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modifications to gym_manipulator and buffer

This commit is contained in:
Michel Aractingi 2025-04-07 08:45:53 +02:00
parent ab2c2d39fb
commit f3cea2a3e5
8 changed files with 76 additions and 78 deletions
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4
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
@ -203,7 +201,7 @@ 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_quantization_threshold: float | None = None
gripper_penalty: float = 0.0
open_gripper_on_reset: bool = False

12
lerobot/common/policies/sac/modeling_sac.py
View File

@ -144,8 +144,8 @@ class SACPolicy(
self.grasp_critic_target.load_state_dict(self.grasp_critic.state_dict())
self.grasp_critic = torch.compile(self.grasp_critic)
self.grasp_critic_target = torch.compile(self.grasp_critic_target)
# self.grasp_critic = torch.compile(self.grasp_critic)
# self.grasp_critic_target = torch.compile(self.grasp_critic_target)
self.actor = Policy(
encoder=encoder_actor,
@ -224,6 +224,10 @@ class SACPolicy(
critics = self.critic_target if use_target else self.critic_ensemble
q_values = critics(observations, actions, observation_features)
if not use_target:
for name, param in critics.named_parameters():
if param.requires_grad:
print(f"Critic Ensemble layer {name}, norm {param.data.norm().item()}")
return q_values
def grasp_critic_forward(self, observations, use_target=False, observation_features=None) -> torch.Tensor:
@ -239,6 +243,10 @@ class SACPolicy(
"""
grasp_critic = self.grasp_critic_target if use_target else self.grasp_critic
q_values = grasp_critic(observations, observation_features)
if not use_target:
for name, param in grasp_critic.named_parameters():
if param.requires_grad:
print(f"Grasp critic layer {name}, norm {param.data.norm().item()}")
return q_values
def forward(

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()

30
lerobot/scripts/server/actor_server.py
View File

@ -250,28 +250,18 @@ def act_with_policy(
logging.info("[ACTOR] Shutting down act_with_policy")
return
if interaction_step >= cfg.policy.online_step_before_learning:
# Time policy inference and check if it meets FPS requirement
with TimerManager(
elapsed_time_list=list_policy_time,
label="Policy inference time",
log=False,
) as timer: # noqa: F841
action = policy.select_action(batch=obs)
policy_fps = 1.0 / (list_policy_time[-1] + 1e-9)
# Time policy inference and check if it meets FPS requirement
with TimerManager(
elapsed_time_list=list_policy_time,
label="Policy inference time",
log=False,
) as timer: # noqa: F841
action = policy.select_action(batch=obs)
policy_fps = 1.0 / (list_policy_time[-1] + 1e-9)
log_policy_frequency_issue(policy_fps=policy_fps, cfg=cfg, interaction_step=interaction_step)
log_policy_frequency_issue(policy_fps=policy_fps, cfg=cfg, interaction_step=interaction_step)
next_obs, reward, done, truncated, info = online_env.step(action.squeeze(dim=0).cpu().numpy())
else:
# TODO (azouitine): Make a custom space for torch tensor
action = online_env.action_space.sample()
next_obs, reward, done, truncated, info = online_env.step(action)
# HACK: We have only one env but we want to batch it, it will be resolved with the torch box
action = (
torch.from_numpy(action[0]).to(device, non_blocking=device.type == "cuda").unsqueeze(dim=0)
)
next_obs, reward, done, truncated, info = online_env.step(action.squeeze(dim=0).cpu().numpy())
sum_reward_episode += float(reward)
# Increment total steps counter for intervention rate

6
lerobot/scripts/server/buffer.py
View File

@ -505,7 +505,6 @@ 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 +519,6 @@ 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.
@ -565,8 +563,6 @@ 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
@ -598,8 +594,6 @@ class ReplayBuffer:
else:
action = action[:, action_mask]
if action_delta is not None:
action = action / action_delta
replay_buffer.add(
state=data["state"],

20
lerobot/scripts/server/end_effector_control_utils.py
View File

@ -258,24 +258,24 @@ class GamepadController(InputController):
elif event.button == 0:
self.episode_end_status = "rerecord_episode"
# RB button (6) for opening gripper
# LT button for closing gripper
elif event.button == 6:
self.open_gripper_command = True
# LT button (7) for closing gripper
elif event.button == 7:
self.close_gripper_command = True
# RB button for opening gripper
elif event.button == 7:
self.open_gripper_command = True
# Reset episode status on button release
elif event.type == pygame.JOYBUTTONUP:
if event.button in [0, 2, 3]:
self.episode_end_status = None
elif event.button == 6:
self.open_gripper_command = False
elif event.button == 7:
if event.button == 6:
self.close_gripper_command = False
if event.button == 7:
self.open_gripper_command = False
# Check for RB button (typically button 5) for intervention flag
if self.joystick.get_button(5):

71
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")
@ -555,6 +551,9 @@ class ImageCropResizeWrapper(gym.Wrapper):
# TODO(michel-aractingi): Bug in resize, it returns values outside [0, 1]
obs[k] = obs[k].clamp(0.0, 1.0)
# import cv2
# cv2.imwrite(f"tmp_img/{k}.jpg", cv2.cvtColor(obs[k].squeeze(0).permute(1,2,0).cpu().numpy()*255, cv2.COLOR_RGB2BGR))
# Check for NaNs after processing
if torch.isnan(obs[k]).any():
logging.error(f"NaN values detected in observation {k} after crop and resize")
@ -720,19 +719,31 @@ 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.",
@ -777,7 +788,6 @@ class GripperPenaltyWrapper(gym.RewardWrapper):
gripper_penalty_bool = (gripper_state_normalized < 0.1 and action_normalized > 0.9) or (
gripper_state_normalized > 0.9 and action_normalized < 0.1
)
breakpoint()
return reward + self.penalty * gripper_penalty_bool
@ -791,8 +801,7 @@ class GripperPenaltyWrapper(gym.RewardWrapper):
self.last_gripper_state = None
return super().reset(**kwargs)
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 +810,18 @@ 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 +847,12 @@ 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 +1010,11 @@ 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 +1154,6 @@ 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,
)
@ -1165,7 +1177,7 @@ 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)
@ -1176,6 +1188,7 @@ 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 +1205,7 @@ 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 +1355,10 @@ 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 +1366,7 @@ 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 +1397,7 @@ 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()

8
lerobot/scripts/server/learner_server.py
View File

@ -380,6 +380,7 @@ def add_actor_information_and_train(
for _ in range(utd_ratio - 1):
# Sample from the iterators
batch = next(online_iterator)
# batch = replay_buffer.sample(batch_size)
if dataset_repo_id is not None:
batch_offline = next(offline_iterator)
@ -437,9 +438,11 @@ def add_actor_information_and_train(
# Sample for the last update in the UTD ratio
batch = next(online_iterator)
# batch = replay_buffer.sample(batch_size)
if dataset_repo_id is not None:
batch_offline = next(offline_iterator)
# batch_offline = offline_replay_buffer.sample(batch_size)
batch = concatenate_batch_transitions(
left_batch_transitions=batch, right_batch_transition=batch_offline
)
@ -775,9 +778,7 @@ def make_optimizers_and_scheduler(cfg: TrainPipelineConfig, policy: nn.Module):
params=policy.actor.parameters_to_optimize,
lr=cfg.policy.actor_lr,
)
optimizer_critic = torch.optim.Adam(
params=policy.critic_ensemble.parameters_to_optimize, lr=cfg.policy.critic_lr
)
optimizer_critic = torch.optim.Adam(params=policy.critic_ensemble.parameters_to_optimize, lr=cfg.policy.critic_lr)
if cfg.policy.num_discrete_actions is not None:
optimizer_grasp_critic = torch.optim.Adam(
@ -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,