modified according to comments
This commit is contained in:
Hongyi Zhou
parent
1fd4a1e848
commit
bdd51ba61f
@ -16,7 +16,8 @@ from .mujoco.hopper_throw.hopper_throw import MAX_EPISODE_STEPS_HOPPERTHROW
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from .mujoco.hopper_throw.hopper_throw_in_basket import MAX_EPISODE_STEPS_HOPPERTHROWINBASKET
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from .mujoco.reacher.reacher import ReacherEnv, MAX_EPISODE_STEPS_REACHER
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from .mujoco.walker_2d_jump.walker_2d_jump import MAX_EPISODE_STEPS_WALKERJUMP
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from .mujoco.box_pushing.box_pushing_env import BoxPushingEnv, MAX_EPISODE_STEPS_BOX_PUSHING
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from .mujoco.box_pushing.box_pushing_env import BoxPushingDense, BoxPushingTemporalSparse, \
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BoxPushingTemporalSpatialSparse, MAX_EPISODE_STEPS_BOX_PUSHING
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ALL_FANCY_MOVEMENT_PRIMITIVE_ENVIRONMENTS = {"DMP": [], "ProMP": [], "ProDMP": []}
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@ -232,10 +233,9 @@ register(
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for reward_type in ["Dense", "TemporalSparse", "TemporalSpatialSparse"]:
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register(
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id='BoxPushing{}-v0'.format(reward_type),
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entry_point='fancy_gym.envs.mujoco:BoxPushingEnv',
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entry_point='fancy_gym.envs.mujoco:BoxPushing{}'.format(reward_type),
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max_episode_steps=MAX_EPISODE_STEPS_BOX_PUSHING//10, # divided by frames skip
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kwargs={
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"reward_type": reward_type,
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"frame_skip": 10
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}
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)
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@ -7,4 +7,4 @@ from .hopper_throw.hopper_throw import HopperThrowEnv
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from .hopper_throw.hopper_throw_in_basket import HopperThrowInBasketEnv
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from .reacher.reacher import ReacherEnv
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from .walker_2d_jump.walker_2d_jump import Walker2dJumpEnv
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from .box_pushing.box_pushing_env import BoxPushingEnv
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from .box_pushing.box_pushing_env import BoxPushingDense, BoxPushingTemporalSparse, BoxPushingTemporalSpatialSparse
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@ -5,7 +5,7 @@ from gym import utils, spaces
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from gym.envs.mujoco import MujocoEnv
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from fancy_gym.envs.mujoco.box_pushing.box_pushing_utils import rot_to_quat, get_quaternion_error, rotation_distance
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from fancy_gym.envs.mujoco.box_pushing.box_pushing_utils import q_max, q_min, q_dot_max, q_torque_max
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from fancy_gym.envs.mujoco.box_pushing.box_pushing_utils import BoxPushingReward
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from fancy_gym.envs.mujoco.box_pushing.box_pushing_utils import desired_rod_quat
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import mujoco
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@ -13,7 +13,7 @@ MAX_EPISODE_STEPS_BOX_PUSHING = 1000
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BOX_POS_BOUND = np.array([[0.3, -0.45, -0.01], [0.6, 0.45, -0.01]])
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class BoxPushingEnv(MujocoEnv, utils.EzPickle):
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class BoxPushingEnvBase(MujocoEnv, utils.EzPickle):
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"""
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franka box pushing environment
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action space:
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@ -26,14 +26,18 @@ class BoxPushingEnv(MujocoEnv, utils.EzPickle):
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3. time-spatial-depend sparse reward
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"""
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def __init__(self, reward_type: str = "Dense", frame_skip: int = 10):
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def __init__(self, frame_skip: int = 10):
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utils.EzPickle.__init__(**locals())
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self._steps = 0
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self.init_qpos_box_pushing = np.array([0., 0., 0., -1.5, 0., 1.5, 0., 0., 0., 0.6, 0.45, 0.0, 1., 0., 0., 0.])
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self.init_qvel_box_pushing = np.zeros(15)
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self.frame_skip = frame_skip
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assert reward_type in ["Dense", "TemporalSparse", "TemporalSpatialSparse"], "unrecognized reward type"
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self.reward = BoxPushingReward(reward_type, q_max, q_min, q_dot_max)
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self._q_max = q_max
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self._q_min = q_min
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self._q_dot_max = q_dot_max
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self._desired_rod_quat = desired_rod_quat
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self._episode_energy = 0.
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MujocoEnv.__init__(self,
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model_path=os.path.join(os.path.dirname(__file__), "assets", "box_pushing.xml"),
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@ -68,7 +72,7 @@ class BoxPushingEnv(MujocoEnv, utils.EzPickle):
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qvel = self.data.qvel[:7].copy()
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if not unstable_simulation:
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reward = self.reward.get_reward(episode_end, box_pos, box_quat, target_pos, target_quat,
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reward = self._get_reward(episode_end, box_pos, box_quat, target_pos, target_quat,
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rod_tip_pos, rod_quat, qpos, qvel, action)
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else:
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reward = -50
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@ -96,9 +100,9 @@ class BoxPushingEnv(MujocoEnv, utils.EzPickle):
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box_target_pos = self.sample_context()
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while np.linalg.norm(box_target_pos[:2] - box_init_pos[:2]) < 0.3:
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box_target_pos = self.sample_context()
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#box_target_pos[0] = 0.4
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#box_target_pos[1] = -0.3
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#box_target_pos[-4:] = np.array([0.0, 0.0, 0.0, 1.0])
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# box_target_pos[0] = 0.4
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# box_target_pos[1] = -0.3
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# box_target_pos[-4:] = np.array([0.0, 0.0, 0.0, 1.0])
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self.model.body_pos[2] = box_target_pos[:3]
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self.model.body_quat[2] = box_target_pos[-4:]
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self.model.body_pos[3] = box_target_pos[:3]
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@ -122,6 +126,10 @@ class BoxPushingEnv(MujocoEnv, utils.EzPickle):
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quat = rot_to_quat(theta, np.array([0, 0, 1]))
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return np.concatenate([pos, quat])
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def _get_reward(self, episode_end, box_pos, box_quat, target_pos, target_quat,
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rod_tip_pos, rod_quat, qpos, qvel, action):
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raise NotImplementedError
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def _get_obs(self):
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obs = np.concatenate([
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self.data.qpos[:7].copy(), # joint position
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@ -136,6 +144,22 @@ class BoxPushingEnv(MujocoEnv, utils.EzPickle):
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])
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return obs
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def _joint_limit_violate_penalty(self, qpos, qvel, enable_pos_limit=False, enable_vel_limit=False):
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penalty = 0.
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p_coeff = 1.
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v_coeff = 1.
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# q_limit
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if enable_pos_limit:
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higher_error = qpos - self._q_max
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lower_error = self._q_min - qpos
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penalty -= p_coeff * (abs(np.sum(higher_error[qpos > self._q_max])) +
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abs(np.sum(lower_error[qpos < self._q_min])))
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# q_dot_limit
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if enable_vel_limit:
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q_dot_error = abs(qvel) - abs(self._q_dot_max)
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penalty -= v_coeff * abs(np.sum(q_dot_error[q_dot_error > 0.]))
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return penalty
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def get_body_jacp(self, name):
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id = mujoco.mj_name2id(self.model, 1, name)
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jacp = np.zeros((3, self.model.nv))
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@ -252,8 +276,89 @@ class BoxPushingEnv(MujocoEnv, utils.EzPickle):
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return q
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class BoxPushingDense(BoxPushingEnvBase):
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def __init__(self, frame_skip: int = 10):
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super(BoxPushingDense, self).__init__(frame_skip=frame_skip)
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def _get_reward(self, episode_end, box_pos, box_quat, target_pos, target_quat,
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rod_tip_pos, rod_quat, qpos, qvel, action):
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joint_penalty = self._joint_limit_violate_penalty(qpos,
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qvel,
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enable_pos_limit=True,
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enable_vel_limit=True)
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tcp_box_dist_reward = -2 * np.clip(np.linalg.norm(box_pos - rod_tip_pos), 0.05, 100)
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box_goal_pos_dist_reward = -3.5 * np.linalg.norm(box_pos - target_pos)
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box_goal_rot_dist_reward = -rotation_distance(box_quat, target_quat) / np.pi
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energy_cost = -0.0005 * np.sum(np.square(action))
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reward = joint_penalty + tcp_box_dist_reward + \
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box_goal_pos_dist_reward + box_goal_rot_dist_reward + energy_cost
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rod_inclined_angle = rotation_distance(rod_quat, self._desired_rod_quat)
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if rod_inclined_angle > np.pi / 4:
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reward -= rod_inclined_angle / (np.pi)
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return reward
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class BoxPushingTemporalSparse(BoxPushingEnvBase):
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def __init__(self, frame_skip: int = 10):
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super(BoxPushingTemporalSparse, self).__init__(frame_skip=frame_skip)
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def _get_reward(self, episode_end, box_pos, box_quat, target_pos, target_quat,
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rod_tip_pos, rod_quat, qpos, qvel, action):
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reward = 0.
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joint_penalty = self._joint_limit_violate_penalty(qpos, qvel, enable_pos_limit=True, enable_vel_limit=True)
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energy_cost = -0.0005 * np.sum(np.square(action))
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tcp_box_dist_reward = -2 * np.clip(np.linalg.norm(box_pos - rod_tip_pos), 0.05, 100)
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reward += joint_penalty + tcp_box_dist_reward + energy_cost
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rod_inclined_angle = rotation_distance(rod_quat, desired_rod_quat)
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if rod_inclined_angle > np.pi / 4:
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reward -= rod_inclined_angle / (np.pi)
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if not episode_end:
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return reward
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box_goal_dist = np.linalg.norm(box_pos - target_pos)
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box_goal_pos_dist_reward = -3.5 * box_goal_dist * 100
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box_goal_rot_dist_reward = -rotation_distance(box_quat, target_quat) / np.pi * 100
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reward += box_goal_pos_dist_reward + box_goal_rot_dist_reward
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return reward
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class BoxPushingTemporalSpatialSparse(BoxPushingEnvBase):
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def __init__(self, frame_skip: int = 10):
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super(BoxPushingTemporalSpatialSparse, self).__init__(frame_skip=frame_skip)
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def _get_reward(self, episode_end, box_pos, box_quat, target_pos, target_quat,
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rod_tip_pos, rod_quat, qpos, qvel, action):
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reward = 0.
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joint_penalty = self._joint_limit_violate_penalty(qpos, qvel, enable_pos_limit=True, enable_vel_limit=True)
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energy_cost = -0.0005 * np.sum(np.square(action))
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tcp_box_dist_reward = -2 * np.clip(np.linalg.norm(box_pos - rod_tip_pos), 0.05, 100)
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reward += joint_penalty + tcp_box_dist_reward + energy_cost
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rod_inclined_angle = rotation_distance(rod_quat, desired_rod_quat)
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if rod_inclined_angle > np.pi / 4:
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reward -= rod_inclined_angle / (np.pi)
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if not episode_end:
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return reward
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box_goal_dist = np.linalg.norm(box_pos - target_pos)
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if box_goal_dist < 0.1:
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reward += 300
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box_goal_pos_dist_reward = np.clip(- 3.5 * box_goal_dist * 100 * 3, -100, 0)
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box_goal_rot_dist_reward = np.clip(- rotation_distance(box_quat, target_quat)/np.pi * 100 * 1.5, -100, 0)
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reward += box_goal_pos_dist_reward + box_goal_rot_dist_reward
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return reward
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if __name__=="__main__":
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env = BoxPushingEnv(reward_type="dense", frame_skip=10)
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env = BoxPushingTemporalSpatialSparse(frame_skip=10)
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env.reset()
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for i in range(100):
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env.reset()
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@ -10,184 +10,44 @@ q_torque_max = np.array([90., 90., 90., 90., 12., 12., 12.])
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#
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desired_rod_quat = np.array([0.0, 1.0, 0.0, 0.0])
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def skew(x):
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"""
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Returns the skew-symmetric matrix of x
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param x: 3x1 vector
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"""
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return np.array([[0, -x[2], x[1]], [x[2], 0, -x[0]], [-x[1], x[0], 0]])
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def get_quaternion_error(curr_quat, des_quat):
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"""
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Calculates the difference between the current quaternion and the desired quaternion.
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See Siciliano textbook page 140 Eq 3.91
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:param curr_quat: current quaternion
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:param des_quat: desired quaternion
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:return: difference between current quaternion and desired quaternion
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param curr_quat: current quaternion
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param des_quat: desired quaternion
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return: difference between current quaternion and desired quaternion
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"""
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quatError = np.zeros((3, ))
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quatError[0] = (curr_quat[0] * des_quat[1] - des_quat[0] * curr_quat[1] -
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curr_quat[3] * des_quat[2] + curr_quat[2] * des_quat[3])
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quatError[1] = (curr_quat[0] * des_quat[2] - des_quat[0] * curr_quat[2] +
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curr_quat[3] * des_quat[1] - curr_quat[1] * des_quat[3])
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quatError[2] = (curr_quat[0] * des_quat[3] - des_quat[0] * curr_quat[3] -
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curr_quat[2] * des_quat[1] + curr_quat[1] * des_quat[2])
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return quatError
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return curr_quat[0] * des_quat[1:] - des_quat[0] * curr_quat[1:] - skew(des_quat[1:]) @ curr_quat[1:]
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def rotation_distance(p: np.array, q: np.array):
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"""
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p: quaternion
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q: quaternion
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Calculates the rotation angular between two quaternions
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param p: quaternion
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param q: quaternion
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theta: rotation angle between p and q (rad)
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"""
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assert p.shape == q.shape, "p and q should be quaternion"
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product = p[0] * q[0] + p[1] * q[1] + p[2] * q[2] + p[3] * q[3]
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theta = 2 * np.arccos(abs(product))
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theta = 2 * np.arccos(abs(p @ q))
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return theta
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def rot_to_quat(theta, axis):
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"""
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Converts rotation angle along an axis to quaternion
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param theta: rotation angle (rad)
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param axis: rotation axis
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return: quaternion
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"""
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quant = np.zeros(4)
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quant[0] = np.sin(theta / 2.)
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quant[1] = np.cos(theta / 2.) * axis[0]
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quant[2] = np.cos(theta / 2.) * axis[1]
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quant[3] = np.cos(theta / 2.) * axis[2]
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quant[1:] = np.cos(theta / 2.) * axis
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return quant
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class RewardBase():
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def __init__(self, q_max, q_min, q_dot_max):
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self._reward = 0.
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self._done = False
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self._q_max = q_max
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self._q_min = q_min
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self._q_dot_max = q_dot_max
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def get_reward(self, episodic_end, box_pos, box_quat, target_pos, target_quat,
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rod_tip_pos, rod_quat, qpos, qvel, action):
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raise NotImplementedError
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def _joint_limit_violate_penalty(self,
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qpos,
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qvel,
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enable_pos_limit=False,
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enable_vel_limit=False):
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penalty = 0.
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p_coeff = 1.
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v_coeff = 1.
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# q_limit
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if enable_pos_limit:
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higher_indice = np.where(qpos > self._q_max)
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lower_indice = np.where(qpos < self._q_min)
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higher_error = qpos - self._q_max
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lower_error = self._q_min - qpos
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penalty -= p_coeff * (abs(np.sum(higher_error[higher_indice])) +
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abs(np.sum(lower_error[lower_indice])))
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# q_dot_limit
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if enable_vel_limit:
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q_dot_error = abs(qvel) - abs(self._q_dot_max)
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q_dot_violate_idx = np.where(q_dot_error > 0.)
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penalty -= v_coeff * abs(np.sum(q_dot_error[q_dot_violate_idx]))
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return penalty
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class DenseReward(RewardBase):
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def __init__(self, q_max, q_min, q_dot_max):
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super(DenseReward, self).__init__(q_max, q_min, q_dot_max)
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def get_reward(self, episodic_end, box_pos, box_quat, target_pos, target_quat,
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rod_tip_pos, rod_quat, qpos, qvel, action):
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joint_penalty = self._joint_limit_violate_penalty(qpos,
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qvel,
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enable_pos_limit=True,
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enable_vel_limit=True)
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tcp_box_dist_reward = -2 * np.clip(np.linalg.norm(box_pos - rod_tip_pos), 0.05, 100)
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box_goal_pos_dist_reward = -3.5 * np.linalg.norm(box_pos - target_pos)
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box_goal_rot_dist_reward = -rotation_distance(box_quat, target_quat) / np.pi
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energy_cost = -0.0005 * np.sum(np.square(action))
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reward = joint_penalty + tcp_box_dist_reward + \
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box_goal_pos_dist_reward + box_goal_rot_dist_reward + energy_cost
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rod_inclined_angle = rotation_distance(rod_quat, desired_rod_quat)
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if rod_inclined_angle > np.pi / 4:
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reward -= rod_inclined_angle / (np.pi)
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return reward
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class TemporalSparseReward(RewardBase):
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def __init__(self, q_max, q_min, q_dot_max):
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super(TemporalSparseReward, self).__init__(q_max, q_min, q_dot_max)
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def get_reward(self, episodic_end, box_pos, box_quat, target_pos, target_quat,
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rod_tip_pos, rod_quat, qpos, qvel, action):
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reward = 0.
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joint_penalty = self._joint_limit_violate_penalty(qpos, qvel, enable_pos_limit=True, enable_vel_limit=True)
|
||||
energy_cost = -0.0005 * np.sum(np.square(action))
|
||||
tcp_box_dist_reward = -2 * np.clip(np.linalg.norm(box_pos - rod_tip_pos), 0.05, 100)
|
||||
reward += joint_penalty + tcp_box_dist_reward + energy_cost
|
||||
rod_inclined_angle = rotation_distance(rod_quat, desired_rod_quat)
|
||||
|
||||
if rod_inclined_angle > np.pi / 4:
|
||||
reward -= rod_inclined_angle / (np.pi)
|
||||
|
||||
if not episodic_end:
|
||||
return reward
|
||||
|
||||
box_goal_dist = np.linalg.norm(box_pos - target_pos)
|
||||
|
||||
box_goal_pos_dist_reward = -3.5 * box_goal_dist * 100
|
||||
box_goal_rot_dist_reward = -rotation_distance(box_quat, target_quat) / np.pi * 100
|
||||
|
||||
reward += box_goal_pos_dist_reward + box_goal_rot_dist_reward
|
||||
|
||||
return reward
|
||||
|
||||
|
||||
|
||||
|
||||
class TemporalSpatialSparseReward(RewardBase):
|
||||
def __init__(self, q_max, q_min, q_dot_max):
|
||||
super(TemporalSpatialSparseReward, self).__init__(q_max, q_min, q_dot_max)
|
||||
|
||||
def get_reward(self, episodic_end, box_pos, box_quat, target_pos, target_quat,
|
||||
rod_tip_pos, rod_quat, qpos, qvel, action):
|
||||
reward = 0.
|
||||
joint_penalty = self._joint_limit_violate_penalty(qpos, qvel, enable_pos_limit=True, enable_vel_limit=True)
|
||||
energy_cost = -0.0005 * np.sum(np.square(action))
|
||||
tcp_box_dist_reward = -2 * np.clip(np.linalg.norm(box_pos - rod_tip_pos), 0.05, 100)
|
||||
reward += joint_penalty + tcp_box_dist_reward + energy_cost
|
||||
rod_inclined_angle = rotation_distance(rod_quat, desired_rod_quat)
|
||||
|
||||
if rod_inclined_angle > np.pi / 4:
|
||||
reward -= rod_inclined_angle / (np.pi)
|
||||
|
||||
if not episodic_end:
|
||||
return reward
|
||||
|
||||
box_goal_dist = np.linalg.norm(box_pos - target_pos)
|
||||
|
||||
if box_goal_dist < 0.1:
|
||||
reward += 300
|
||||
box_goal_pos_dist_reward = np.clip(- 3.5 * box_goal_dist * 100 * 3, -100, 0)
|
||||
box_goal_rot_dist_reward = np.clip(- rotation_distance(box_quat, target_quat)/np.pi * 100 * 1.5, -100, 0)
|
||||
reward += box_goal_pos_dist_reward + box_goal_rot_dist_reward
|
||||
|
||||
return reward
|
||||
|
||||
|
||||
|
||||
def BoxPushingReward(reward_type, q_max, q_min, q_dot_max):
|
||||
if reward_type == 'Dense':
|
||||
return DenseReward(q_max, q_min, q_dot_max)
|
||||
elif reward_type == 'TemporalSparse':
|
||||
return TemporalSparseReward(q_max, q_min, q_dot_max)
|
||||
elif reward_type == 'TemporalSpatialSparse':
|
||||
return TemporalSpatialSparseReward(q_max, q_min, q_dot_max)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
@ -157,17 +157,17 @@ def example_fully_custom_mp(seed=1, iterations=1, render=True):
|
||||
if __name__ == '__main__':
|
||||
render = True
|
||||
# DMP
|
||||
# example_mp("HoleReacherDMP-v0", seed=10, iterations=5, render=render)
|
||||
example_mp("HoleReacherDMP-v0", seed=10, iterations=5, render=render)
|
||||
|
||||
# ProMP
|
||||
# example_mp("HoleReacherProMP-v0", seed=10, iterations=5, render=render)
|
||||
# example_mp("BoxPushingDenseProMP-v0", seed=10, iterations=50, render=render)
|
||||
example_mp("HoleReacherProMP-v0", seed=10, iterations=5, render=render)
|
||||
example_mp("BoxPushingTemporalSparseProMP-v0", seed=10, iterations=1, render=render)
|
||||
|
||||
# ProDMP
|
||||
example_mp("BoxPushingDenseProDMP-v0", seed=10, iterations=50, render=render)
|
||||
example_mp("BoxPushingDenseProDMP-v0", seed=10, iterations=1, render=render)
|
||||
|
||||
# Altered basis functions
|
||||
# obs1 = example_custom_mp("Reacher5dProMP-v0", seed=10, iterations=1, render=render)
|
||||
obs1 = example_custom_mp("Reacher5dProMP-v0", seed=10, iterations=1, render=render)
|
||||
|
||||
# Custom MP
|
||||
# example_fully_custom_mp(seed=10, iterations=1, render=render)
|
||||
example_fully_custom_mp(seed=10, iterations=1, render=render)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user