * 3 } }) s = json.dumps(request) with openravepy.RobotStateSaver(robot): orig_dof_vals with util.suppress_stdout(): prob = trajoptpy.ConstructProblem(s, robot.GetEnv( )) # create object that stores optimization problem result = trajoptpy.OptimizeProblem(prob) # do optimization traj = result.GetTraj() pose_costs = np.sum([ cost_val for (cost_type, cost_val) in result.GetCosts() if cost_type == "pose" ]) pose_err = [] with openravepy.RobotStateSaver(robot): for (ee_link_name, ee_traj) in zip(ee_link_names, ee_trajs): ee_link = robot.GetLink(ee_link_name) for (i_step, hmat) in enumerate(ee_traj): if start_fixed and i_step == 0: continue robot.SetDOFValues(traj[i_step], dof_inds) new_hmat = ee_link.GetTransform() pose_err.append( openravepy.poseFromMatrix( mu.invertHmat(hmat).dot(new_hmat))) pose_err = np.asarray(pose_err) pose_costs2 = (beta_rot / n_steps) * np.square(pose_err[:, 1:4]).sum() + ( beta_pos / n_steps) * np.square(pose_err[:, 4:7]).sum() joint_vel_cost = np.sum([ cost_val for (cost_type, cost_val) in result.GetCosts() if cost_type == "joint_vel" ]) joint_vel_err = np.diff(traj, axis=0) joint_vel_cost2 = (gamma / (n_steps - 1)) * np.square(joint_vel_err).sum() sim_util.unwrap_in_place(traj, dof_inds) joint_vel_err = np.diff(traj, axis=0) collision_costs = [ cost_val for (cost_type, cost_val) in result.GetCosts() if "collision" in cost_type ] if len(collision_costs) > 0: collision_err = np.asarray(collision_costs) collision_costs = np.sum(collision_costs) obj_value = np.sum( [cost_val for (cost_type, cost_val) in result.GetCosts()]) print "{:>15} | {:>10} | {:>10}".format("", "trajopt", "computed") print "{:>15} | {:>10}".format("COSTS", "-" * 23) print "{:>15} | {:>10,.4} | {:>10,.4}".format("joint_vel", joint_vel_cost, joint_vel_cost2) if np.isscalar(collision_costs): print "{:>15} | {:>10,.4} | {:>10}".format("collision(s)", collision_costs, "-") print "{:>15} | {:>10,.4} | {:>10,.4}".format("pose(s)", pose_costs, pose_costs2) print "{:>15} | {:>10,.4} | {:>10}".format("total_obj", obj_value, "-") print "" print "{:>15} | {:>10} | {:>10}".format("", "abs min", "abs max") print "{:>15} | {:>10}".format("ERRORS", "-" * 23) print "{:>15} | {:>10,.4} | {:>10,.4}".format( "joint_vel (deg)", np.rad2deg(np.abs(joint_vel_err).min()), np.rad2deg(np.abs(joint_vel_err).max())) if np.isscalar(collision_costs): print "{:>15} | {:>10,.4} | {:>10,.4}".format( "collision(s)", np.abs(-collision_err).min(), np.abs(-collision_err).max()) print "{:>15} | {:>10,.4} | {:>10,.4}".format( "rot pose(s)", np.abs(pose_err[:, 1:4]).min(), np.abs(pose_err[:, 1:4]).max()) print "{:>15} | {:>10,.4} | {:>10,.4}".format( "trans pose(s)", np.abs(pose_err[:, 4:7]).min(), np.abs(pose_err[:, 4:7]).max()) print "" # make sure this function doesn't change state of the robot assert not np.any(orig_dof_inds - robot.GetActiveDOFIndices()) assert not np.any(orig_dof_vals - robot.GetDOFValues()) return traj, obj_value, pose_costs
def pre_motion_traj(premotion_starting_config, attempt_starting_config, \ joint_vel_coeff=20, collision_coeff=20, init_traj_input=None): #global n_steps, Final_pose, manip, ideal_config, ideal_config_vanilla n_steps = 30 robot.SetDOFValues(attempt_starting_config, manip.GetArmIndices()) armids = list(manip.GetArmIndices()) #get arm indices links = robot.GetLinks() link_idx = links.index(robot.GetLink('r_gripper_palm_link')) linkstrans = robot.GetLinkTransformations() xyz_target1 = list(linkstrans[link_idx][:3][:,3]) #get xyz of specific link quat_target1 = list(Quaternion(matrix=linkstrans[link_idx])) robot.SetDOFValues(premotion_starting_config, manip.GetArmIndices()) init_traj = interpolate(premotion_starting_config, attempt_starting_config, n_steps) #filling in request dictionary request = { "basic_info" : { "n_steps" : n_steps, "manip" : "rightarm", "start_fixed" : True }, "costs" : [ { "type" : "joint_vel", "params": {"coeffs" : [joint_vel_coeff]} }, { "type" : "collision", "params" : { "coeffs" : [collision_coeff], # penalty coefficients. list of length one is automatically expanded to a list of length n_timesteps "dist_pen" : [0.025] # robot-obstacle distance that penalty kicks in. expands to length n_timesteps } }, ], "constraints" : [{"type": "pose", "params" : { "xyz" : xyz_target1, "wxyz" : quat_target1, "link": "r_gripper_palm_link", "timestep" : n_steps-1, "rot_coeffs" : [2,2,2], "pos_coeffs" : [5,5,5] }}, { "type" : "joint", # joint-space target "params" : {"vals" : attempt_starting_config.tolist() } # length of vals = # dofs of manip }], "init_info" : { #"type": "given_traj", #"data": init_traj.tolist() #"type": "straight_line", #"endpoint": attempt_starting_config.tolist() #"type": "stationary", } } if init_traj_input is None: request["init_info"]["type"] = "straight_line" request["init_info"]["endpoint"] = attempt_starting_config.tolist() else: request["init_info"]["type"] = "given_traj" request["init_info"]["data"] = init_traj_input.tolist() s = json.dumps(request) with openravepy.RobotStateSaver(robot): with util.suppress_stdout(): prob = trajoptpy.ConstructProblem(s, robot.GetEnv()) # create object that stores optimization problem result = trajoptpy.OptimizeProblem(prob) # do optimization traj = result.GetTraj() dof_inds = sim_util.dof_inds_from_name(robot, manip_name) sim_util.unwrap_in_place(traj, dof_inds) return traj, result
def joint_fit_tps_follow_finger_pts_trajs(robot, manip_name, flr2finger_link_names, flr2finger_rel_pts, flr2old_finger_pts_trajs, old_traj, f, closing_pts=None, no_collision_cost_first=False, use_collision_cost=True, start_fixed=False, joint_vel_limits=None, alpha=settings.ALPHA, beta_pos=settings.BETA_POS, gamma=settings.GAMMA): orig_dof_inds = robot.GetActiveDOFIndices() orig_dof_vals = robot.GetDOFValues() n_steps = old_traj.shape[0] dof_inds = sim_util.dof_inds_from_name(robot, manip_name) assert old_traj.shape[1] == len(dof_inds) for flr2old_finger_pts_traj in flr2old_finger_pts_trajs: for old_finger_pts_traj in flr2old_finger_pts_traj.values(): assert len(old_finger_pts_traj)== n_steps assert len(flr2finger_link_names) == len(flr2old_finger_pts_trajs) # expand these (n,d) = f.x_na.shape bend_coefs = np.ones(d) * f.bend_coef if np.isscalar(f.bend_coef) else f.bend_coef rot_coefs = np.ones(d) * f.rot_coef if np.isscalar(f.rot_coef) else f.rot_coef if f.wt_n is None: wt_n = np.ones(n) else: wt_n = f.wt_n if wt_n.ndim == 1: wt_n = wt_n[:,None] if wt_n.shape[1] == 1: wt_n = np.tile(wt_n, (1,d)) if no_collision_cost_first: init_traj, _, (N, init_z) , _, _ = joint_fit_tps_follow_finger_pts_trajs(robot, manip_name, flr2finger_link_names, flr2finger_rel_pts, flr2old_finger_pts_trajs, old_traj, f, closing_pts=closing_pts, no_collision_cost_first=False, use_collision_cost=False, start_fixed=start_fixed, joint_vel_limits=joint_vel_limits, alpha=alpha, beta_pos=beta_pos, gamma=gamma) else: init_traj = old_traj.copy() N = f.N init_z = f.z if start_fixed: init_traj = np.r_[robot.GetDOFValues(dof_inds)[None,:], init_traj[1:]] sim_util.unwrap_in_place(init_traj, dof_inds) init_traj += robot.GetDOFValues(dof_inds) - init_traj[0,:] request = { "basic_info" : { "n_steps" : n_steps, "m_ext" : n, "n_ext" : d, "manip" : manip_name, "start_fixed" : start_fixed }, "costs" : [ { "type" : "joint_vel", "params": {"coeffs" : [gamma/(n_steps-1)]} }, { "type" : "tps", "name" : "tps", "params" : {"x_na" : [row.tolist() for row in f.x_na], "y_ng" : [row.tolist() for row in f.y_ng], "bend_coefs" : bend_coefs.tolist(), "rot_coefs" : rot_coefs.tolist(), "wt_n" : [row.tolist() for row in wt_n], "N" : [row.tolist() for row in N], "alpha" : alpha, } } ], "constraints" : [ ], "init_info" : { "type":"given_traj", "data":[x.tolist() for x in init_traj], "data_ext":[row.tolist() for row in init_z] } } if use_collision_cost: request["costs"].append( { "type" : "collision", "params" : { "continuous" : True, "coeffs" : [1000], # penalty coefficients. list of length one is automatically expanded to a list of length n_timesteps "dist_pen" : [0.025] # robot-obstacle distance that penalty kicks in. expands to length n_timesteps } }) if joint_vel_limits is not None: request["constraints"].append( { "type" : "joint_vel_limits", "params": {"vals" : joint_vel_limits, "first_step" : 0, "last_step" : n_steps-1 } }) if closing_pts is not None: request["costs"].append( { "type":"tps_jac_orth", "params": { "tps_cost_name":"tps", "pts":closing_pts.tolist(), "coeffs":[10.0]*len(closing_pts), } }) for (flr2finger_link_name, flr2old_finger_pts_traj) in zip(flr2finger_link_names, flr2old_finger_pts_trajs): for finger_lr, finger_link_name in flr2finger_link_name.items(): finger_rel_pts = flr2finger_rel_pts[finger_lr] old_finger_pts_traj = flr2old_finger_pts_traj[finger_lr] for (i_step, old_finger_pts) in enumerate(old_finger_pts_traj): if start_fixed and i_step == 0: continue request["costs"].append( {"type":"tps_rel_pts", "params":{ "tps_cost_name":"tps", "src_xyzs":old_finger_pts.tolist(), "rel_xyzs":finger_rel_pts.tolist(), "link":finger_link_name, "timestep":i_step, "pos_coeffs":[np.sqrt(beta_pos/n_steps)]*4, } }) s = json.dumps(request) with openravepy.RobotStateSaver(robot): with util.suppress_stdout(): prob = trajoptpy.ConstructProblem(s, robot.GetEnv()) # create object that stores optimization problem result = trajoptpy.OptimizeProblem(prob) # do optimization traj = result.GetTraj() f.z = result.GetExt() theta = N.dot(f.z) f.trans_g = theta[0,:] f.lin_ag = theta[1:d+1,:] f.w_ng = theta[d+1:] tps_rel_pts_costs = np.sum([cost_val for (cost_type, cost_val) in result.GetCosts() if cost_type == "tps_rel_pts"]) tps_rel_pts_err = [] with openravepy.RobotStateSaver(robot): for (flr2finger_link_name, flr2old_finger_pts_traj) in zip(flr2finger_link_names, flr2old_finger_pts_trajs): for finger_lr, finger_link_name in flr2finger_link_name.items(): finger_link = robot.GetLink(finger_link_name) finger_rel_pts = flr2finger_rel_pts[finger_lr] old_finger_pts_traj = flr2old_finger_pts_traj[finger_lr] for (i_step, old_finger_pts) in enumerate(old_finger_pts_traj): if start_fixed and i_step == 0: continue robot.SetDOFValues(traj[i_step], dof_inds) new_hmat = finger_link.GetTransform() tps_rel_pts_err.append(f.transform_points(old_finger_pts) - (new_hmat[:3,3][None,:] + finger_rel_pts.dot(new_hmat[:3,:3].T))) tps_rel_pts_err = np.concatenate(tps_rel_pts_err, axis=0) tps_rel_pts_costs2 = (beta_pos/n_steps) * np.square(tps_rel_pts_err).sum() # TODO don't square n_steps tps_cost = np.sum([cost_val for (cost_type, cost_val) in result.GetCosts() if cost_type == "tps"]) tps_cost2 = alpha * f.get_objective().sum() matching_err = f.transform_points(f.x_na) - f.y_ng joint_vel_cost = np.sum([cost_val for (cost_type, cost_val) in result.GetCosts() if cost_type == "joint_vel"]) joint_vel_err = np.diff(traj, axis=0) joint_vel_cost2 = (gamma/(n_steps-1)) * np.square(joint_vel_err).sum() sim_util.unwrap_in_place(traj, dof_inds) joint_vel_err = np.diff(traj, axis=0) collision_costs = [cost_val for (cost_type, cost_val) in result.GetCosts() if "collision" in cost_type] if len(collision_costs) > 0: collision_err = np.asarray(collision_costs) collision_costs = np.sum(collision_costs) tps_jac_orth_cost = [cost_val for (cost_type, cost_val) in result.GetCosts() if "tps_jac_orth" in cost_type] if len(tps_jac_orth_cost) > 0: tps_jac_orth_cost = np.sum(tps_jac_orth_cost) f_jacs = f.compute_jacobian(closing_pts) tps_jac_orth_err = [] for jac in f_jacs: tps_jac_orth_err.extend((jac.dot(jac.T) - np.eye(3)).flatten()) tps_jac_orth_err = np.asarray(tps_jac_orth_err) tps_jac_orth_cost2 = np.square( 10.0 * tps_jac_orth_err ).sum() obj_value = np.sum([cost_val for (cost_type, cost_val) in result.GetCosts()]) print "{:>15} | {:>10} | {:>10}".format("", "trajopt", "computed") print "{:>15} | {:>10}".format("COSTS", "-"*23) print "{:>15} | {:>10,.4} | {:>10,.4}".format("joint_vel", joint_vel_cost, joint_vel_cost2) print "{:>15} | {:>10,.4} | {:>10,.4}".format("tps", tps_cost, tps_cost2) if np.isscalar(collision_costs): print "{:>15} | {:>10,.4} | {:>10}".format("collision(s)", collision_costs, "-") print "{:>15} | {:>10,.4} | {:>10,.4}".format("tps_rel_pts(s)", tps_rel_pts_costs, tps_rel_pts_costs2) if np.isscalar(tps_jac_orth_cost): print "{:>15} | {:>10,.4} | {:>10,.4}".format("tps_jac_orth", tps_jac_orth_cost, tps_jac_orth_cost2) print "{:>15} | {:>10,.4} | {:>10}".format("total_obj", obj_value, "-") print "" print "{:>15} | {:>10} | {:>10}".format("", "abs min", "abs max") print "{:>15} | {:>10}".format("ERRORS", "-"*23) print "{:>15} | {:>10,.4} | {:>10,.4}".format("joint_vel (deg)", np.rad2deg(np.abs(joint_vel_err).min()), np.rad2deg(np.abs(joint_vel_err).max())) print "{:>15} | {:>10,.4} | {:>10,.4}".format("tps (matching)", np.abs(matching_err).min(), np.abs(matching_err).max()) if np.isscalar(collision_costs): print "{:>15} | {:>10,.4} | {:>10,.4}".format("collision(s)", np.abs(-collision_err).min(), np.abs(-collision_err).max()) print "{:>15} | {:>10,.4} | {:>10,.4}".format("tps_rel_pts(s)", np.abs(tps_rel_pts_err).min(), np.abs(tps_rel_pts_err).max()) if np.isscalar(tps_jac_orth_cost): print "{:>15} | {:>10,.4} | {:>10,.4}".format("tps_jac_orth", np.abs(tps_jac_orth_err).min(), np.abs(tps_jac_orth_err).max()) print "" # make sure this function doesn't change state of the robot assert not np.any(orig_dof_inds - robot.GetActiveDOFIndices()) assert not np.any(orig_dof_vals - robot.GetDOFValues()) return traj, obj_value, tps_rel_pts_costs, tps_cost
def plan_follow_trajs(robot, manip_name, ee_link_names, ee_trajs, old_traj, no_collision_cost_first=False, use_collision_cost=True, start_fixed=False, joint_vel_limits=None, beta_pos=settings.BETA_POS, beta_rot=settings.BETA_ROT, gamma=settings.GAMMA): orig_dof_inds = robot.GetActiveDOFIndices() orig_dof_vals = robot.GetDOFValues() n_steps = len(ee_trajs[0]) dof_inds = sim_util.dof_inds_from_name(robot, manip_name) assert old_traj.shape[0] == n_steps assert old_traj.shape[1] == len(dof_inds) assert len(ee_link_names) == len(ee_trajs) if no_collision_cost_first: init_traj, _, _ = plan_follow_trajs(robot, manip_name, ee_link_names, ee_trajs, old_traj, no_collision_cost_first=False, use_collision_cost=False, start_fixed=start_fixed, joint_vel_limits=joint_vel_limits, beta_pos = beta_pos, beta_rot = beta_rot, gamma = gamma) else: init_traj = old_traj.copy() if start_fixed: init_traj = np.r_[robot.GetDOFValues(dof_inds)[None,:], init_traj[1:]] sim_util.unwrap_in_place(init_traj, dof_inds) init_traj += robot.GetDOFValues(dof_inds) - init_traj[0,:] request = { "basic_info" : { "n_steps" : n_steps, "manip" : manip_name, "start_fixed" : start_fixed }, "costs" : [ { "type" : "joint_vel", "params": {"coeffs" : [gamma/(n_steps-1)]} }, ], "constraints" : [ ], "init_info" : { "type":"given_traj", "data":[x.tolist() for x in init_traj] } } if use_collision_cost: request["costs"].append( { "type" : "collision", "params" : { "continuous" : True, "coeffs" : [1000], # penalty coefficients. list of length one is automatically expanded to a list of length n_timesteps "dist_pen" : [0.025] # robot-obstacle distance that penalty kicks in. expands to length n_timesteps } }) if joint_vel_limits is not None: request["constraints"].append( { "type" : "joint_vel_limits", "params": {"vals" : joint_vel_limits, "first_step" : 0, "last_step" : n_steps-1 } }) for (ee_link_name, ee_traj) in zip(ee_link_names, ee_trajs): poses = [openravepy.poseFromMatrix(hmat) for hmat in ee_traj] for (i_step,pose) in enumerate(poses): if start_fixed and i_step == 0: continue request["costs"].append( {"type":"pose", "params":{ "xyz":pose[4:7].tolist(), "wxyz":pose[0:4].tolist(), "link":ee_link_name, "timestep":i_step, "pos_coeffs":[np.sqrt(beta_pos/n_steps)]*3, "rot_coeffs":[np.sqrt(beta_rot/n_steps)]*3 } }) s = json.dumps(request) with openravepy.RobotStateSaver(robot): orig_dof_vals with util.suppress_stdout(): prob = trajoptpy.ConstructProblem(s, robot.GetEnv()) # create object that stores optimization problem result = trajoptpy.OptimizeProblem(prob) # do optimization traj = result.GetTraj() pose_costs = np.sum([cost_val for (cost_type, cost_val) in result.GetCosts() if cost_type == "pose"]) pose_err = [] with openravepy.RobotStateSaver(robot): for (ee_link_name, ee_traj) in zip(ee_link_names, ee_trajs): ee_link = robot.GetLink(ee_link_name) for (i_step, hmat) in enumerate(ee_traj): if start_fixed and i_step == 0: continue robot.SetDOFValues(traj[i_step], dof_inds) new_hmat = ee_link.GetTransform() pose_err.append(openravepy.poseFromMatrix(mu.invertHmat(hmat).dot(new_hmat))) pose_err = np.asarray(pose_err) pose_costs2 = (beta_rot/n_steps) * np.square(pose_err[:,1:4]).sum() + (beta_pos/n_steps) * np.square(pose_err[:,4:7]).sum() joint_vel_cost = np.sum([cost_val for (cost_type, cost_val) in result.GetCosts() if cost_type == "joint_vel"]) joint_vel_err = np.diff(traj, axis=0) joint_vel_cost2 = (gamma/(n_steps-1)) * np.square(joint_vel_err).sum() sim_util.unwrap_in_place(traj, dof_inds) joint_vel_err = np.diff(traj, axis=0) collision_costs = [cost_val for (cost_type, cost_val) in result.GetCosts() if "collision" in cost_type] if len(collision_costs) > 0: collision_err = np.asarray(collision_costs) collision_costs = np.sum(collision_costs) obj_value = np.sum([cost_val for (cost_type, cost_val) in result.GetCosts()]) print "{:>15} | {:>10} | {:>10}".format("", "trajopt", "computed") print "{:>15} | {:>10}".format("COSTS", "-"*23) print "{:>15} | {:>10,.4} | {:>10,.4}".format("joint_vel", joint_vel_cost, joint_vel_cost2) if np.isscalar(collision_costs): print "{:>15} | {:>10,.4} | {:>10}".format("collision(s)", collision_costs, "-") print "{:>15} | {:>10,.4} | {:>10,.4}".format("pose(s)", pose_costs, pose_costs2) print "{:>15} | {:>10,.4} | {:>10}".format("total_obj", obj_value, "-") print "" print "{:>15} | {:>10} | {:>10}".format("", "abs min", "abs max") print "{:>15} | {:>10}".format("ERRORS", "-"*23) print "{:>15} | {:>10,.4} | {:>10,.4}".format("joint_vel (deg)", np.rad2deg(np.abs(joint_vel_err).min()), np.rad2deg(np.abs(joint_vel_err).max())) if np.isscalar(collision_costs): print "{:>15} | {:>10,.4} | {:>10,.4}".format("collision(s)", np.abs(-collision_err).min(), np.abs(-collision_err).max()) print "{:>15} | {:>10,.4} | {:>10,.4}".format("rot pose(s)", np.abs(pose_err[:,1:4]).min(), np.abs(pose_err[:,1:4]).max()) print "{:>15} | {:>10,.4} | {:>10,.4}".format("trans pose(s)", np.abs(pose_err[:,4:7]).min(), np.abs(pose_err[:,4:7]).max()) print "" # make sure this function doesn't change state of the robot assert not np.any(orig_dof_inds - robot.GetActiveDOFIndices()) assert not np.any(orig_dof_vals - robot.GetDOFValues()) return traj, obj_value, pose_costs
def plan_follow_trajs(robot, manip_name, ee_link_names, ee_trajs, old_traj, no_collision_cost_first=False, use_collision_cost=True, start_fixed=False, joint_vel_limits=None, beta_pos=settings.BETA_POS, beta_rot=settings.BETA_ROT, gamma=settings.GAMMA, pose_weights=1, grasp_cup=False, R=0.02, D=0.035, cup_xyz=None): orig_dof_inds = robot.GetActiveDOFIndices() orig_dof_vals = robot.GetDOFValues() n_steps = len(ee_trajs[0]) dof_inds = sim_util.dof_inds_from_name(robot, manip_name) assert old_traj.shape[0] == n_steps assert old_traj.shape[1] == len(dof_inds) assert len(ee_link_names) == len(ee_trajs) if no_collision_cost_first: init_traj, _, _ = plan_follow_trajs(robot, manip_name, ee_link_names, ee_trajs, old_traj, no_collision_cost_first=False, use_collision_cost=False, start_fixed=start_fixed, joint_vel_limits=joint_vel_limits, beta_pos = beta_pos, beta_rot = beta_rot, gamma = gamma) else: init_traj = old_traj.copy() if start_fixed: init_traj = np.r_[robot.GetDOFValues(dof_inds)[None,:], init_traj[1:]] sim_util.unwrap_in_place(init_traj, dof_inds) init_traj += robot.GetDOFValues(dof_inds) - init_traj[0,:] request = { "basic_info" : { "n_steps" : n_steps, "manip" : manip_name, "start_fixed" : start_fixed }, "costs" : [ { "type" : "joint_vel", #"params": {"coeffs" : [gamma/(n_steps-1)]} "params": {"coeffs" : [1]} }, ], "constraints" : [ { "type" : "cart_vel", "name" : "cart_vel", "params" : { "max_displacement" : .02, "first_step" : 0, "last_step" : n_steps-1, #inclusive "link" : "r_gripper_tool_frame" } }, #{ # "type" : "collision", # "params" : { # "continuous" : True, # "coeffs" : [100]*(n_steps-3) + [0]*3, # "dist_pen" : [0.01] # } #} ], "init_info" : { "type":"given_traj", "data":[x.tolist() for x in init_traj] } } if use_collision_cost: request["costs"].append( { "type" : "collision", "params" : { "continuous" : True, "coeffs" : [20000], # penalty coefficients. list of length one is automatically expanded to a list of length n_timesteps #"coeffs" : [0] * (len(ee_trajs[0]) - 3) + [100]*3, "dist_pen" : [0.025] # robot-obstacle distance that penalty kicks in. expands to length n_timesteps } }) if joint_vel_limits is not None: request["constraints"].append( { "type" : "joint_vel_limits", "params": {"vals" : joint_vel_limits, "first_step" : 0, "last_step" : n_steps-1 } }) #coeff_mult = [0.0] * (len(poses) - 1) + [100] #coeff_mult = np.linspace(1, 100, num=len(poses)) #coeff_mult_trans = np.logspace(-3, 1, num=len(ee_trajs[0])) #coeff_mult_rot = np.logspace(-3, 1, num=len(ee_trajs[0])) coeff_mult_trans = np.array([0] * (len(ee_trajs[0])-1) + [100]) coeff_mult_rot = np.array([0] * (len(ee_trajs[0])-1) + [100]) if not grasp_cup: #coeff_mult_trans = np.array([0.1] * (len(ee_trajs[0])-1) + [100]) #coeff_mult_rot = np.array([1] * len(ee_trajs[0])) for (ee_link_name, ee_traj) in zip(ee_link_names, ee_trajs): poses = [openravepy.poseFromMatrix(hmat) for hmat in ee_traj] for (i_step,pose) in enumerate(poses): if start_fixed and i_step == 0: continue request["costs"].append( {"type":"pose", "params":{ "xyz":pose[4:7].tolist(), "wxyz":pose[0:4].tolist(), "link":ee_link_name, "timestep":i_step, #"pos_coeffs":list(pose_weights * [np.sqrt(beta_pos/n_steps)]*3), "pos_coeffs":[np.sqrt(beta_pos/n_steps) * coeff_mult_trans[i_step]]*3, #"rot_coeffs":list(pose_weights * [np.sqrt(beta_rot/n_steps)]*3) "rot_coeffs":[np.sqrt(beta_rot/n_steps) * coeff_mult_rot[i_step]]*3 } }) print "Cup XYZ:", cup_xyz s = json.dumps(request) with openravepy.RobotStateSaver(robot): with util.suppress_stdout(): prob = trajoptpy.ConstructProblem(s, robot.GetEnv()) # create object that stores optimization problem # for t in range(1,n_steps): # prob.AddCost(table_cost, [(t,j) for j in range(7)], "table%i"%t) if grasp_cup: tool_link = robot.GetLink("r_gripper_tool_frame") local_dir = np.array([0.,0.,1.]) manip = robot.GetManipulator(manip_name) arm_inds = manip.GetArmIndices() arm_joints = [robot.GetJointFromDOFIndex(ind) for ind in arm_inds] f = lambda x: gripper_tilt(x, robot, arm_inds, tool_link, local_dir) f2 = lambda x: gripper_tilt2(x, robot, arm_inds, tool_link, local_dir) dfdx = lambda x: gripper_tilt_dx(x, robot, arm_inds, tool_link, local_dir, arm_joints) #for t in xrange(1,n_steps): for t in xrange(n_steps-2,n_steps): prob.AddConstraint(f, dfdx, [(t,j) for j in xrange(7)], "EQ", "up%i"%t) #for t in xrange(1,n_steps-1): # prob.AddConstraint(f2, [(t,j) for j in xrange(7)], "EQ", "up%i"%t) #prob.AddConstraint(f2, [(n_steps-1,j) for j in xrange(7)], "EQ", "up%i"%(n_steps-1)) g = lambda x: gripper_around_cup(x, robot, arm_inds, tool_link, cup_xyz, R, D) prob.AddConstraint(g, [(n_steps-1,j) for j in xrange(7)], "EQ", "cup%i"%(n_steps-1)) result = trajoptpy.OptimizeProblem(prob) # do optimization print [(_name, viol) for (_name, viol) in result.GetConstraints() if viol > 0.0001] #assert [viol <= 1e-4 for (_name,viol) in result.GetConstraints()] traj = result.GetTraj() pose_costs = np.sum([cost_val for (cost_type, cost_val) in result.GetCosts() if cost_type == "pose"]) pose_err = [] with openravepy.RobotStateSaver(robot): for (ee_link_name, ee_traj) in zip(ee_link_names, ee_trajs): ee_link = robot.GetLink(ee_link_name) for (i_step, hmat) in enumerate(ee_traj): if start_fixed and i_step == 0: continue robot.SetDOFValues(traj[i_step], dof_inds) new_hmat = ee_link.GetTransform() pose_err.append(openravepy.poseFromMatrix(mu.invertHmat(hmat).dot(new_hmat))) pose_err = np.asarray(pose_err) pose_costs2 = (beta_rot/n_steps) * np.square(pose_err[:,1:4]).sum() + (beta_pos/n_steps) * np.square(pose_err[:,4:7]).sum() pose_costs3 = (beta_rot/n_steps) * np.dot(coeff_mult_rot[np.newaxis,1:], np.square(pose_err[:,1:4])).sum() + (beta_pos/n_steps) * np.dot(coeff_mult_trans[np.newaxis,1:], np.square(pose_err[:,4:7])).sum() joint_vel_cost = np.sum([cost_val for (cost_type, cost_val) in result.GetCosts() if cost_type == "joint_vel"]) joint_vel_err = np.diff(traj, axis=0) joint_vel_cost2 = (gamma/(n_steps-1)) * np.square(joint_vel_err).sum() sim_util.unwrap_in_place(traj, dof_inds) joint_vel_err = np.diff(traj, axis=0) collision_costs = [cost_val for (cost_type, cost_val) in result.GetCosts() if "collision" in cost_type] #collision_costs = [cost_val for (cost_type, cost_val) in result.GetConstraints() if "collision" in cost_type] if len(collision_costs) > 0: collision_err = np.asarray(collision_costs) collision_costs = np.sum(collision_costs) obj_value = np.sum([cost_val for (cost_type, cost_val) in result.GetCosts()]) print "{:>15} | {:>10} | {:>10}".format("", "trajopt", "computed") print "{:>15} | {:>10}".format("COSTS", "-"*23) print "{:>15} | {:>10,.4} | {:>10,.4}".format("joint_vel", joint_vel_cost, joint_vel_cost2) if np.isscalar(collision_costs): print "{:>15} | {:>10,.4} | {:>10}".format("collision(s)", collision_costs, "-") print "{:>15} | {:>10,.4} | {:>10,.4}".format("pose(s)", pose_costs, pose_costs2) print "{:>15} | {:>10,.4} | {:>10}".format("total_obj", obj_value, "-") print "" print "{:>15} | {:>10} | {:>10}".format("", "abs min", "abs max") print "{:>15} | {:>10}".format("ERRORS", "-"*23) print "{:>15} | {:>10,.4} | {:>10,.4}".format("joint_vel (deg)", np.rad2deg(np.abs(joint_vel_err).min()), np.rad2deg(np.abs(joint_vel_err).max())) if np.isscalar(collision_costs): print "{:>15} | {:>10,.4} | {:>10,.4}".format("collision(s)", np.abs(-collision_err).min(), np.abs(-collision_err).max()) print "{:>15} | {:>10,.4} | {:>10,.4}".format("rot pose(s)", np.abs(pose_err[:,1:4]).min(), np.abs(pose_err[:,1:4]).max()) print "{:>15} | {:>10,.4} | {:>10,.4}".format("trans pose(s)", np.abs(pose_err[:,4:7]).min(), np.abs(pose_err[:,4:7]).max()) print "" # make sure this function doesn't change state of the robot assert not np.any(orig_dof_inds - robot.GetActiveDOFIndices()) assert not np.any(orig_dof_vals - robot.GetDOFValues()) rot_err = np.abs(pose_err[:,1:4]).sum() pos_err = np.abs(pose_err[:,4:7]).sum() print collision_err return traj, obj_value, (pose_costs3, np.abs(collision_err[:-1]).sum())