def fn(self, edge, object_name, object_pose):
oracle = self.oracle
preprocess_edge(oracle, edge)
object_aabb = oracle.get_aabb(object_name,
trans_from_pose(object_pose.value))
object_aabb_min, object_aabb_max = aabb_min(object_aabb), aabb_max(
object_aabb)
configs = [
q for q in edge.configs()
if fast_aabb_overlap(object_aabb_min, object_aabb_max, q.aabbs)
]
if len(configs) == 0: return False
#distance = oracle.get_radius2('pr2') + oracle.get_radius2(object_name)
#z = get_point(oracle.robot)[-1]
#configs = [q for q in edge.configs() if length2(np.array([q.value[-3], q.value[-2], z]) - point_from_pose(object_pose.value)) <= distance]
#if len(configs) == 0: return False
# TODO - oracle.robot.SetActiveDOFValues(q.value) is the bottleneck: check each object at the same time
with oracle.body_saver(object_name):
oracle.set_pose(object_name, object_pose)
with oracle.robot_saver():
CSpace.robot_arm_and_base(
oracle.robot.GetActiveManipulator()).set_active()
with collision_saver(
oracle.env, openravepy_int.CollisionOptions.ActiveDOFs
): # TODO - does this really do what I think?
for q in configs: # TODO - do this with other other collision functions
q.saver.Restore()
if robot_collision(oracle, object_name):
return True
return False
def compile_problem(oracle):
problem = oracle.problem
CSpace.robot_base(oracle.robot).set_active()
initial_config = Config(
base_values_from_full_config(oracle.initial_config.value))
initial_atoms = [
AtConfig(Conf(initial_config)),
]
print 'Initial', initial_config.value
goal_literals = [AtConfig(Conf(problem.goal_config))
] if problem.goal_config is not None else []
print 'Goal', problem.goal_config.value
actions = [
Move(oracle),
]
axioms = [
#InRegionAxiom(),
]
cond_streams = [
CollisionFreeTest(oracle),
ConfStream(oracle),
]
objects = []
return STRIPStreamProblem(initial_atoms, goal_literals, actions + axioms,
cond_streams, objects)
def fn(self, edge, object_name, object_pose):
oracle = self.oracle
preprocess_edge(oracle, edge)
object_aabb = oracle.get_aabb(object_name, trans_from_pose(object_pose.value))
object_aabb_min, object_aabb_max = aabb_min(object_aabb), aabb_max(object_aabb)
configs = [q for q in edge.configs() if fast_aabb_overlap(object_aabb_min, object_aabb_max, q.aabbs)]
if len(configs) == 0: return False
#distance = oracle.get_radius2('pr2') + oracle.get_radius2(object_name)
#z = get_point(oracle.robot)[-1]
#configs = [q for q in edge.configs() if length2(np.array([q.value[-3], q.value[-2], z]) - point_from_pose(object_pose.value)) <= distance]
#if len(configs) == 0: return False
# TODO - oracle.robot.SetActiveDOFValues(q.value) is the bottleneck: check each object at the same time
with oracle.body_saver(object_name):
oracle.set_pose(object_name, object_pose)
with oracle.robot_saver():
CSpace.robot_arm_and_base(oracle.robot.GetActiveManipulator()).set_active()
with collision_saver(oracle.env, openravepy_int.CollisionOptions.ActiveDOFs): # TODO - does this really do what I think?
for q in configs: # TODO - do this with other other collision functions
q.saver.Restore()
if robot_collision(oracle, object_name):
return True
return False
def preprocess_edge(oracle, edge):
if not hasattr(edge, 'preprocessed'):
edge.preprocessed = True
with oracle.robot_saver():
CSpace.robot_arm_and_base(oracle.robot.GetActiveManipulator()).set_active()
for q in edge.configs():
if not hasattr(q, 'saver'):
oracle.robot.SetActiveDOFValues(q.value)
q.saver = oracle.robot_saver()
q.manip_trans = get_manip_trans(oracle)
q.aabbs = [(aabb_min(aabb), aabb_max(aabb)) for aabb in [oracle.compute_part_aabb(part) for part in USE_ROBOT_PARTS]]
def preprocess_edge(oracle, edge):
if not hasattr(edge, 'preprocessed'):
edge.preprocessed = True
with oracle.robot_saver():
CSpace.robot_arm_and_base(
oracle.robot.GetActiveManipulator()).set_active()
for q in edge.configs():
if not hasattr(q, 'saver'):
oracle.robot.SetActiveDOFValues(q.value)
q.saver = oracle.robot_saver()
q.manip_trans = get_manip_trans(oracle)
q.aabbs = [(aabb_min(aabb), aabb_max(aabb)) for aabb in [
oracle.compute_part_aabb(part)
for part in USE_ROBOT_PARTS
]]
def load_grasp_database(robot, filename):
grasps = []
for grasp_tform, gripper_config, (trajectory, indices), approach_vector in read_pickle(filename):
grasps.append(Grasp(np.array(grasp_tform), np.array(gripper_config),
TrajTrajectory(CSpace(robot, indices=np.array(indices)), new_traj().deserialize(trajectory)), approach_vector))
if DEBUG: print 'Loaded', filename
return grasps
def initialize_openrave(env, arm, min_delta=.01, collision_checker='ode'):
env.StopSimulation()
for sensor in env.GetSensors():
sensor.Configure(Sensor.ConfigureCommand.PowerOff)
sensor.Configure(Sensor.ConfigureCommand.RenderDataOff)
sensor.Configure(Sensor.ConfigureCommand.RenderGeometryOff)
env.Remove(sensor)
env.SetCollisionChecker(RaveCreateCollisionChecker(env, collision_checker))
env.GetCollisionChecker().SetCollisionOptions(0)
robot = env.GetRobots()[0]
cd_model = databases.convexdecomposition.ConvexDecompositionModel(robot)
if not cd_model.load(): cd_model.autogenerate()
l_model = databases.linkstatistics.LinkStatisticsModel(robot)
if not l_model.load(): l_model.autogenerate()
l_model.setRobotWeights()
l_model.setRobotResolutions(xyzdelta=min_delta) # xyzdelta is the minimum Cartesian distance of an object
or_arm = 'leftarm' if arm == 'l' else 'rightarm'
robot.SetActiveManipulator(or_arm) # NOTE - Need this or the manipulator computations are off
#extrema = aabb_extrema(aabb_union([aabb_from_body(body) for body in env.GetBodies()])).T
#robot.SetAffineTranslationLimits(*extrema)
ikmodel = databases.inversekinematics.InverseKinematicsModel(robot=robot, iktype=IkParameterization.Type.Transform6D,
forceikfast=True, freeindices=None, freejoints=None, manip=None)
if not ikmodel.load(): ikmodel.autogenerate()
cspace = CSpace.robot_arm(robot.GetActiveManipulator())
cspace.set_active()
def process_scan_room(robot):
# TODO: could do this whole thing symbolically using point head
path = get_scan_path(robot, math.pi / 6)
if path is None:
raise RuntimeError()
cspace = CSpace.robot_manipulator(robot, 'head')
h_traj = PathTrajectory(cspace, path)
h_traj.traj() # Precompute the traj
return [h_traj]
def sample_trajectory(robot, q1, q2, name, **kwargs):
cspace = CSpace.robot_manipulator(robot, name)
cspace.name = name
with robot:
cspace.set_active()
base_path = mp_birrt(robot, q1, q2, **kwargs)
if base_path is None:
return None
return PathTrajectory(cspace, base_path)
def sample_motion(q1, q2):
if not DO_BASE_MOTION:
yield [None]
#with oracle.robot: # NOTE - this causes an segfault when freeing the memory
oracle.set_robot_config(q1)
goal = base_values_from_full_config(q2.value)
traj = motion_plan(oracle.env, CSpace.robot_base(oracle.robot), goal)
if traj is not None:
traj.obj = None
yield [traj]
def sample_motion(q1, q2):
if not DO_BASE_MOTION:
yield [None]
with oracle.robot:
oracle.set_robot_config(q1)
goal = base_values_from_full_config(q2.value)
traj = motion_plan(oracle.env, CSpace.robot_base(oracle.robot), goal)
if traj is not None:
traj.obj = None
yield [traj]
def collision_ignorant_grasp(oracle, geom_hash, grasp_tform, approach_vector, manip_name=None): # TODO - try to make a valid grasp if possible
with oracle.state_saver():
if manip_name is not None: oracle.robot.SetActiveManipulator(manip_name)
oracle.set_active_state(active_obstacles=False, active_objects=set())
open_gripper(oracle)
oracle.robot.SetDOFValues(oracle.default_left_arm_config, oracle.robot.GetActiveManipulator().GetArmIndices())
gripper_config, gripper_traj = oracle.task_manip.CloseFingers(offset=None, movingdir=None, execute=False, outputtraj=False, outputfinal=True,
coarsestep=None, translationstepmult=None, finestep=None, outputtrajobj=True)
return Grasp(grasp_tform, gripper_config, TrajTrajectory(CSpace(oracle.robot,
indices=oracle.robot.GetActiveManipulator().GetGripperIndices()), gripper_traj), approach_vector)
def collision_free_grasp(oracle, geom_hash, grasp_tform, approach_vector, manip_name=None):
body_name = oracle.get_body_name(geom_hash)
with oracle.state_saver():
if manip_name is not None: oracle.robot.SetActiveManipulator(manip_name)
oracle.set_active_state(active_obstacles=False, active_objects=set([body_name]))
open_gripper(oracle)
oracle.robot.SetDOFValues(oracle.default_left_arm_config, oracle.robot.GetActiveManipulator().GetArmIndices())
set_trans(oracle.bodies[body_name], object_trans_from_manip_trans(get_manip_trans(oracle), grasp_tform))
if not robot_collision(oracle, body_name):
gripper_config, gripper_traj = oracle.task_manip.CloseFingers(offset=None, movingdir=None, execute=False, outputtraj=False, outputfinal=True,
coarsestep=None, translationstepmult=None, finestep=None, outputtrajobj=True)
return Grasp(grasp_tform, gripper_config, TrajTrajectory(CSpace(oracle.robot,
indices=oracle.robot.GetActiveManipulator().GetGripperIndices()), gripper_traj), approach_vector)
return None
def sample_base_trajectory(robot, q1, q2, **kwargs):
# TODO: ActiveDOFs doesn't work here for FCL?
env = robot.GetEnv()
cspace = CSpace.robot_manipulator(robot, 'base')
cspace.name = 'base'
with robot:
cspace.set_active()
collision_fn = get_collision_fn(env,
robot,
self_collisions=False,
limits=True)
base_path = birrt(q1, q2, get_distance_fn(robot),
get_sample_fn(env, robot), get_extend_fn(robot),
collision_fn, **kwargs)
if base_path is None:
return None
return PathTrajectory(cspace, base_path)
def feasible_pick_place(env, robot, obj, grasps, approach_config,
do_motion_planning):
base_manip = interfaces.BaseManipulation(robot,
plannername=None,
maxvelmult=None)
pose = Pose(get_pose(obj))
for grasp in grasps:
manip_trans, approach_vector = manip_from_pose_grasp(pose, grasp)
set_config(robot, approach_config,
robot.GetActiveManipulator().GetArmIndices())
if env.CheckCollision(robot) or robot.CheckSelfCollision():
continue
grasp_config = inverse_kinematics_helper(
env, robot,
manip_trans) # NOTE - maybe need to find all IK solutions
#grasp_config = solve_inverse_kinematics_exhaustive(env, robot, manip_trans)
if grasp_config is None:
continue
set_config(robot, grasp_config, get_active_arm_indices(robot))
#approach_config = get_full_config(robot)
traj, arm_traj = None, None
if do_motion_planning:
#traj = vector_traj_helper(env, robot, approach_vector)
traj = workspace_traj_helper(base_manip, approach_vector)
if traj is None:
continue
set_config(robot, traj.end(), get_active_arm_indices(robot))
#vector_config = oracle.get_robot_config()
arm_traj = motion_plan(env,
CSpace.robot_arm(get_manipulator(robot)),
approach_config,
self_collisions=True)
if arm_traj is None:
continue
return grasp_config, traj, arm_traj
return None
def open_gripper_trajectory(arm):
cspace = CSpace.robot_gripper(arm)
cspace.name = '{}_gripper'.format(arm.GetName()[0])
return PathTrajectory(cspace, [get_close_conf(arm), get_open_conf(arm)])
def solve_tamp(env):
viewer = env.GetViewer() is not None
problem = PROBLEM(env)
robot = env.GetRobots()[0]
set_base_conf(robot, (-.75, .2, -math.pi / 2))
initialize_openrave(env, ARM, min_delta=.01)
manipulator = robot.GetActiveManipulator()
cspace = CSpace.robot_arm(manipulator)
base_manip = interfaces.BaseManipulation(robot,
plannername=None,
maxvelmult=None)
bodies = {obj: env.GetKinBody(obj) for obj in problem.object_names}
all_bodies = bodies.values()
assert len({body.GetKinematicsGeometryHash()
for body in all_bodies
}) == 1 # NOTE - assuming all objects has the same geometry
body1 = all_bodies[-1] # Generic object 1
body2 = all_bodies[-2] if len(bodies) >= 2 else body1 # Generic object 2
grasps = get_grasps(env, robot, body1, USE_GRASP_APPROACH,
USE_GRASP_TYPE)[:MAX_GRASPS]
poses = problem.known_poses if problem.known_poses else []
open_gripper(manipulator)
initial_conf = Conf(
robot.GetConfigurationValues()[manipulator.GetArmIndices()])
####################
cond_streams = [
# Pick/place trajectory
EasyListGenStream(inputs=[P, G],
outputs=[Q, T],
conditions=[],
effects=[GraspMotion(P, G, Q, T)],
generator=sample_grasp_traj_fn(
env, robot, manipulator, body1, all_bodies)),
# Move trajectory
EasyListGenStream(inputs=[Q, Q2],
outputs=[T],
conditions=[],
effects=[FreeMotion(Q, Q2, T)],
generator=sample_free_motion_fn(
manipulator, base_manip, cspace, all_bodies),
order=1,
max_level=0),
EasyListGenStream(inputs=[Q, Q2, G],
outputs=[T],
conditions=[],
effects=[HoldingMotion(Q, Q2, G, T)],
generator=sample_holding_motion_fn(
robot, manipulator, base_manip, cspace, body1,
all_bodies),
order=1,
max_level=0),
# Collisions
EasyTestStream(inputs=[P, P2],
conditions=[],
effects=[CFreePose(P, P2)],
test=cfree_pose_fn(env, body1, body2),
eager=True),
EasyTestStream(inputs=[T, P],
conditions=[],
effects=[CFreeTraj(T, P)],
test=cfree_traj_fn(env, robot, manipulator, body1,
body2, all_bodies)),
]
####################
constants = map(GRASP, grasps) + map(POSE, poses)
initial_atoms = [
ConfEq(initial_conf),
HandEmpty(),
] + [PoseEq(obj, pose) for obj, pose in problem.initial_poses.iteritems()]
goal_formula = And(
ConfEq(initial_conf),
*(PoseEq(obj, pose) for obj, pose in problem.goal_poses.iteritems()))
stream_problem = STRIPStreamProblem(initial_atoms, goal_formula,
actions + axioms, cond_streams,
constants)
if viewer: raw_input('Start?')
search_fn = get_fast_downward('eager', max_time=10, verbose=False)
solve = lambda: incremental_planner(
stream_problem, search=search_fn, frequency=1, waves=True, debug=False)
#solve = lambda: simple_focused(stream_problem, search=search_fn, max_level=INF, shared=False, debug=False, verbose=False, max_time=15)
env.Lock()
plan, universe = solve()
env.Unlock()
print SEPARATOR
plan = convert_plan(plan)
if plan is not None:
print 'Success'
for i, (action, args) in enumerate(plan):
print i + 1, action, args
else:
print 'Failure'
####################
if viewer and plan is not None:
print SEPARATOR
visualize_solution(env, problem, initial_conf, robot, manipulator,
bodies, plan)
raw_input('Finish?')
def solve_tamp(env):
viewer = env.GetViewer() is not None
problem = PROBLEM(env)
robot = env.GetRobots()[0]
set_base_values(robot, (-.75, .2, -math.pi/2))
initialize_openrave(env, ARM)
manipulator = robot.GetActiveManipulator()
cspace = CSpace.robot_arm(manipulator)
base_manip = interfaces.BaseManipulation(robot, plannername=None, maxvelmult=None)
bodies = {obj: env.GetKinBody(obj) for obj in problem.object_names}
geom_hashes = {body.GetKinematicsGeometryHash() for body in bodies.values()}
assert len(geom_hashes) == 1 # NOTE - assuming all objects has the same geometry
all_bodies = bodies.values()
body1 = all_bodies[-1]
body2 = all_bodies[-2] if len(bodies) >= 2 else body1
grasps = get_grasps(env, robot, body1, USE_GRASP_APPROACH, USE_GRASP_TYPE)[:1]
poses = problem.known_poses if problem.known_poses else []
open_gripper2(manipulator)
initial_manip = get_arm_conf(manipulator, Config(get_full_config(robot)))
def enable_all(enable):
for body in all_bodies:
body.Enable(enable)
####################
def cfree_pose_pose(pose1, pose2):
body1.Enable(True)
set_pose(body1, pose1.value)
body2.Enable(True)
set_pose(body2, pose2.value)
return not env.CheckCollision(body1, body2)
def cfree_gtraj_pose(gt, p):
return cfree_mtraj_pose(gt, p) and cfree_mtraj_grasp_pose(gt, gt.grasp, p)
####################
def cfree_mtraj_grasp(mt, g):
enable_all(False)
body1.Enable(True)
for conf in mt.path():
set_manipulator_values(manipulator, conf) # NOTE - can also grab
set_pose(body1, object_trans_from_manip_trans(get_trans(manipulator), g.grasp_trans))
if env.CheckCollision(body1):
print 'cfree_mtraj_grasp'
return False
return True
def cfree_mtraj_pose(mt, p):
enable_all(False)
body2.Enable(True)
set_pose(body2, p.value)
for conf in mt.path():
set_manipulator_values(manipulator, conf)
if env.CheckCollision(robot, body2):
print 'cfree_mtraj_pose'
return False
return True
def cfree_mtraj_grasp_pose(mt, g, p):
enable_all(False)
body1.Enable(True)
body2.Enable(True)
set_pose(body2, p.value)
for conf in mt.path():
set_manipulator_values(manipulator, conf)
set_pose(body1, object_trans_from_manip_trans(get_trans(manipulator), g.grasp_trans))
if env.CheckCollision(body1, body2):
print 'cfree_mtraj_grasp_pose'
return False
return True
####################
def sample_grasp_traj(pose, grasp):
enable_all(False)
body1.Enable(True)
set_pose(body1, pose.value)
manip_trans, approach_vector = manip_from_pose_grasp(pose, grasp)
grasp_config = inverse_kinematics_helper(env, robot, manip_trans)
if grasp_config is None: return
set_manipulator_values(manipulator, grasp_config)
robot.Grab(body1)
grasp_traj = workspace_traj_helper(base_manip, approach_vector)
robot.Release(body1)
if grasp_traj is None: return
grasp_traj.grasp = grasp
yield [(Config(grasp_traj.end()), grasp_traj)]
def sample_manip_motion(mq1, mq2):
enable_all(False)
set_manipulator_values(manipulator, mq1.value)
mt = motion_plan(env, cspace, mq2.value, self_collisions=True)
if not mt: return
yield [(mt,)]
####################
cond_streams = [
# Pick/place trajectory
EasyListGenStream(inputs=[P, G], outputs=[MQ, GT], conditions=[],
effects=[GraspMotion(P, G, MQ, GT)], generator=sample_grasp_traj),
# Move trajectory
EasyListGenStream(inputs=[MQ, MQ2], outputs=[MT], conditions=[],
effects=[ManipMotion(MQ, MQ2, MT)], generator=sample_manip_motion, order=1, max_level=0),
# Pick/place collisions
EasyTestStream(inputs=[P, P2], conditions=[], effects=[CFreePosePose(P, P2)],
test=cfree_pose_pose, eager=True),
EasyTestStream(inputs=[GT, P], conditions=[], effects=[CFreeGTrajPose(GT, P)],
test=cfree_gtraj_pose),
# Move collisions
EasyTestStream(inputs=[MT, P], conditions=[], effects=[CFreeMTrajPose(MT, P)],
test=cfree_mtraj_pose),
EasyTestStream(inputs=[MT, G], conditions=[], effects=[CFreeMTrajGrasp(MT, G)],
test=cfree_mtraj_grasp),
EasyTestStream(inputs=[MT, G, P], conditions=[], effects=[CFreeMTrajGraspPose(MT, G, P)],
test=cfree_mtraj_grasp_pose),
]
####################
constants = map(GRASP, grasps) + map(POSE, poses)
initial_atoms = [
ManipEq(initial_manip),
HandEmpty(),
] + [
PoseEq(obj, pose) for obj, pose in problem.initial_poses.iteritems()
]
goal_formula = And(ManipEq(initial_manip), *(PoseEq(obj, pose) for obj, pose in problem.goal_poses.iteritems()))
stream_problem = STRIPStreamProblem(initial_atoms, goal_formula, actions + axioms, cond_streams, constants)
if viewer: raw_input('Start?')
search_fn = get_fast_downward('eager', max_time=10)
#solve = lambda: incremental_planner(stream_problem, search=search_fn, frequency=1, waves=True, debug=False)
solve = lambda: simple_focused(stream_problem, search=search_fn, max_level=INF, shared=False, debug=False, verbose=False)
env.Lock()
plan, universe = solve()
env.Unlock()
print SEPARATOR
plan = convert_plan(plan)
if plan is not None:
print 'Success'
for i, (action, args) in enumerate(plan):
print i+1, action, args
else:
print 'Failure'
####################
def step_path(traj):
#for j, conf in enumerate(traj.path()):
for j, conf in enumerate([traj.end()]):
set_manipulator_values(manipulator, conf)
raw_input('%s/%s) Step?'%(j, len(traj.path())))
if viewer and plan is not None:
print SEPARATOR
# Resets the initial state
set_manipulator_values(manipulator, initial_manip.value)
for obj, pose in problem.initial_poses.iteritems():
set_pose(bodies[obj], pose.value)
for i, (action, args) in enumerate(plan):
raw_input('\n%s/%s) Next?'%(i, len(plan)))
if action.name == 'move':
mq1, mq2, mt = args
step_path(mt)
elif action.name == 'pick':
o, p, g, mq, gt = args
step_path(gt.reverse())
robot.Grab(bodies[o])
step_path(gt)
elif action.name == 'place':
o, p, g, mq, gt = args
step_path(gt.reverse())
robot.Release(bodies[o])
step_path(gt)
else:
raise ValueError(action.name)
env.UpdatePublishedBodies()
raw_input('Finish?')
def solve_tamp(env):
viewer = env.GetViewer() is not None
#problem = dantam(env)
problem = dantam2(env)
#problem = move_several_4(env)
robot = env.GetRobots()[0]
set_base_values(robot, (-.75, .2, -math.pi/2))
initialize_openrave(env, 'leftarm')
manipulator = robot.GetActiveManipulator()
cspace = CSpace.robot_arm(manipulator)
base_manip = interfaces.BaseManipulation(robot, plannername=None, maxvelmult=None)
#USE_GRASP_APPROACH = GRASP_APPROACHES.SIDE
USE_GRASP_APPROACH = GRASP_APPROACHES.TOP
#USE_GRASP_TYPE = GRASP_TYPES.TOUCH
USE_GRASP_TYPE = GRASP_TYPES.GRASP
bodies = {obj: env.GetKinBody(obj) for obj in problem.object_names}
geom_hashes = {body.GetKinematicsGeometryHash() for body in bodies.values()}
assert len(geom_hashes) == 1 # NOTE - assuming all objects has the same geometry
all_bodies = bodies.values()
body1 = all_bodies[-1]
body2 = all_bodies[-2] if len(bodies) >= 2 else body1
grasps = get_grasps(env, robot, body1, USE_GRASP_APPROACH, USE_GRASP_TYPE)[:1]
poses = problem.known_poses if problem.known_poses else []
##################################################
def enable_all(enable):
for body in all_bodies:
body.Enable(enable)
def collision_free(pose1, pose2):
body1.Enable(True)
set_pose(body1, pose1.value)
body2.Enable(True)
set_pose(body2, pose2.value)
return not env.CheckCollision(body1, body2)
def grasp_env_cfree(mt, g):
enable_all(False) # TODO - base config?
body1.Enable(True)
for conf in mt.path():
set_manipulator_values(manipulator, conf) # NOTE - can also grab
set_pose(body1, object_trans_from_manip_trans(get_trans(manipulator), g.grasp_trans))
if env.CheckCollision(body1):
return False
return True
def grasp_pose_cfree(mt, g, p):
enable_all(False) # TODO - base config?
body1.Enable(True)
body2.Enable(True)
set_pose(body2, p.value)
for conf in mt.path():
set_manipulator_values(manipulator, conf)
set_pose(body1, object_trans_from_manip_trans(get_trans(manipulator), g.grasp_trans))
if env.CheckCollision(body1, body2):
return False
return True
##################################################
"""
class CollisionStream(Stream): # TODO - could make an initial state version of this that doesn't need pose2
def get_values(self, **kwargs):
self.enumerated = True
pose1, pose2 = map(get_value, self.inputs)
if collision_free(pose1, pose2):
return [PoseCFree(pose1, pose2)]
return []
#return [Movable()] # NOTE - only make movable when it fails a collision check
CheckedInitial = Pred(POSE) # How should this be handled? The planner will need to revisit on the next state anyways
class EnvCollisionStream(Stream): # NOTE - I could also make an environment OBJECT which I mutate. I'm kind of doing that now
movable = []
def get_values(self, **kwargs):
self.enumerated = True
pose, = map(get_value, self.inputs)
results = [CheckedInitial(pose)]
for obj, pose2 in problem.initial_poses.iteritems():
if obj not in self.movable and collision_free(pose, pose2):
pass
#results.append(PoseCFree(pose, pose2))
else:
self.movable.append(obj)
results.append(PoseEq(obj, pose2))
#results.append(Movable(obj))
if results:
pass # NOTE - I could make this fail if there is a collision
# I could prevent the binding by directly adding CheckedInitial to the universe
# In general, I probably can just mutate the problem however I see fit here
return results
"""
##################################################
# NOTE - can do pose, approach manip, true approach traj, motion plan
# NOTE - can make something that produces approach trajectories
def get_manip_vector(pose, grasp):
manip_trans, approach_vector = manip_from_pose_grasp(pose, grasp)
#enable_all(False)
#if manipulator.CheckEndEffectorCollision(manip_trans):
# return None
return ManipVector(manip_trans, approach_vector)
def sample_ik(pose, grasp, base_conf): # TODO - make this return the grasp
enable_all(False)
set_base_values(robot, base_conf.value)
body1.Enable(True)
set_pose(body1, pose.value)
manip_vector = get_manip_vector(pose, grasp)
grasp_config = inverse_kinematics_helper(env, robot, manip_vector.manip_trans) # NOTE - maybe need to find all IK solutions
#print manipulator.CheckEndEffectorCollision(manip_trans)
if grasp_config is not None:
yield [Config(grasp_config)]
#traj = workspace_traj_helper(base_manip, approach_vector)
def sample_grasp_traj(pose, grasp, base_conf):
enable_all(False)
set_base_values(robot, base_conf.value)
body1.Enable(True)
set_pose(body1, pose.value)
manip_vector = get_manip_vector(pose, grasp)
grasp_config = inverse_kinematics_helper(env, robot, manip_vector.manip_trans) # NOTE - maybe need to find all IK solutions
if grasp_config is None: return
set_manipulator_values(manipulator, grasp_config)
grasp_traj = workspace_traj_helper(base_manip, manip_vector.approach_vector)
if grasp_config is None: return
yield [(Config(grasp_traj.end()), grasp_traj)]
##################################################
# NOTE - can either include the held object in the traj or have a special condition that not colliding
def sample_arm_traj(mq1, mq2, bq): # TODO - need to add holding back in
yield None
#enable_all(False)
#with robot:
# set_base_values(robot, bq.value)
# pass
# TODO - does it make sense to make a new stream for the biasing or to continuously just pass things
# I suppose I could cache the state or full plan as context
class MotionStream(Stream): # TODO - maybe make this produce the correct values
num = 0
#def get_values(self, **kwargs):
# self.enumerated = True
# mq1, mq2, bq = map(get_value, self.inputs)
# #mt = None
# mt = MotionStream.num
# MotionStream.num += 1 # Ensures all are unique
# return [ManipMotion(mq1, mq2, bq, mt)]
def sample_motion_plan(self, mq1, mq2, bq):
set_manipulator_values(manipulator, mq1.value)
set_base_values(robot, bq.value)
return motion_plan(env, cspace, mq2.value, self_collisions=True)
def get_values(self, universe, dependent_atoms=set(), **kwargs):
mq1, mq2, bq = map(get_value, self.inputs)
collision_atoms = filter(lambda atom: atom.predicate in [MTrajGraspCFree, MTrajPoseCFree], dependent_atoms)
collision_params = {atom: atom.args[0] for atom in collision_atoms}
grasp = None
for atom in collision_atoms:
if atom.predicate is MTrajGraspCFree:
assert grasp is None # Can't have two grasps
_, grasp = map(get_value, atom.args)
placed = []
for atom in collision_atoms:
if atom.predicate is MTrajPoseCFree:
_, pose = map(get_value, atom.args)
placed.append(pose)
#placed, grasp = [], None
print grasp, placed
if placed or grasp:
assert len(placed) <= len(all_bodies) # How would I handle many constraints on the same traj?
enable_all(False)
for b, p in zip(all_bodies, placed):
b.Enable(True)
set_pose(b, p.value)
if grasp:
assert grasp is None or len(placed) <= len(all_bodies)-1
set_pose(body1, object_trans_from_manip_trans(get_trans(manipulator), grasp.grasp_trans))
robot.Grab(body1)
mt = self.sample_motion_plan(mq1, mq2, bq)
if grasp: robot.Release(body1)
if mt:
self.enumerated = True # NOTE - if satisfies all constraints then won't need another. Not true. What if called with different grasps...
# TODO - could always hash this trajectory for the current set of constraints
bound_collision_atoms = [atom.instantiate({collision_params[atom]: MTRAJ(mt)}) for atom in collision_atoms]
#bound_collision_atoms = []
return [ManipMotion(mq1, mq2, bq, mt)] + bound_collision_atoms
raise ValueError()
enable_all(False)
mt = self.sample_motion_plan(mq1, mq2, bq)
if mt:
return [ManipMotion(mq1, mq2, bq, mt)]
return []
##################################################
cond_streams = [
#MultiEasyGenStream(inputs=[O], outputs=[P], conditions=[], effects=[LegalPose(O, P)], generator=sample_poses),
#EasyGenStream(inputs=[MQ, MQ2, BQ], outputs=[MT], conditions=[],
# effects=[ManipMotion(MQ, MQ2, BQ, MT)], generator=sample_arm_traj),
ClassStream(inputs=[MQ, MQ2, BQ], outputs=[MT], conditions=[],
effects=[ManipMotion(MQ, MQ2, BQ, MT)], StreamClass=MotionStream, order=1, max_level=0),
#MultiEasyGenStream(inputs=[P, G, BQ], outputs=[MQ], conditions=[],
# effects=[Kin(P, G, BQ, MQ)], generator=sample_ik),
EasyListGenStream(inputs=[P, G, BQ], outputs=[MQ, GT], conditions=[],
effects=[GraspTraj(P, G, BQ, MQ, GT)], generator=sample_grasp_traj),
#EasyTestStream(inputs=[O, P, T], conditions=[], effects=[CFree(O, P, T)],
# test=collision_free, eager=True),
EasyTestStream(inputs=[P, P2], conditions=[], effects=[PoseCFree(P, P2)],
test=collision_free, eager=True),
#ClassStream(inputs=[P, P2], conditions=[], outputs=[],
# effects=[PoseCFree(P, P2)], StreamClass=CollisionStream, eager=True),
EasyTestStream(inputs=[MT, P], conditions=[], effects=[MTrajPoseCFree(MT, P)],
test=lambda mt, p: True, eager=True),
EasyTestStream(inputs=[MT, G], conditions=[], effects=[MTrajGraspCFree(MT, G)],
test=lambda mt, g: True, eager=True),
EasyTestStream(inputs=[MT, G, P], conditions=[], effects=[MTrajGraspPoseCFree(MT, G, P)],
test=lambda mt, g, p: True, eager=True),
#ClassStream(inputs=[P], conditions=[], outputs=[],
# effects=[CheckedInitial(P)], StreamClass=EnvCollisionStream),
]
##################################################
constants = map(GRASP, grasps) + map(POSE, poses)
initial_full = Config(get_full_config(robot))
initial_base = get_base_conf(initial_full)
initial_manip = get_arm_conf(manipulator, initial_full)
initial_atoms = [
BaseEq(initial_base),
ManipEq(initial_manip),
HandEmpty(),
] + [
PoseEq(obj, pose) for obj, pose in problem.initial_poses.iteritems()
]
goal_formula = And(ManipEq(initial_manip), *(PoseEq(obj, pose) for obj, pose in problem.goal_poses.iteritems()))
stream_problem = STRIPStreamProblem(initial_atoms, goal_formula, operators, cond_streams, constants)
if viewer: raw_input('Start?')
search_fn = get_fast_downward('eager')
#plan, universe = incremental_planner(stream_problem, search=search_fn, frequency=INF, waves=True, debug=False) # 1 | 20 | 100 | INF
#plan, _ = focused_planner(stream_problem, search=search_fn, frequency=1, waves=True, debug=False) # 1 | 20 | 100 | INF
#plan, universe = simple_focused(stream_problem, search=search_fn, max_level=INF, debug=False, verbose=False) # 1 | 20 | 100 | INF
#plan, _ = plan_focused(stream_problem, search=search_fn, debug=False) # 1 | 20 | 100 | INF
from misc.profiling import run_profile, str_profile
#solve = lambda: incremental_planner(stream_problem, search=search_fn, frequency=INF, waves=True, debug=False)
solve = lambda: simple_focused(stream_problem, search=search_fn, max_level=INF, shared=False, debug=False, verbose=True)
#with env:
env.Lock()
(plan, universe), prof = run_profile(solve)
env.Unlock()
print SEPARATOR
universe.print_domain_statistics()
universe.print_statistics()
print SEPARATOR
print str_profile(prof)
print SEPARATOR
plan = convert_plan(plan)
if plan is not None:
print 'Success'
for i, (action, args) in enumerate(plan):
print i+1, action, args
else:
print 'Failure'
##################################################
def step_path(traj):
#for j, conf in enumerate(traj.path()):
for j, conf in enumerate([traj.end()]):
set_manipulator_values(manipulator, conf)
raw_input('%s/%s) Step?'%(j, len(traj.path())))
if viewer and plan is not None:
print SEPARATOR
# Resets the initial state
open_gripper2(manipulator)
set_base_values(robot, initial_base.value)
set_manipulator_values(manipulator, initial_manip.value)
for obj, pose in problem.initial_poses.iteritems():
set_pose(bodies[obj], pose.value)
for i, (action, args) in enumerate(plan):
raw_input('\n%s/%s) Next?'%(i, len(plan)))
if action.name == 'move_arm':
mq1, mq2, bq, mt = args
#set_manipulator_values(manipulator, mq2.value)
step_path(mt)
elif action.name == 'pick':
#o, p, q, mq, bq = args
o, p, g, mq, bq, gt = args
step_path(gt.reverse())
#grasp_gripper2(manipulator, g) # NOTE - g currently isn't a real grasp
robot.Grab(bodies[o])
step_path(gt)
elif action.name == 'place':
#o, p, q, mq, bq = args
o, p, g, mq, bq, gt = args
step_path(gt.reverse())
robot.Release(bodies[o])
#open_gripper2(manipulator)
step_path(gt)
else:
raise ValueError(action.name)
env.UpdatePublishedBodies()
raw_input('Finish?')
def feasible_pick_place(env, robot, obj, grasps, approach_config, do_motion_planning):
t0 = time()
base_manip = interfaces.BaseManipulation(robot, plannername=None, maxvelmult=None)
#base_manip = interfaces.BaseManipulation(robot, plannername=None, maxvelmult=None)
print time() - t0
#t0 = time()
#task_manip = interfaces.TaskManipulation(robot, plannername=None, maxvelmult=None, graspername=None)
#task_manip = interfaces.TaskManipulation(robot, plannername=None, maxvelmult=None, graspername=None)
#print time() - t0
#print
# Slight overhead but it appears you can just keep creating them
#0.000910043716431
#0.000546932220459
pose = Pose(get_pose(obj))
#env.Remove(obj) # NOTE - I'm just testing this now
for grasp in grasps:
manip_trans, approach_vector = manip_from_pose_grasp(pose, grasp)
set_config(robot, approach_config, robot.GetActiveManipulator().GetArmIndices())
if env.CheckCollision(robot) or robot.CheckSelfCollision():
print 'Collision failure'
#rospy.loginfo("Failed collision")
continue
#DrawAxes(env, manip_trans) # TODO - OpenRAVE bug
#print grasp.grasp_trans
#handles = draw_axes(env, get_trans(obj))
#handles = draw_axes(env, manip_trans)
#raw_input('Continue?')
#grasp_config = inverse_kinematics_helper(env, robot, manip_trans) # NOTE - maybe need to find all IK solutions
grasp_config = solve_inverse_kinematics_exhaustive(env, robot, manip_trans)
if grasp_config is None:
print 'IK failure'
#rospy.loginfo("Failed IK")
continue
set_config(robot, grasp_config, get_active_arm_indices(robot))
#approach_config = get_full_config(robot)
traj, arm_traj = None, None
if do_motion_planning:
#traj = vector_traj_helper(env, robot, approach_vector)
traj = workspace_traj_helper(base_manip, approach_vector)
if traj is None:
print 'Vec traj failure'
continue
set_config(robot, traj.end(), get_active_arm_indices(robot))
#vector_config = oracle.get_robot_config()
arm_traj = motion_plan(env, CSpace.robot_arm(get_manipulator(robot)), approach_config, self_collisions=True)
#arm_traj = manip_traj_helper(base_manip, approach_config, max_iterations=25, max_tries=2)
if arm_traj is None:
print 'Approach traj failure'
continue
# TODO - need to do reverse trajectory
return grasp_config, traj, arm_traj
return None
def process_move_head(robot, q1, q2):
cspace = CSpace.robot_manipulator(robot, 'head')
h_traj = PathTrajectory(cspace, [q1.value, q2.value])
h_traj.traj()
return [h_traj]
def solve_tamp(env):
viewer = env.GetViewer() is not None
problem = PROBLEM(env)
robot = env.GetRobots()[0]
set_base_values(robot, (-.75, .2, -math.pi / 2))
initialize_openrave(env, ARM)
manipulator = robot.GetActiveManipulator()
cspace = CSpace.robot_arm(manipulator)
base_manip = interfaces.BaseManipulation(robot,
plannername=None,
maxvelmult=None)
bodies = {obj: env.GetKinBody(obj) for obj in problem.object_names}
geom_hashes = {
body.GetKinematicsGeometryHash()
for body in bodies.values()
}
assert len(
geom_hashes) == 1 # NOTE - assuming all objects has the same geometry
all_bodies = bodies.values()
body1 = all_bodies[-1]
body2 = all_bodies[-2] if len(bodies) >= 2 else body1
grasps = get_grasps(env, robot, body1, USE_GRASP_APPROACH,
USE_GRASP_TYPE)[:1]
poses = problem.known_poses if problem.known_poses else []
open_gripper2(manipulator)
initial_manip = get_arm_conf(manipulator, Config(get_full_config(robot)))
def enable_all(enable):
for body in all_bodies:
body.Enable(enable)
####################
def cfree_pose_pose(pose1, pose2):
body1.Enable(True)
set_pose(body1, pose1.value)
body2.Enable(True)
set_pose(body2, pose2.value)
return not env.CheckCollision(body1, body2)
def cfree_gtraj_pose(gt, p):
return cfree_mtraj_pose(gt, p) and cfree_mtraj_grasp_pose(
gt, gt.grasp, p)
####################
def cfree_mtraj_grasp(mt, g):
enable_all(False)
body1.Enable(True)
for conf in mt.path():
set_manipulator_values(manipulator, conf) # NOTE - can also grab
set_pose(
body1,
object_trans_from_manip_trans(get_trans(manipulator),
g.grasp_trans))
if env.CheckCollision(body1):
return False
return True
def cfree_mtraj_pose(mt, p):
enable_all(False)
body2.Enable(True)
set_pose(body2, p.value)
for conf in mt.path():
set_manipulator_values(manipulator, conf)
if env.CheckCollision(body2):
return False
return True
def cfree_mtraj_grasp_pose(mt, g, p):
enable_all(False)
body1.Enable(True)
body2.Enable(True)
set_pose(body2, p.value)
for conf in mt.path():
set_manipulator_values(manipulator, conf)
set_pose(
body1,
object_trans_from_manip_trans(get_trans(manipulator),
g.grasp_trans))
if env.CheckCollision(body1, body2):
return False
return True
####################
def sample_grasp_traj(pose, grasp):
enable_all(False)
body1.Enable(True)
set_pose(body1, pose.value)
manip_trans, approach_vector = manip_from_pose_grasp(pose, grasp)
grasp_config = inverse_kinematics_helper(env, robot, manip_trans)
if grasp_config is None: return
set_manipulator_values(manipulator, grasp_config)
robot.Grab(body1)
grasp_traj = workspace_traj_helper(base_manip, approach_vector)
robot.Release(body1)
if grasp_traj is None: return
grasp_traj.grasp = grasp
yield [(Config(grasp_traj.end()), grasp_traj)]
class MotionStream(Stream):
def get_values(self, universe, dependent_atoms=set(), **kwargs):
mq1, mq2 = map(get_value, self.inputs)
collision_atoms = filter(
lambda atom: atom.predicate in
[CFreeMTrajGrasp, CFreeMTrajPose], dependent_atoms)
collision_params = {atom: atom.args[0] for atom in collision_atoms}
grasp = None
for atom in collision_atoms:
if atom.predicate is CFreeMTrajGrasp:
assert grasp is None # Can't have two grasps
_, grasp = map(get_value, atom.args)
placed = []
for atom in collision_atoms:
if atom.predicate is CFreeMTrajPose:
_, pose = map(get_value, atom.args)
placed.append(pose)
#placed, grasp = [], None
#print grasp, placed
if placed or grasp:
assert len(placed) <= len(all_bodies)
enable_all(False)
for b, p in zip(all_bodies, placed):
b.Enable(True)
set_pose(b, p.value)
if grasp:
assert grasp is None or len(placed) <= len(all_bodies) - 1
set_pose(
body1,
object_trans_from_manip_trans(get_trans(manipulator),
grasp.grasp_trans))
robot.Grab(body1)
set_manipulator_values(manipulator, mq1.value)
mt = motion_plan(env, cspace, mq2.value, self_collisions=True)
if grasp: robot.Release(body1)
if mt:
self.enumerated = True # NOTE - if satisfies all constraints then won't need another. Not true. What if called with different grasps...
# TODO - could always hash this trajectory for the current set of constraints
bound_collision_atoms = [
atom.instantiate({collision_params[atom]: MTRAJ(mt)})
for atom in collision_atoms
]
#bound_collision_atoms = []
return [ManipMotion(mq1, mq2, mt)] + bound_collision_atoms
raise ValueError()
enable_all(False)
set_manipulator_values(manipulator, mq1.value)
mt = motion_plan(env, cspace, mq2.value, self_collisions=True)
if mt:
return [ManipMotion(mq1, mq2, mt)]
return []
####################
cond_streams = [
# Pick/place trajectory
EasyListGenStream(inputs=[P, G],
outputs=[MQ, GT],
conditions=[],
effects=[GraspMotion(P, G, MQ, GT)],
generator=sample_grasp_traj),
# Move trajectory
ClassStream(inputs=[MQ, MQ2],
outputs=[MT],
conditions=[],
effects=[ManipMotion(MQ, MQ2, MT)],
StreamClass=MotionStream,
order=1,
max_level=0),
# Pick/place collisions
EasyTestStream(inputs=[P, P2],
conditions=[],
effects=[CFreePosePose(P, P2)],
test=cfree_pose_pose,
eager=True),
EasyTestStream(inputs=[GT, P],
conditions=[],
effects=[CFreeGTrajPose(GT, P)],
test=cfree_gtraj_pose),
# Move collisions
EasyTestStream(inputs=[MT, P],
conditions=[],
effects=[CFreeMTrajPose(MT, P)],
test=cfree_mtraj_pose),
EasyTestStream(inputs=[MT, G],
conditions=[],
effects=[CFreeMTrajGrasp(MT, G)],
test=cfree_mtraj_grasp),
EasyTestStream(inputs=[MT, G, P],
conditions=[],
effects=[CFreeMTrajGraspPose(MT, G, P)],
test=cfree_mtraj_grasp_pose),
]
####################
constants = map(GRASP, grasps) + map(POSE, poses)
initial_atoms = [
ManipEq(initial_manip),
HandEmpty(),
] + [PoseEq(obj, pose) for obj, pose in problem.initial_poses.iteritems()]
goal_formula = And(
ManipEq(initial_manip),
*(PoseEq(obj, pose) for obj, pose in problem.goal_poses.iteritems()))
stream_problem = STRIPStreamProblem(initial_atoms, goal_formula,
actions + axioms, cond_streams,
constants)
if viewer: raw_input('Start?')
search_fn = get_fast_downward('eager')
#plan, universe = incremental_planner(stream_problem, search=search_fn, frequency=INF, waves=True, debug=False) # 1 | 20 | 100 | INF
#plan, _ = focused_planner(stream_problem, search=search_fn, frequency=1, waves=True, debug=False) # 1 | 20 | 100 | INF
#plan, universe = simple_focused(stream_problem, search=search_fn, max_level=INF, debug=False, verbose=False) # 1 | 20 | 100 | INF
#plan, _ = plan_focused(stream_problem, search=search_fn, debug=False) # 1 | 20 | 100 | INF
from misc.profiling import run_profile, str_profile
#solve = lambda: incremental_planner(stream_problem, search=search_fn, frequency=INF, waves=True, debug=False)
solve = lambda: simple_focused(stream_problem,
search=search_fn,
max_level=INF,
shared=False,
debug=False,
verbose=False)
#with env:
env.Lock()
(plan, universe), prof = run_profile(solve)
env.Unlock()
print SEPARATOR
universe.print_domain_statistics()
universe.print_statistics()
print SEPARATOR
print str_profile(prof)
print SEPARATOR
plan = convert_plan(plan)
if plan is not None:
print 'Success'
for i, (action, args) in enumerate(plan):
print i + 1, action, args
else:
print 'Failure'
####################
def step_path(traj):
#for j, conf in enumerate(traj.path()):
for j, conf in enumerate([traj.end()]):
set_manipulator_values(manipulator, conf)
raw_input('%s/%s) Step?' % (j, len(traj.path())))
if viewer and plan is not None:
print SEPARATOR
# Resets the initial state
set_manipulator_values(manipulator, initial_manip.value)
for obj, pose in problem.initial_poses.iteritems():
set_pose(bodies[obj], pose.value)
for i, (action, args) in enumerate(plan):
raw_input('\n%s/%s) Next?' % (i, len(plan)))
if action.name == 'move':
mq1, mq2, mt = args
step_path(mt)
elif action.name == 'pick':
o, p, g, mq, gt = args
step_path(gt.reverse())
robot.Grab(bodies[o])
step_path(gt)
elif action.name == 'place':
o, p, g, mq, gt = args
step_path(gt.reverse())
robot.Release(bodies[o])
step_path(gt)
else:
raise ValueError(action.name)
env.UpdatePublishedBodies()
raw_input('Finish?')
