def main(_):
robot = ROBOT_CLASS_MAP[FLAGS.robot_type]
motor_control_mode = MOTOR_CONTROL_MODE_MAP[FLAGS.motor_control_mode]
env = env_builder.build_regular_env(robot,
motor_control_mode=motor_control_mode,
enable_rendering=True,
on_rack=FLAGS.on_rack,
wrap_trajectory_generator=False)
action_low, action_high = env.action_space.low, env.action_space.high
action_median = (action_low + action_high) / 2.
dim_action = action_low.shape[0]
action_selector_ids = []
for dim in range(dim_action):
action_selector_id = p.addUserDebugParameter(
paramName='dim{}'.format(dim),
rangeMin=action_low[dim],
rangeMax=action_high[dim],
startValue=action_median[dim])
action_selector_ids.append(action_selector_id)
if FLAGS.video_dir:
log_id = p.startStateLogging(p.STATE_LOGGING_VIDEO_MP4,
FLAGS.video_dir)
for _ in tqdm(range(800)):
action = np.zeros(dim_action)
for dim in range(dim_action):
action[dim] = env.pybullet_client.readUserDebugParameter(
action_selector_ids[dim])
env.step(action)
if FLAGS.video_dir:
p.stopStateLogging(log_id)
def main(_):
logging.info(
"WARNING: this code executes low-level controller on the robot.")
logging.info("Make sure the robot is hang on rack before proceeding.")
input("Press enter to continue...")
# Construct sim env and real robot
sim_env = env_builder.build_regular_env(
robot_class=a1.A1,
motor_control_mode=robot_config.MotorControlMode.POSITION,
on_rack=True,
enable_rendering=True,
wrap_trajectory_generator=False)
real_env = env_builder.build_regular_env(
robot_class=a1_robot.A1Robot,
motor_control_mode=robot_config.MotorControlMode.POSITION,
on_rack=False,
enable_rendering=False,
wrap_trajectory_generator=False)
# Add debug sliders
action_low, action_high = sim_env.action_space.low, sim_env.action_space.high
dim_action = action_low.shape[0]
action_selector_ids = []
robot_motor_angles = real_env.robot.GetMotorAngles()
for dim in range(dim_action):
action_selector_id = p.addUserDebugParameter(
paramName='dim{}'.format(dim),
rangeMin=action_low[dim],
rangeMax=action_high[dim],
startValue=robot_motor_angles[dim])
action_selector_ids.append(action_selector_id)
# Visualize debug slider in sim
for _ in range(10000):
# Get user action input
action = np.zeros(dim_action)
for dim in range(dim_action):
action[dim] = sim_env.pybullet_client.readUserDebugParameter(
action_selector_ids[dim])
real_env.step(action)
sim_env.step(action)
real_env.Terminate()
def __init__(self,
action_limit=(0.75, 0.75, 0.75),
render=False,
on_rack=False):
self._env = env_builder.build_regular_env(
a1.A1,
motor_control_mode=robot_config.MotorControlMode.POSITION,
enable_rendering=render,
action_limit=action_limit,
on_rack=on_rack)
self.observation_space = self._env.observation_space
self.action_space = self._env.action_space
def main(_):
robot = ROBOT_CLASS_MAP[FLAGS.robot_type]
motor_control_mode = MOTOR_CONTROL_MODE_MAP[FLAGS.motor_control_mode]
env = env_builder.build_regular_env(robot,
motor_control_mode=motor_control_mode,
enable_rendering=True,
on_rack=FLAGS.on_rack)
env.reset()
for _ in tqdm(range(1000)):
_, _, done, _ = env.step(env.action_space.sample())
if done:
break
def main(_):
robot = ROBOT_CLASS_MAP[FLAGS.robot_type]
motor_control_mode = MOTOR_CONTROL_MODE_MAP[FLAGS.motor_control_mode]
env = env_builder.build_regular_env(robot,
motor_control_mode=motor_control_mode,
enable_rendering=True,
on_rack=FLAGS.on_rack,
wrap_trajectory_generator=False)
action_low, action_high = env.action_space.low/3., env.action_space.high/3.
action_median = (action_low + action_high) / 2.
dim_action = action_low.shape[0]
action_selector_ids = []
for dim in range(dim_action):
action_selector_id = p.addUserDebugParameter(paramName='dim{}'.format(dim),
rangeMin=action_low[dim],
rangeMax=action_high[dim],
startValue=action_median[dim])
action_selector_ids.append(action_selector_id)
action_selector_id = p.addUserDebugParameter(paramName='height',
rangeMin=0,
rangeMax=2,
startValue=1)
action_selector_ids.append(action_selector_id)
action_selector_id = p.addUserDebugParameter(paramName='angle',
rangeMin=-3.14/2,
rangeMax=3.14/2,
startValue=0)
action_selector_ids.append(action_selector_id)
if FLAGS.video_dir:
log_id = p.startStateLogging(p.STATE_LOGGING_VIDEO_MP4, FLAGS.video_dir)
#for _ in tqdm(range(800)):
last_state_vec = []
while True:
state_vec = []
rot = Rotation.from_euler('zxy', [0,0,env.pybullet_client.readUserDebugParameter(
action_selector_ids[-1])])
base = Rotation.from_quat([0.5,0.5,0.5,0.5])
final_quat = (rot*base).as_quat()
final_pos = [0,0,env.pybullet_client.readUserDebugParameter(
action_selector_ids[-2])]
env.moveRack(final_pos, final_quat)
state_vec.extend(final_pos)
state_vec.extend(final_quat)
action = np.zeros(dim_action)
for dim in range(dim_action):
action[dim] = env.pybullet_client.readUserDebugParameter(
action_selector_ids[dim])
state_vec.append(action[dim])
env.step(action)
if last_state_vec != state_vec:
print(state_vec)
last_state_vec = state_vec
if FLAGS.video_dir:
p.stopStateLogging(log_id)