Add maniskill support.

Co-authored-by: Michel Aractingi <michel.aractingi@gmail.com>

lerobot/configs/env/maniskill_example.yaml

@@ -5,11 +5,16 @@ fps: 20
 env:
   name: maniskill/pushcube
   task:  PushCube-v1
-  image_size: 64
+  image_size: 128
   control_mode: pd_ee_delta_pose
   state_dim: 25
   action_dim: 7
   fps: ${fps}
   obs: rgb
   render_mode: rgb_array
-  render_size: 64
+  render_size: 128
+  device: cuda
+
+  reward_classifier:
+    pretrained_path: null
+    config_path: null

lerobot/configs/policy/sac_maniskill.yaml

@@ -8,7 +8,7 @@
 #   env.gym.obs_type=environment_state_agent_pos \
 
 seed: 1
-dataset_repo_id: aractingi/hil-serl-maniskill-pushcube
+dataset_repo_id: null
 
 training:
   # Offline training dataloader
@@ -20,7 +20,7 @@ training:
   lr: 3e-4
 
   eval_freq: 2500
-  log_freq: 500
+  log_freq: 10
   save_freq: 2000000
 
   online_steps: 1000000
@@ -52,14 +52,16 @@ policy:
   n_action_steps: 1
 
   shared_encoder: true
-  # vision_encoder_name: null
+  vision_encoder_name: null
+  # vision_encoder_name: "helper2424/resnet10"
+  # freeze_vision_encoder: true
   freeze_vision_encoder: false
   input_shapes:
     # # TODO(rcadene, alexander-soare): add variables for height and width from the dataset/env?
     observation.state: ["${env.state_dim}"]
-    observation.image: [3, 64, 64]
+    observation.image: [3, 128, 128]
   output_shapes:
-    action: ["${env.action_dim}"]
+    action: [7]
 
   # Normalization / Unnormalization
   input_normalization_modes: null
@@ -67,8 +69,8 @@ policy:
     action: min_max
   output_normalization_params:
     action:
-      min: [-1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0]
-      max: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
+      min: [-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0]
+      max: [10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0]
 
   # Architecture / modeling.
   # Neural networks.
@@ -88,14 +90,3 @@ policy:
 actor_learner_config:
   actor_ip: "127.0.0.1"
   port: 50051
-
-  # # Loss coefficients.
-  # reward_coeff: 0.5
-  # expectile_weight: 0.9
-  # value_coeff: 0.1
-  # consistency_coeff: 20.0
-  # advantage_scaling: 3.0
-  # pi_coeff: 0.5
-  # temporal_decay_coeff: 0.5
-  # # Target model.
-  # target_model_momentum: 0.995