[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

lerobot/scripts/server/actor_server.py

@@ -47,7 +47,7 @@ from lerobot.scripts.server.buffer import (
     python_object_to_bytes,
     transitions_to_bytes,
 )
-from lerobot.scripts.server.gym_manipulator import get_classifier, make_robot_env
+from lerobot.scripts.server.gym_manipulator import make_robot_env
 from lerobot.scripts.server.network_utils import (
     receive_bytes_in_chunks,
     send_bytes_in_chunks,
@@ -444,7 +444,7 @@ def receive_policy(
 
         # Initialize logging with explicit log file
         init_logging(log_file=log_file)
-        logging.info(f"Actor receive policy process logging initialized")
+        logging.info("Actor receive policy process logging initialized")
 
         # Setup process handlers to handle shutdown signal
         # But use shutdown event from the main process

lerobot/scripts/server/buffer.py

@@ -515,7 +515,7 @@ class ReplayBuffer:
             frame_dict["action"] = self.actions[actual_idx].cpu()
             frame_dict["next.reward"] = torch.tensor([self.rewards[actual_idx]], dtype=torch.float32).cpu()
             frame_dict["next.done"] = torch.tensor([self.dones[actual_idx]], dtype=torch.bool).cpu()
-            
+
             # Add task field which is required by LeRobotDataset
             frame_dict["task"] = task_name
 

lerobot/scripts/server/end_effector_control_utils.py

@@ -701,10 +701,10 @@ def teleoperate_gym_env(env, controller, fps: int = 30):
 
 
 if __name__ == "__main__":
+    from lerobot.common.envs.configs import EEActionSpaceConfig, EnvWrapperConfig, HILSerlRobotEnvConfig
     from lerobot.common.robot_devices.robots.configs import RobotConfig
     from lerobot.common.robot_devices.robots.utils import make_robot_from_config
     from lerobot.scripts.server.gym_manipulator import make_robot_env
-    from lerobot.common.envs.configs import HILSerlRobotEnvConfig, EEActionSpaceConfig, EnvWrapperConfig
 
     parser = argparse.ArgumentParser(description="Test end-effector control")
     parser.add_argument(

lerobot/scripts/server/find_joint_limits.py

@@ -5,14 +5,15 @@ import cv2
 import numpy as np
 
 from lerobot.common.robot_devices.control_utils import is_headless
-from lerobot.common.robot_devices.robots.utils import make_robot_from_config
-from lerobot.scripts.server.kinematics import RobotKinematics
-from lerobot.configs import parser
 from lerobot.common.robot_devices.robots.configs import RobotConfig
+from lerobot.common.robot_devices.robots.utils import make_robot_from_config
+from lerobot.configs import parser
+from lerobot.scripts.server.kinematics import RobotKinematics
 
 follower_port = "/dev/tty.usbmodem58760431631"
 leader_port = "/dev/tty.usbmodem58760433331"
 
+
 def find_joint_bounds(
     robot,
     control_time_s=30,
@@ -85,21 +86,22 @@ def find_ee_bounds(
 def make_robot(robot_type="so100"):
     """
     Create a robot instance using the appropriate robot config class.
-    
+
     Args:
         robot_type: Robot type string (e.g., "so100", "koch", "aloha")
-        
+
     Returns:
         Robot instance
     """
-    
+
     # Get the appropriate robot config class based on robot_type
     robot_config = RobotConfig.get_choice_class(robot_type)(mock=False)
     robot_config.leader_arms["main"].port = leader_port
     robot_config.follower_arms["main"].port = follower_port
-    
+
     return make_robot_from_config(robot_config)
 
+
 if __name__ == "__main__":
     # Create argparse for script-specific arguments
     parser = argparse.ArgumentParser(add_help=False)  # Set add_help=False to avoid conflict
@@ -125,14 +127,14 @@ if __name__ == "__main__":
 
     # Only parse known args, leaving robot config args for Hydra if used
     args = parser.parse_args()
-    
+
     # Create robot with the appropriate config
     robot = make_robot(args.robot_type)
-    
+
     if args.mode == "joint":
         find_joint_bounds(robot, args.control_time_s)
     elif args.mode == "ee":
         find_ee_bounds(robot, args.control_time_s)
-    
+
     if robot.is_connected:
-        robot.disconnect()
+        robot.disconnect()

lerobot/scripts/server/maniskill_manipulator.py

@@ -1,7 +1,6 @@
 import logging
 import time
-from dataclasses import dataclass, field
-from typing import Any, Dict, Optional, Tuple
+from typing import Any
 
 import einops
 import gymnasium as gym
@@ -10,10 +9,8 @@ import torch
 from mani_skill.utils.wrappers.record import RecordEpisode
 from mani_skill.vector.wrappers.gymnasium import ManiSkillVectorEnv
 
-from lerobot.common.envs.configs import EnvConfig, ManiskillEnvConfig
+from lerobot.common.envs.configs import ManiskillEnvConfig
 from lerobot.configs import parser
-from lerobot.configs.types import FeatureType, PolicyFeature
-from lerobot.common.constants import ACTION, OBS_IMAGE, OBS_ROBOT
 
 
 def preprocess_maniskill_observation(
@@ -53,9 +50,6 @@ def preprocess_maniskill_observation(
     return return_observations
 
 
-
-
-
 class ManiSkillObservationWrapper(gym.ObservationWrapper):
     def __init__(self, env, device: torch.device = "cuda"):
         super().__init__(env)
@@ -122,6 +116,7 @@ class ManiSkillMultiplyActionWrapper(gym.Wrapper):
 
 class BatchCompatibleWrapper(gym.ObservationWrapper):
     """Ensures observations are batch-compatible by adding a batch dimension if necessary."""
+
     def __init__(self, env):
         super().__init__(env)
 
@@ -136,6 +131,7 @@ class BatchCompatibleWrapper(gym.ObservationWrapper):
 
 class TimeLimitWrapper(gym.Wrapper):
     """Adds a time limit to the environment based on fps and control_time."""
+
     def __init__(self, env, control_time_s, fps):
         super().__init__(env)
         self.control_time_s = control_time_s
@@ -146,10 +142,10 @@ class TimeLimitWrapper(gym.Wrapper):
     def step(self, action):
         obs, reward, terminated, truncated, info = self.env.step(action)
         self.current_step += 1
-        
+
         if self.current_step >= self.max_episode_steps:
             terminated = True
-            
+
         return obs, reward, terminated, truncated, info
 
     def reset(self, *, seed=None, options=None):
@@ -190,18 +186,18 @@ def make_maniskill(
             save_video=True,
             video_fps=30,
         )
-    
+
     # Add observation and image processing
     env = ManiSkillObservationWrapper(env, device=cfg.device)
     env = ManiSkillVectorEnv(env, ignore_terminations=True, auto_reset=False)
     env._max_episode_steps = env.max_episode_steps = cfg.episode_length
     env.unwrapped.metadata["render_fps"] = cfg.fps
-    
+
     # Add compatibility wrappers
     env = ManiSkillCompat(env)
     env = ManiSkillActionWrapper(env)
     env = ManiSkillMultiplyActionWrapper(env, multiply_factor=0.03)  # Scale actions for better control
-    
+
     return env
 
 
@@ -210,29 +206,29 @@ def main(cfg: ManiskillEnvConfig):
     """Main function to run the ManiSkill environment."""
     # Create the ManiSkill environment
     env = make_maniskill(cfg, n_envs=1)
-    
+
     # Reset the environment
     obs, info = env.reset()
-    
+
     # Run a simple interaction loop
     sum_reward = 0
     for i in range(100):
         # Sample a random action
         action = env.action_space.sample()
-        
+
         # Step the environment
         start_time = time.perf_counter()
         obs, reward, terminated, truncated, info = env.step(action)
         step_time = time.perf_counter() - start_time
         sum_reward += reward
         # Log information
-        
+
         # Reset if episode terminated
         if terminated or truncated:
             logging.info(f"Step {i}, reward: {sum_reward}, step time: {step_time}s")
             sum_reward = 0
             obs, info = env.reset()
-    
+
     # Close the environment
     env.close()
 
@@ -243,6 +239,10 @@ def main(cfg: ManiskillEnvConfig):
 
 if __name__ == "__main__":
     import draccus
+
     config = ManiskillEnvConfig()
     draccus.set_config_type("json")
-    draccus.dump(config=config, stream=open(file='run_config.json', mode='w'), )
+    draccus.dump(
+        config=config,
+        stream=open(file="run_config.json", mode="w"),
+    )

lerobot/scripts/train_hilserl_classifier.py

@@ -32,7 +32,6 @@ from tqdm import tqdm
 from lerobot.common.datasets.factory import resolve_delta_timestamps
 from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.common.policies.hilserl.classifier.configuration_classifier import ClassifierConfig
-
 from lerobot.common.policies.hilserl.classifier.modeling_classifier import Classifier
 from lerobot.common.utils.utils import (
     format_big_number,