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General fixes in code, removed delta action, fixed grasp penalty, added logic to put gripper reward in info

This commit is contained in:
Michel Aractingi 2025-04-09 17:04:43 +02:00
parent e7edf2a8d8
commit 5428ab96f5
7 changed files with 75 additions and 65 deletions
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8
lerobot/common/envs/configs.py
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@ -171,7 +171,6 @@ class VideoRecordConfig:
class WrapperConfig:
"""Configuration for environment wrappers."""
delta_action: float | None = None
joint_masking_action_space: list[bool] | None = None
@ -191,7 +190,6 @@ class EnvWrapperConfig:
"""Configuration for environment wrappers."""
display_cameras: bool = False
delta_action: float = 0.1
use_relative_joint_positions: bool = True
add_joint_velocity_to_observation: bool = False
add_ee_pose_to_observation: bool = False
@ -203,11 +201,13 @@ class EnvWrapperConfig:
joint_masking_action_space: Optional[Any] = None
ee_action_space_params: Optional[EEActionSpaceConfig] = None
use_gripper: bool = False
gripper_quantization_threshold: float = 0.8
gripper_penalty: float = 0.0
gripper_quantization_threshold: float | None = 0.8
gripper_penalty: float = 0.0
gripper_penalty_in_reward: bool = False
open_gripper_on_reset: bool = False
@EnvConfig.register_subclass(name="gym_manipulator")
@dataclass
class HILSerlRobotEnvConfig(EnvConfig):

1
lerobot/common/policies/sac/modeling_sac.py
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@ -428,6 +428,7 @@ class SACPolicy(
actions_discrete = torch.round(actions_discrete)
actions_discrete = actions_discrete.long()
gripper_penalties: Tensor | None = None
if complementary_info is not None:
gripper_penalties: Tensor | None = complementary_info.get("gripper_penalty")

3
lerobot/common/robot_devices/control_utils.py
View File

@ -221,7 +221,6 @@ def record_episode(
events=events,
policy=policy,
fps=fps,
# record_delta_actions=record_delta_actions,
teleoperate=policy is None,
single_task=single_task,
)
@ -267,8 +266,6 @@ def control_loop(
if teleoperate:
observation, action = robot.teleop_step(record_data=True)
# if record_delta_actions:
# action["action"] = action["action"] - current_joint_positions
else:
observation = robot.capture_observation()

2
lerobot/scripts/control_robot.py
View File

@ -363,8 +363,6 @@ def replay(
start_episode_t = time.perf_counter()
action = actions[idx]["action"]
# if replay_delta_actions:
# action = action + current_joint_positions
robot.send_action(action)
dt_s = time.perf_counter() - start_episode_t

12
lerobot/scripts/server/buffer.py
View File

@ -78,9 +78,7 @@ def move_transition_to_device(transition: Transition, device: str = "cpu") -> Tr
if isinstance(val, torch.Tensor):
transition["complementary_info"][key] = val.to(device, non_blocking=non_blocking)
elif isinstance(val, (int, float, bool)):
transition["complementary_info"][key] = torch.tensor(
val, device=device, non_blocking=non_blocking
)
transition["complementary_info"][key] = torch.tensor(val, device=device)
else:
raise ValueError(f"Unsupported type {type(val)} for complementary_info[{key}]")
return transition
@ -505,7 +503,6 @@ class ReplayBuffer:
state_keys: Optional[Sequence[str]] = None,
capacity: Optional[int] = None,
action_mask: Optional[Sequence[int]] = None,
action_delta: Optional[float] = None,
image_augmentation_function: Optional[Callable] = None,
use_drq: bool = True,
storage_device: str = "cpu",
@ -520,7 +517,6 @@ class ReplayBuffer:
state_keys (Optional[Sequence[str]]): The list of keys that appear in `state` and `next_state`.
capacity (Optional[int]): Buffer capacity. If None, uses dataset length.
action_mask (Optional[Sequence[int]]): Indices of action dimensions to keep.
action_delta (Optional[float]): Factor to divide actions by.
image_augmentation_function (Optional[Callable]): Function for image augmentation.
If None, uses default random shift with pad=4.
use_drq (bool): Whether to use DrQ image augmentation when sampling.
@ -565,9 +561,6 @@ class ReplayBuffer:
else:
first_action = first_action[:, action_mask]
if action_delta is not None:
first_action = first_action / action_delta
# Get complementary info if available
first_complementary_info = None
if (
@ -598,9 +591,6 @@ class ReplayBuffer:
else:
action = action[:, action_mask]
if action_delta is not None:
action = action / action_delta
replay_buffer.add(
state=data["state"],
action=action,

110
lerobot/scripts/server/gym_manipulator.py
View File

@ -42,7 +42,6 @@ class HILSerlRobotEnv(gym.Env):
self,
robot,
use_delta_action_space: bool = True,
delta: float | None = None,
display_cameras: bool = False,
):
"""
@ -55,8 +54,6 @@ class HILSerlRobotEnv(gym.Env):
robot: The robot interface object used to connect and interact with the physical robot.
use_delta_action_space (bool): If True, uses a delta (relative) action space for joint control. Otherwise, absolute
joint positions are used.
delta (float or None): A scaling factor for the relative adjustments applied to joint positions. Should be a value between
0 and 1 when using a delta action space.
display_cameras (bool): If True, the robot's camera feeds will be displayed during execution.
"""
super().__init__()
@ -74,7 +71,6 @@ class HILSerlRobotEnv(gym.Env):
self.current_step = 0
self.episode_data = None
self.delta = delta
self.use_delta_action_space = use_delta_action_space
self.current_joint_positions = self.robot.follower_arms["main"].read("Present_Position")
@ -374,7 +370,7 @@ class RewardWrapper(gym.Wrapper):
self.device = device
def step(self, action):
observation, _, terminated, truncated, info = self.env.step(action)
observation, reward, terminated, truncated, info = self.env.step(action)
images = [
observation[key].to(self.device, non_blocking=self.device.type == "cuda")
for key in observation
@ -382,15 +378,17 @@ class RewardWrapper(gym.Wrapper):
]
start_time = time.perf_counter()
with torch.inference_mode():
reward = (
success = (
self.reward_classifier.predict_reward(images, threshold=0.8)
if self.reward_classifier is not None
else 0.0
)
info["Reward classifer frequency"] = 1 / (time.perf_counter() - start_time)
if reward == 1.0:
if success == 1.0:
terminated = True
reward = 1.0
return observation, reward, terminated, truncated, info
def reset(self, seed=None, options=None):
@ -720,19 +718,31 @@ class ResetWrapper(gym.Wrapper):
env: HILSerlRobotEnv,
reset_pose: np.ndarray | None = None,
reset_time_s: float = 5,
open_gripper_on_reset: bool = False
):
super().__init__(env)
self.reset_time_s = reset_time_s
self.reset_pose = reset_pose
self.robot = self.unwrapped.robot
self.open_gripper_on_reset = open_gripper_on_reset
def reset(self, *, seed=None, options=None):
if self.reset_pose is not None:
start_time = time.perf_counter()
log_say("Reset the environment.", play_sounds=True)
reset_follower_position(self.robot, self.reset_pose)
busy_wait(self.reset_time_s - (time.perf_counter() - start_time))
log_say("Reset the environment done.", play_sounds=True)
if self.open_gripper_on_reset:
current_joint_pos = self.robot.follower_arms["main"].read("Present_Position")
current_joint_pos[-1] = MAX_GRIPPER_COMMAND
self.robot.send_action(torch.from_numpy(current_joint_pos))
busy_wait(0.1)
current_joint_pos[-1] = 0.0
self.robot.send_action(torch.from_numpy(current_joint_pos))
busy_wait(0.2)
else:
log_say(
f"Manually reset the environment for {self.reset_time_s} seconds.",
@ -762,37 +772,48 @@ class BatchCompitableWrapper(gym.ObservationWrapper):
class GripperPenaltyWrapper(gym.RewardWrapper):
def __init__(self, env, penalty: float = -0.1):
def __init__(self, env, penalty: float = -0.1, gripper_penalty_in_reward: bool = True):
super().__init__(env)
self.penalty = penalty
self.gripper_penalty_in_reward = gripper_penalty_in_reward
self.last_gripper_state = None
def reward(self, reward, action):
gripper_state_normalized = self.last_gripper_state / MAX_GRIPPER_COMMAND
if isinstance(action, tuple):
action = action[0]
action_normalized = action[-1] / MAX_GRIPPER_COMMAND
action_normalized = action - 1.0 #action / MAX_GRIPPER_COMMAND
gripper_penalty_bool = (gripper_state_normalized < 0.1 and action_normalized > 0.9) or (
gripper_state_normalized > 0.9 and action_normalized < 0.1
gripper_penalty_bool = (gripper_state_normalized < 0.5 and action_normalized > 0.5) or (
gripper_state_normalized > 0.75 and action_normalized < -0.5
)
breakpoint()
return reward + self.penalty * gripper_penalty_bool
return reward + self.penalty * int(gripper_penalty_bool)
def step(self, action):
self.last_gripper_state = self.unwrapped.robot.follower_arms["main"].read("Present_Position")[-1]
if isinstance(action, tuple):
gripper_action = action[0][-1]
else:
gripper_action = action[-1]
obs, reward, terminated, truncated, info = self.env.step(action)
reward = self.reward(reward, action)
gripper_penalty = self.reward(reward, gripper_action)
if self.gripper_penalty_in_reward:
reward += gripper_penalty
else:
info["gripper_penalty"] = gripper_penalty
return obs, reward, terminated, truncated, info
def reset(self, **kwargs):
self.last_gripper_state = None
return super().reset(**kwargs)
obs, info = super().reset(**kwargs)
if self.gripper_penalty_in_reward:
info["gripper_penalty"] = 0.0
return obs, info
class GripperQuantizationWrapper(gym.ActionWrapper):
class GripperActionWrapper(gym.ActionWrapper):
def __init__(self, env, quantization_threshold: float = 0.2):
super().__init__(env)
self.quantization_threshold = quantization_threshold
@ -801,16 +822,18 @@ class GripperQuantizationWrapper(gym.ActionWrapper):
is_intervention = False
if isinstance(action, tuple):
action, is_intervention = action
gripper_command = action[-1]
# Quantize gripper command to -1, 0 or 1
if gripper_command < -self.quantization_threshold:
gripper_command = -MAX_GRIPPER_COMMAND
elif gripper_command > self.quantization_threshold:
gripper_command = MAX_GRIPPER_COMMAND
else:
gripper_command = 0.0
# Gripper actions are between 0, 2
# we want to quantize them to -1, 0 or 1
gripper_command = gripper_command - 1.0
if self.quantization_threshold is not None:
# Quantize gripper command to -1, 0 or 1
gripper_command = (
np.sign(gripper_command) if abs(gripper_command) > self.quantization_threshold else 0.0
)
gripper_command = gripper_command * MAX_GRIPPER_COMMAND
gripper_state = self.unwrapped.robot.follower_arms["main"].read("Present_Position")[-1]
gripper_action = np.clip(gripper_state + gripper_command, 0, MAX_GRIPPER_COMMAND)
action[-1] = gripper_action.item()
@ -836,10 +859,12 @@ class EEActionWrapper(gym.ActionWrapper):
]
)
if self.use_gripper:
action_space_bounds = np.concatenate([action_space_bounds, [1.0]])
# gripper actions open at 2.0, and closed at 0.0
min_action_space_bounds = np.concatenate([-action_space_bounds, [0.0]])
max_action_space_bounds = np.concatenate([action_space_bounds, [2.0]])
ee_action_space = gym.spaces.Box(
low=-action_space_bounds,
high=action_space_bounds,
low=min_action_space_bounds,
high=max_action_space_bounds,
shape=(3 + int(self.use_gripper),),
dtype=np.float32,
)
@ -997,11 +1022,11 @@ class GamepadControlWrapper(gym.Wrapper):
if self.use_gripper:
gripper_command = self.controller.gripper_command()
if gripper_command == "open":
gamepad_action = np.concatenate([gamepad_action, [1.0]])
gamepad_action = np.concatenate([gamepad_action, [2.0]])
elif gripper_command == "close":
gamepad_action = np.concatenate([gamepad_action, [-1.0]])
else:
gamepad_action = np.concatenate([gamepad_action, [0.0]])
else:
gamepad_action = np.concatenate([gamepad_action, [1.0]])
# Check episode ending buttons
# We'll rely on controller.get_episode_end_status() which returns "success", "failure", or None
@ -1141,7 +1166,6 @@ def make_robot_env(cfg) -> gym.vector.VectorEnv:
env = HILSerlRobotEnv(
robot=robot,
display_cameras=cfg.wrapper.display_cameras,
delta=cfg.wrapper.delta_action,
use_delta_action_space=cfg.wrapper.use_relative_joint_positions
and cfg.wrapper.ee_action_space_params is None,
)
@ -1165,10 +1189,11 @@ def make_robot_env(cfg) -> gym.vector.VectorEnv:
# env = RewardWrapper(env=env, reward_classifier=reward_classifier, device=cfg.device)
env = TimeLimitWrapper(env=env, control_time_s=cfg.wrapper.control_time_s, fps=cfg.fps)
if cfg.wrapper.use_gripper:
env = GripperQuantizationWrapper(
env = GripperActionWrapper(
env=env, quantization_threshold=cfg.wrapper.gripper_quantization_threshold
)
# env = GripperPenaltyWrapper(env=env, penalty=cfg.wrapper.gripper_penalty)
if cfg.wrapper.gripper_penalty is not None:
env = GripperPenaltyWrapper(env=env, penalty=cfg.wrapper.gripper_penalty, gripper_penalty_in_reward=cfg.wrapper.gripper_penalty_in_reward)
if cfg.wrapper.ee_action_space_params is not None:
env = EEActionWrapper(
@ -1176,6 +1201,7 @@ def make_robot_env(cfg) -> gym.vector.VectorEnv:
ee_action_space_params=cfg.wrapper.ee_action_space_params,
use_gripper=cfg.wrapper.use_gripper,
)
if cfg.wrapper.ee_action_space_params is not None and cfg.wrapper.ee_action_space_params.use_gamepad:
# env = ActionScaleWrapper(env=env, ee_action_space_params=cfg.wrapper.ee_action_space_params)
env = GamepadControlWrapper(
@ -1192,6 +1218,7 @@ def make_robot_env(cfg) -> gym.vector.VectorEnv:
env=env,
reset_pose=cfg.wrapper.fixed_reset_joint_positions,
reset_time_s=cfg.wrapper.reset_time_s,
open_gripper_on_reset=cfg.wrapper.open_gripper_on_reset
)
if cfg.wrapper.ee_action_space_params is None and cfg.wrapper.joint_masking_action_space is not None:
env = JointMaskingActionSpace(env=env, mask=cfg.wrapper.joint_masking_action_space)
@ -1341,11 +1368,10 @@ def record_dataset(env, policy, cfg):
dataset.push_to_hub()
def replay_episode(env, repo_id, root=None, episode=0):
def replay_episode(env, cfg):
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
local_files_only = root is not None
dataset = LeRobotDataset(repo_id, root=root, episodes=[episode], local_files_only=local_files_only)
dataset = LeRobotDataset(cfg.repo_id, root=cfg.dataset_root, episodes=[cfg.episode])
env.reset()
actions = dataset.hf_dataset.select_columns("action")
@ -1353,7 +1379,7 @@ def replay_episode(env, repo_id, root=None, episode=0):
for idx in range(dataset.num_frames):
start_episode_t = time.perf_counter()
action = actions[idx]["action"][:4]
action = actions[idx]["action"]
env.step((action, False))
# env.step((action / env.unwrapped.delta, False))
@ -1384,9 +1410,7 @@ def main(cfg: EnvConfig):
if cfg.mode == "replay":
replay_episode(
env,
cfg.replay_repo_id,
root=cfg.dataset_root,
episode=cfg.replay_episode,
cfg=cfg,
)
exit()

4
lerobot/scripts/server/learner_server.py
View File

@ -406,7 +406,8 @@ def add_actor_information_and_train(
"next_state": next_observations,
"done": done,
"observation_feature": observation_features,
"next_observation_feature": next_observation_features,
"next_observation_feature": next_observation_features,
"complementary_info": batch["complementary_info"],
}
# Use the forward method for critic loss (includes both main critic and grasp critic)
@ -992,7 +993,6 @@ def initialize_offline_replay_buffer(
device=device,
state_keys=cfg.policy.input_features.keys(),
action_mask=active_action_dims,
action_delta=cfg.env.wrapper.delta_action,
storage_device=storage_device,
optimize_memory=True,
capacity=cfg.policy.offline_buffer_capacity,