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maxEpisodeLength: 200 #200
approaching: 5
reward:
base_height_target: 0.55
only_positive_rewards: False
scales:
approaching: 5.0
lifting: 1.0
pick_up: 5.0 # 0.5
acc_penalty: -0.001
command_penalty: -1.0
command_reward: 0.25
standpick: 0.25 # no found in reward_vec_task.py
action_rate: -0.001
ee_orn: 0.05 # 0.01
base_dir: 0.25
rad_penalty: 0.0
base_ang_pen: 0.0
base_approaching: 0.01 # 0.05
grasp_base_height: 0.5 # no found in reward_vec_task.py
gripper_rate: -0.1 # -0.1
reward:
base_height_target: 0.55
only_positive_rewards: False
scales:
approaching: 5.0
lifting: 1.0
pick_up: 5.0 # 0.5
acc_penalty: -0.001
command_penalty: -1.0
command_reward: 0.25
standpick: 0.25 # no found in reward_vec_task.py
action_rate: -0.001
ee_orn: 0.05 # 0.01
base_dir: 0.25
rad_penalty: 0.0
base_ang_pen: 0.0
base_approaching: 0.01 # 0.05
grasp_base_height: 0.5 # no found in reward_vec_task.py
gripper_rate: -0.1 # -0.1
…wholebody into train/parameter
reward:
base_height_target: 0.55
only_positive_rewards: False
scales:
approaching: 10.0
lifting: 1.0
pick_up: 5.0 # 0.5
acc_penalty: -0.001
command_penalty: -1.0
command_reward: 0.25
standpick: 0.25 # no found in reward_vec_task.py
action_rate: -0.001
ee_orn: 0.05 # 0.01
base_dir: 0.25
rad_penalty: 0.0
base_ang_pen: 0.0
base_approaching: 0.01 # 0.05
grasp_base_height: 0.5 # no found in reward_vec_task.py
gripper_rate: -0.1 # -0.1
reward:
base_height_target: 0.55
only_positive_rewards: False
scales:
approaching: 5.0
lifting: 1.0
pick_up: 5.0 # 0.5
acc_penalty: -0.001
command_penalty: -1.0
command_reward: 0.25
standpick: 0.25 # no found in reward_vec_task.py #
action_rate: -0.001
ee_orn: 0.1 # 0.01
base_dir: 0.25
rad_penalty: 0.0
base_ang_pen: 0.0
base_approaching: 0.01 # 0.05
grasp_base_height: 0.5 # no found in reward_vec_task.py
gripper_rate: -0.1 # -0.1
reward:
base_height_target: 0.55
only_positive_rewards: False
scales:
approaching: 5.0
lifting: 1.0
pick_up: 5.0 # 0.5
acc_penalty: -0.001
command_penalty: -1.0
command_reward: 0.25
standpick: 0.25 # no found in reward_vec_task.py #
action_rate: -0.001
ee_orn: 0.1 # 0.01
base_dir: 0.25
rad_penalty: 0.0
base_ang_pen: 0.0
base_approaching: 0.01 # 0.05
grasp_base_height: 0.5 # no found in reward_vec_task.py
gripper_rate: -0.1 # -0.1
…lebody into train/scales
def _reward_base_dir(self, obj_pos):
base_x_dir = torch.tensor([0., 0., 1.], device=self.device).repeat(self.num_envs, 1)
base_x_dir_world = quat_apply(self.base_yaw_quat, base_x_dir)
obj_dir = obj_pos - self._robot_root_states[:, :3]
obj_dir[:,:2] = 0.
obj_dist = torch.norm(obj_dir, dim=-1)
safe_dis = obj_dist >= 0.01
obj_dir_unit = obj_dir[safe_dis] / obj_dist[safe_dis].unsqueeze(-1)
rew = torch.zeros(self.num_envs, device=self.device, dtype=torch.float)
# rew[safe_dis] = torch.abs(torch.abs(torch.sum(base_x_dir_world[safe_dis] * obj_dir_unit, dim=-1)) - 1)
rew[safe_dis] = F.cosine_similarity(base_x_dir_world[safe_dis], obj_dir_unit)
return rew, rew
def _reward_base_dir(self, obj_pos):
base_x_dir = torch.tensor([0., 0., 1.], device=self.device).repeat(self.num_envs, 1)
base_x_dir_world = quat_apply(self.base_yaw_quat, base_x_dir)
obj_dir = obj_pos - self._robot_root_states[:, :3]
obj_dir[:,:2] = 0.
obj_dist = torch.norm(obj_dir, dim=-1)
safe_dis = obj_dist >= 0.01
obj_dir_unit = obj_dir[safe_dis] / obj_dist[safe_dis].unsqueeze(-1)
rew = torch.zeros(self.num_envs, device=self.device, dtype=torch.float)
# rew[safe_dis] = torch.abs(torch.abs(torch.sum(base_x_dir_world[safe_dis] * obj_dir_unit, dim=-1)) - 1)
rew[safe_dis] = F.cosine_similarity(base_x_dir_world[safe_dis], obj_dir_unit)
return rew, rew
…holebody into debug/pipeline
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