def main(time_step=0.01): parser = create_parser() parser.add_argument('-teleport', action='store_true', help='Teleports between configurations') parser.add_argument('-viewer', action='store_true', help='enable the viewer while planning') # TODO: argument for selecting prior args = parser.parse_args() print('Arguments:', args) # TODO: nonuniform distribution to bias towards other actions # TODO: closed world and open world real_world = connect(use_gui=not args.viewer) add_data_path() task, state = get_problem1(localized='rooms', p_other=0.25) # surfaces | rooms for body in task.get_bodies(): add_body_name(body) robot = task.robot #dump_body(robot) assert (USE_DRAKE_PR2 == is_drake_pr2(robot)) attach_viewcone(robot) # Doesn't work for the normal pr2? draw_base_limits(get_base_limits(robot), color=(0, 1, 0)) #wait_for_user() # TODO: partially observable values # TODO: base movements preventing pick without look # TODO: do everything in local coordinate frame # TODO: automatically determine an action/command cannot be applied # TODO: convert numpy arrays into what they are close to # TODO: compute whether a plan will still achieve a goal and do that # TODO: update the initial state directly and then just redraw it to ensure uniqueness step = 0 while True: step += 1 print('\n' + 50 * '-') print(step, state) wait_for_user() #print({b: p.value for b, p in state.poses.items()}) with ClientSaver(): commands = plan_commands(state, args) print() if commands is None: print('Failure!') break if not commands: print('Success!') break apply_commands(state, commands, time_step=time_step) print(state) wait_for_user() disconnect()
def main(time_step=0.01): # TODO: closed world and open world real_world = connect(use_gui=True) add_data_path() task, state = get_problem1(localized='rooms', p_other=0.5) # surfaces | rooms for body in task.get_bodies(): add_body_name(body) robot = task.robot #dump_body(robot) assert (USE_DRAKE_PR2 == is_drake_pr2(robot)) attach_viewcone(robot) # Doesn't work for the normal pr2? draw_base_limits(get_base_limits(robot), color=(0, 1, 0)) #wait_for_interrupt() # TODO: partially observable values # TODO: base movements preventing pick without look # TODO: do everything in local coordinate frame # TODO: automatically determine an action/command cannot be applied # TODO: convert numpy arrays into what they are close to # TODO: compute whether a plan will still achieve a goal and do that # TODO: update the initial state directly and then just redraw it to ensure uniqueness step = 0 while True: step += 1 print('\n' + 50 * '-') print(step, state) #print({b: p.value for b, p in state.poses.items()}) with ClientSaver(): commands = plan_commands(state) print() if commands is None: print('Failure!') break if not commands: print('Success!') break apply_commands(state, commands, time_step=time_step) print(state) wait_for_interrupt() disconnect()
def main(): parser = create_parser() parser.add_argument('-problem', default='problem1', help='The name of the problem to solve') parser.add_argument('-cfree', action='store_true', help='Disables collisions') parser.add_argument('-deterministic', action='store_true', help='Uses a deterministic sampler') parser.add_argument('-optimal', action='store_true', help='Runs in an anytime mode') parser.add_argument('-t', '--max_time', default=120, type=int, help='The max time') parser.add_argument('-enable', action='store_true', help='Enables rendering during planning') parser.add_argument('-teleport', action='store_true', help='Teleports between configurations') parser.add_argument('-simulate', action='store_true', help='Simulates the system') parser.add_argument('-viewer', action='store_true', help='Enable the viewer and visualizes the plan') args = parser.parse_args() print('Arguments:', args) problem_fn_from_name = {fn.__name__: fn for fn in PROBLEMS} if args.problem not in problem_fn_from_name: raise ValueError(args.problem) problem_fn = problem_fn_from_name[args.problem] connect(use_gui=args.viewer) with HideOutput(): problem = problem_fn() saver = WorldSaver() draw_base_limits(problem.limits, color=RED) pddlstream_problem = pddlstream_from_problem(problem, collisions=not args.cfree, teleport=args.teleport) stream_info = { 'inverse-kinematics': StreamInfo(), 'plan-base-motion': StreamInfo(overhead=1e1), } _, _, _, stream_map, init, goal = pddlstream_problem print('Init:', init) print('Goal:', goal) #print('Streams:', stream_map.keys()) success_cost = 0 if args.optimal else INF planner = 'ff-astar' search_sample_ratio = 1 max_planner_time = 10 with Profiler(field='cumtime', num=25): # cumtime | tottime with LockRenderer(lock=not args.enable): solution = solve(pddlstream_problem, stream_info=stream_info, planner=planner, max_planner_time=max_planner_time, unit_costs=args.unit, success_cost=success_cost, max_time=args.max_time, verbose=True, debug=False, unit_efforts=True, effort_weight=1, search_sample_ratio=search_sample_ratio) saver.restore() print_solution(solution) plan, cost, evaluations = solution if (plan is None) or not has_gui(): disconnect() return # Maybe openrave didn't actually sample any joints... # http://openrave.org/docs/0.8.2/openravepy/examples.tutorial_iksolutions/ with LockRenderer(lock=not args.enable): commands = post_process(problem, plan, teleport=args.teleport) saver.restore() if args.simulate: control_commands(commands) else: time_step = None if args.teleport else 0.01 apply_commands(BeliefState(problem), commands, time_step) wait_for_user() disconnect()
def main(): parser = create_parser(default_algorithm='binding') parser.add_argument('-cfree', action='store_true', help='Disables collisions') parser.add_argument('-deterministic', action='store_true', help='Uses a deterministic sampler') parser.add_argument('-optimal', action='store_true', help='Runs in an anytime mode') parser.add_argument('-t', '--max_time', default=120, type=int, help='The max time') parser.add_argument('-enable', action='store_true', help='Enables rendering during planning') parser.add_argument('-simulate', action='store_true', help='Simulates the system') parser.add_argument('-viewer', action='store_true', help='Enable the viewer and visualizes the plan') args = parser.parse_args() print('Arguments:', args) connect(use_gui=args.viewer) with HideOutput(): problem = problem_fn(collisions=not args.cfree) saver = WorldSaver() draw_base_limits(problem.limits, color=RED) pddlstream_problem = pddlstream_from_problem(problem) stream_info = { 'test-cfree-conf-pose': StreamInfo(p_success=1e-2), 'test-cfree-traj-pose': StreamInfo(p_success=1e-1), 'compute-motion': StreamInfo(eager=True, p_success=0), 'test-reachable': StreamInfo(eager=True), 'Distance': FunctionInfo(eager=True), } _, _, _, stream_map, init, goal = pddlstream_problem print('Init:', init) print('Goal:', goal) success_cost = 0 if args.optimal else INF planner = 'ff-wastar1' search_sample_ratio = 0 max_planner_time = 10 with Profiler(field='tottime', num=25): # cumtime | tottime with LockRenderer(lock=not args.enable): solution = solve(pddlstream_problem, algorithm=args.algorithm, stream_info=stream_info, planner=planner, max_planner_time=max_planner_time, debug=False, unit_costs=args.unit, success_cost=success_cost, max_time=args.max_time, verbose=True, unit_efforts=True, effort_weight=1, search_sample_ratio=search_sample_ratio) saver.restore() print_solution(solution) plan, cost, evaluations = solution if (plan is None) or not has_gui(): disconnect() return with LockRenderer(): commands = post_process(problem, plan) saver.restore() # Assumes bodies are ordered the same way wait_for_user() if args.simulate: control_commands(commands) else: apply_commands(BeliefState(problem), commands, time_step=1e-2) # 1e-2 | None wait_for_user() disconnect()
def main(teleport=False): #parser = create_parser() parser = argparse.ArgumentParser() parser.add_argument('-algorithm', default='incremental', help='Specifies the algorithm') parser.add_argument('-cfree', action='store_true', help='Disables collisions') parser.add_argument('-deterministic', action='store_true', help='Uses a deterministic sampler') parser.add_argument('-optimal', action='store_true', help='Runs in an anytime mode') parser.add_argument('-t', '--max_time', default=5*60, type=int, help='The max time') parser.add_argument('-enable', action='store_true', help='Enables rendering during planning') parser.add_argument('-viewer', action='store_true', help='Enable the viewer and visualizes the plan') args = parser.parse_args() print('Arguments:', args) connect(use_gui=args.viewer) with HideOutput(): namo_problem = problem_fn(collisions=not args.cfree) saver = WorldSaver() draw_base_limits(namo_problem.limits, color=RED) pddlstream_problem, edges = pddlstream_from_problem(namo_problem, teleport=teleport) _, constant_map, _, stream_map, init, goal = pddlstream_problem print('Constants:', constant_map) print('Init:', init) print('Goal:', goal) stream_info = { 'compute-motion': StreamInfo(eager=True, p_success=0), 'ConfConfCollision': PredicateInfo(p_success=1, overhead=0.1), 'TrajConfCollision': PredicateInfo(p_success=1, overhead=1), 'TrajTrajCollision': PredicateInfo(p_success=1, overhead=10), 'TrajDistance': FunctionInfo(eager=True), # Need to eagerly evaluate otherwise 0 duration (failure) } success_cost = 0 if args.optimal else INF max_planner_time = 10 with Profiler(field='tottime', num=25): # cumtime | tottime with LockRenderer(lock=not args.enable): # TODO: solution = solve_incremental(pddlstream_problem if args.algorithm == 'incremental': solution = solve_incremental(pddlstream_problem, max_planner_time=max_planner_time, success_cost=success_cost, max_time=args.max_time, start_complexity=INF, verbose=True, debug=True) elif args.algorithm == 'focused': solution = solve_focused(pddlstream_problem, stream_info=stream_info, max_planner_time=max_planner_time, success_cost=success_cost, max_time=args.max_time, max_skeletons=None, bind=True, max_failures=INF, verbose=True, debug=True) else: raise ValueError(args.algorithm) print_solution(solution) plan, cost, evaluations = solution if (plan is None) or not has_gui(): disconnect() return saver.restore() draw_edges(edges) state = BeliefState(namo_problem) wait_for_user('Begin?') #time_step = None if teleport else 0.01 #with VideoSaver('video.mp4'): display_plan(namo_problem, state, plan) wait_for_user('Finish?') disconnect()
def main(display=True, teleport=False): parser = argparse.ArgumentParser() parser.add_argument('-algorithm', default='incremental', help='Specifies the algorithm') parser.add_argument('-cfree', action='store_true', help='Disables collisions') parser.add_argument('-deterministic', action='store_true', help='Uses a deterministic sampler') parser.add_argument('-optimal', action='store_true', help='Runs in an anytime mode') parser.add_argument('-t', '--max_time', default=5*60, type=int, help='The max time') parser.add_argument('-viewer', action='store_true', help='enable the viewer while planning') args = parser.parse_args() print(args) #problem_fn_from_name = {fn.__name__: fn for fn in PROBLEMS} #if args.problem not in problem_fn_from_name: # raise ValueError(args.problem) #problem_fn = problem_fn_from_name[args.problem] connect(use_gui=args.viewer) with HideOutput(): problem = problem_fn(collisions=not args.cfree) saver = WorldSaver() draw_base_limits(problem.limits, color=RED) pddlstream, edges = pddlstream_from_problem(problem, teleport=teleport) _, constant_map, _, stream_map, init, goal = pddlstream print('Constants:', constant_map) print('Init:', init) print('Goal:', goal) success_cost = 0 if args.optimal else INF max_planner_time = 10 stream_info = { 'compute-motion': StreamInfo(eager=True, p_success=0), 'ConfConfCollision': FunctionInfo(p_success=1, overhead=0.1), 'TrajConfCollision': FunctionInfo(p_success=1, overhead=1), 'TrajTrajCollision': FunctionInfo(p_success=1, overhead=10), 'TrajDistance': FunctionInfo(eager=True), # Need to eagerly evaluate otherwise 0 duration (failure) } pr = cProfile.Profile() pr.enable() with LockRenderer(False): if args.algorithm == 'incremental': solution = solve_incremental(pddlstream, max_planner_time=max_planner_time, success_cost=success_cost, max_time=args.max_time, start_complexity=INF, verbose=True, debug=True) elif args.algorithm == 'focused': solution = solve_focused(pddlstream, stream_info=stream_info, max_planner_time=max_planner_time, success_cost=success_cost, max_time=args.max_time, max_skeletons=None, bind=True, max_failures=INF, verbose=True, debug=True) else: raise ValueError(args.algorithm) print_solution(solution) plan, cost, evaluations = solution pr.disable() pstats.Stats(pr).sort_stats('tottime').print_stats(25) # cumtime | tottime if plan is None: wait_for_user() return if (not display) or (plan is None): disconnect() return if not args.viewer: disconnect() connect(use_gui=True) with LockRenderer(): with HideOutput(): problem_fn() # TODO: way of doing this without reloading? saver.restore() # Assumes bodies are ordered the same way draw_edges(edges) state = BeliefState(problem) wait_for_user() #time_step = None if teleport else 0.01 #with VideoSaver('video.mp4'): display_plan(problem, state, plan) wait_for_user() disconnect()
def main(verbose=True): # TODO: could work just on postprocessing # TODO: try the other reachability database # TODO: option to only consider costs during local optimization parser = create_parser() parser.add_argument('-problem', default='packed', help='The name of the problem to solve') parser.add_argument('-n', '--number', default=5, type=int, help='The number of objects') parser.add_argument('-cfree', action='store_true', help='Disables collisions') parser.add_argument('-deterministic', action='store_true', help='Uses a deterministic sampler') parser.add_argument('-optimal', action='store_true', help='Runs in an anytime mode') parser.add_argument('-t', '--max_time', default=120, type=int, help='The max time') parser.add_argument('-teleport', action='store_true', help='Teleports between configurations') parser.add_argument('-enable', action='store_true', help='Enables rendering during planning') parser.add_argument('-simulate', action='store_true', help='Simulates the system') parser.add_argument('-viewer', action='store_true', help='Enable the viewer and visualizes the plan') args = parser.parse_args() print('Arguments:', args) problem_fn_from_name = {fn.__name__: fn for fn in PROBLEMS} if args.problem not in problem_fn_from_name: raise ValueError(args.problem) problem_fn = problem_fn_from_name[args.problem] connect(use_gui=args.viewer) with HideOutput(): problem = problem_fn(num=args.number) draw_base_limits(problem.base_limits, color=(1, 0, 0)) saver = WorldSaver() #handles = [] #for link in get_group_joints(problem.robot, 'left_arm'): # handles.append(draw_link_name(problem.robot, link)) #wait_for_user() pddlstream_problem = pddlstream_from_problem(problem, collisions=not args.cfree, teleport=args.teleport) stream_info = { 'inverse-kinematics': StreamInfo(), 'plan-base-motion': StreamInfo(overhead=1e1), 'test-cfree-pose-pose': StreamInfo(p_success=1e-3, verbose=verbose), 'test-cfree-approach-pose': StreamInfo(p_success=1e-2, verbose=verbose), 'test-cfree-traj-pose': StreamInfo(p_success=1e-1, verbose=verbose), # TODO: apply to arm and base trajs #'test-cfree-traj-grasp-pose': StreamInfo(verbose=verbose), 'Distance': FunctionInfo(p_success=0.99, opt_fn=lambda q1, q2: BASE_CONSTANT), #'MoveCost': FunctionInfo(lambda t: BASE_CONSTANT), } #stream_info = {} _, _, _, stream_map, init, goal = pddlstream_problem print('Init:', init) print('Goal:', goal) print('Streams:', str_from_object(set(stream_map))) success_cost = 0 if args.optimal else INF planner = 'ff-astar' if args.optimal else 'ff-wastar3' search_sample_ratio = 2 max_planner_time = 10 # effort_weight = 0 if args.optimal else 1 effort_weight = 1e-3 if args.optimal else 1 with Profiler(field='tottime', num=25): # cumtime | tottime with LockRenderer(lock=not args.enable): solution = solve(pddlstream_problem, algorithm=args.algorithm, stream_info=stream_info, planner=planner, max_planner_time=max_planner_time, unit_costs=args.unit, success_cost=success_cost, max_time=args.max_time, verbose=True, debug=False, unit_efforts=True, effort_weight=effort_weight, search_sample_ratio=search_sample_ratio) saver.restore() cost_over_time = [(s.cost, s.time) for s in SOLUTIONS] for i, (cost, runtime) in enumerate(cost_over_time): print('Plan: {} | Cost: {:.3f} | Time: {:.3f}'.format( i, cost, runtime)) #print(SOLUTIONS) print_solution(solution) plan, cost, evaluations = solution if (plan is None) or not has_gui(): disconnect() return with LockRenderer(lock=not args.enable): commands = post_process(problem, plan, teleport=args.teleport) saver.restore() draw_base_limits(problem.base_limits, color=(1, 0, 0)) wait_for_user() if args.simulate: control_commands(commands) else: time_step = None if args.teleport else 0.01 apply_commands(State(), commands, time_step) wait_for_user() disconnect()
def main(display=True, teleport=False): parser = argparse.ArgumentParser() #parser.add_argument('-problem', default='rovers1', help='The name of the problem to solve') parser.add_argument('-algorithm', default='focused', help='Specifies the algorithm') parser.add_argument('-cfree', action='store_true', help='Disables collisions') parser.add_argument('-deterministic', action='store_true', help='Uses a deterministic sampler') parser.add_argument('-optimal', action='store_true', help='Runs in an anytime mode') parser.add_argument('-t', '--max_time', default=120, type=int, help='The max time') parser.add_argument('-unit', action='store_true', help='Uses unit costs') parser.add_argument('-simulate', action='store_true', help='Simulates the system') parser.add_argument('-viewer', action='store_true', help='enable the viewer while planning') args = parser.parse_args() print(args) #problem_fn_from_name = {fn.__name__: fn for fn in PROBLEMS} #if args.problem not in problem_fn_from_name: # raise ValueError(args.problem) #problem_fn = problem_fn_from_name[args.problem] connect(use_gui=args.viewer) with HideOutput(): problem = problem_fn(collisions=not args.cfree) saver = WorldSaver() draw_base_limits(problem.limits, color=RED) pddlstream_problem = pddlstream_from_problem(problem, teleport=teleport) stream_info = { 'test-cfree-conf-pose': StreamInfo(negate=True, p_success=1e-2), 'test-cfree-traj-pose': StreamInfo(negate=True, p_success=1e-1), 'compute-motion': StreamInfo(eager=True, p_success=0), 'test-reachable': StreamInfo(eager=True), 'Distance': FunctionInfo(eager=True), } _, _, _, stream_map, init, goal = pddlstream_problem print('Init:', init) print('Goal:', goal) success_cost = 0 if args.optimal else INF planner = 'ff-wastar1' search_sample_ratio = 0 max_planner_time = 10 pr = cProfile.Profile() pr.enable() with LockRenderer(True): if args.algorithm == 'focused': solution = solve_focused(pddlstream_problem, stream_info=stream_info, planner=planner, max_planner_time=max_planner_time, debug=False, unit_costs=args.unit, success_cost=success_cost, max_time=args.max_time, verbose=True, unit_efforts=True, effort_weight=1, bind=True, max_skeletons=None, search_sample_ratio=search_sample_ratio) elif args.algorithm == 'incremental': solution = solve_incremental(pddlstream_problem, planner=planner, max_planner_time=max_planner_time, unit_costs=args.unit, success_cost=success_cost, max_time=args.max_time, verbose=True) else: raise ValueError(args.algorithm) saver.restore() print_solution(solution) plan, cost, evaluations = solution pr.disable() pstats.Stats(pr).sort_stats('tottime').print_stats(25) # cumtime | tottime if plan is None: wait_for_user() return if (not display) or (plan is None): disconnect() return with LockRenderer(): commands = post_process(problem, plan, teleport=teleport) saver.restore() # Assumes bodies are ordered the same way if not args.viewer: disconnect() connect(use_gui=True) with LockRenderer(): with HideOutput(): problem_fn() # TODO: way of doing this without reloading? saver.restore() # Assumes bodies are ordered the same way wait_for_user() if args.simulate: control_commands(commands) else: time_step = None if teleport else 0.01 apply_commands(BeliefState(problem), commands, time_step=time_step) wait_for_user() disconnect()
def main(): parser = create_parser() parser.add_argument('-problem', default='rovers1', help='The name of the problem to solve') parser.add_argument('-cfree', action='store_true', help='Disables collisions') parser.add_argument('-deterministic', action='store_true', help='Uses a deterministic sampler') parser.add_argument('-optimal', action='store_true', help='Runs in an anytime mode') parser.add_argument('-t', '--max_time', default=120, type=int, help='The max time') parser.add_argument('-enable', action='store_true', help='Enables rendering during planning') parser.add_argument('-teleport', action='store_true', help='Teleports between configurations') parser.add_argument('-simulate', action='store_true', help='Simulates the system') parser.add_argument('-viewer', action='store_true', help='Enable the viewer and visualizes the plan') args = parser.parse_args() print('Arguments:', args) problem_fn_from_name = {fn.__name__: fn for fn in PROBLEMS} if args.problem not in problem_fn_from_name: raise ValueError(args.problem) problem_fn = problem_fn_from_name[args.problem] connect(use_gui=args.viewer) with HideOutput(): rovers_problem = problem_fn() saver = WorldSaver() draw_base_limits(rovers_problem.limits, color=RED) pddlstream_problem = pddlstream_from_problem(rovers_problem, collisions=not args.cfree, teleport=args.teleport, holonomic=False, reversible=True, use_aabb=True) stream_info = { 'test-cfree-ray-conf': StreamInfo(), 'test-reachable': StreamInfo(p_success=1e-1), 'obj-inv-visible': StreamInfo(), 'com-inv-visible': StreamInfo(), 'sample-above': StreamInfo(), 'sample-motion': StreamInfo(overhead=10), } _, _, _, stream_map, init, goal = pddlstream_problem print('Init:', init) print('Goal:', goal) #print('Streams:', stream_map.keys()) success_cost = 0 if args.optimal else INF planner = 'ff-wastar3' search_sample_ratio = 2 max_planner_time = 10 # TODO: need to accelerate samples here because of the failed test reachable with Profiler(field='tottime', num=25): with LockRenderer(lock=not args.enable): # TODO: option to only consider costs during local optimization solution = solve(pddlstream_problem, algorithm=args.algorithm, stream_info=stream_info, planner=planner, max_planner_time=max_planner_time, debug=False, unit_costs=args.unit, success_cost=success_cost, max_time=args.max_time, verbose=True, unit_efforts=True, effort_weight=1, search_sample_ratio=search_sample_ratio) for body in get_bodies(): if body not in saver.bodies: remove_body(body) saver.restore() print_solution(solution) plan, cost, evaluations = solution if (plan is None) or not has_gui(): disconnect() return # Maybe OpenRAVE didn't actually sample any joints... # http://openrave.org/docs/0.8.2/openravepy/examples.tutorial_iksolutions/ with LockRenderer(): commands = post_process(rovers_problem, plan) saver.restore() wait_for_user('Begin?') if args.simulate: control_commands(commands) else: time_step = None if args.teleport else 0.01 apply_commands(BeliefState(rovers_problem), commands, time_step) wait_for_user('Finish?') disconnect()
def main(display=True, teleport=False): parser = argparse.ArgumentParser() parser.add_argument('-problem', default='rovers1', help='The name of the problem to solve') parser.add_argument('-algorithm', default='focused', help='Specifies the algorithm') parser.add_argument('-cfree', action='store_true', help='Disables collisions') parser.add_argument('-deterministic', action='store_true', help='Uses a deterministic sampler') parser.add_argument('-optimal', action='store_true', help='Runs in an anytime mode') parser.add_argument('-t', '--max_time', default=120, type=int, help='The max time') parser.add_argument('-unit', action='store_true', help='Uses unit costs') parser.add_argument('-simulate', action='store_true', help='Simulates the system') parser.add_argument('-viewer', action='store_true', help='enable the viewer while planning') args = parser.parse_args() print(args) problem_fn_from_name = {fn.__name__: fn for fn in PROBLEMS} if args.problem not in problem_fn_from_name: raise ValueError(args.problem) problem_fn = problem_fn_from_name[args.problem] connect(use_gui=args.viewer) with HideOutput(): problem = problem_fn() saver = WorldSaver() draw_base_limits(problem.limits, color=(1, 0, 0)) pddlstream_problem = pddlstream_from_problem(problem, collisions=not args.cfree, teleport=teleport) stream_info = { 'test-cfree-ray-conf': StreamInfo(negate=True), 'test-reachable': StreamInfo(p_success=1e-1), 'obj-inv-visible': StreamInfo(), 'com-inv-visible': StreamInfo(), 'sample-above': StreamInfo(), 'sample-motion': StreamInfo(overhead=10), } _, _, _, stream_map, init, goal = pddlstream_problem print('Init:', init) print('Goal:', goal) #print('Streams:', stream_map.keys()) success_cost = 0 if args.optimal else INF planner = 'ff-wastar3' search_sample_ratio = 2 max_planner_time = 10 # TODO: need to accelerate samples here because of the failed test reachable pr = cProfile.Profile() pr.enable() with LockRenderer(False): if args.algorithm == 'focused': # TODO: option to only consider costs during local optimization solution = solve_focused( pddlstream_problem, stream_info=stream_info, planner=planner, max_planner_time=max_planner_time, debug=False, unit_costs=args.unit, success_cost=success_cost, max_time=args.max_time, verbose=True, unit_efforts=True, effort_weight=1, #bind=True, max_skeletons=None, search_sample_ratio=search_sample_ratio) elif args.algorithm == 'incremental': solution = solve_incremental(pddlstream_problem, planner=planner, max_planner_time=max_planner_time, unit_costs=args.unit, success_cost=success_cost, max_time=args.max_time, verbose=True) else: raise ValueError(args.algorithm) print_solution(solution) plan, cost, evaluations = solution pr.disable() pstats.Stats(pr).sort_stats('tottime').print_stats(25) # cumtime | tottime if plan is None: return if (not display) or (plan is None): disconnect() return # Maybe openrave didn't actually sample any joints... # http://openrave.org/docs/0.8.2/openravepy/examples.tutorial_iksolutions/ with LockRenderer(): commands = post_process(problem, plan, teleport=teleport) saver.restore() # Assumes bodies are ordered the same way if not args.viewer: disconnect() connect(use_gui=True) with LockRenderer(): with HideOutput(): problem_fn() # TODO: way of doing this without reloading? saver.restore() # Assumes bodies are ordered the same way if args.simulate: control_commands(commands) else: time_step = None if teleport else 0.01 apply_commands(BeliefState(problem), commands, time_step) wait_for_user() disconnect()