Refactor SAC policy with performance optimizations and multi-camera support

- Introduced Ensemble and CriticHead classes for more efficient critic network handling
- Added support for multiple camera inputs in observation encoder
- Optimized image encoding by batching image processing
- Updated configuration for ManiSkill environment with reduced image size and action scaling
- Compiled critic networks for improved performance
- Simplified normalization and ensemble handling in critic networks
Co-authored-by: michel-aractingi <michel.aractingi@gmail.com>

lerobot/scripts/server/maniskill_manipulator.py

@@ -10,7 +10,6 @@ from typing import Any
 from mani_skill.vector.wrappers.gymnasium import ManiSkillVectorEnv
 
 
-
 def preprocess_maniskill_observation(observations: dict[str, np.ndarray]) -> dict[str, torch.Tensor]:
     """Convert environment observation to LeRobot format observation.
     Args:
@@ -42,6 +41,7 @@ def preprocess_maniskill_observation(observations: dict[str, np.ndarray]) -> dic
     state = torch.cat([q_pos, q_vel, tcp_pos], dim=-1)
 
     return_observations["observation.image"] = img
+    return_observations["observation.image.2"] = img
     return_observations["observation.state"] = state
     return return_observations
 
@@ -142,7 +142,7 @@ def make_maniskill(
     env.unwrapped.metadata["render_fps"] = 20
     env = ManiSkillCompat(env)
     env = ManiSkillActionWrapper(env)
-    env = ManiSkillMultiplyActionWrapper(env, multiply_factor=10.0)
+    env = ManiSkillMultiplyActionWrapper(env, multiply_factor=1)
 
     return env