Add refactor env.py

lerobot/common/envs/factory.py

@@ -27,14 +27,6 @@ def make_env(cfg: DictConfig, n_envs: int | None = None) -> gym.vector.VectorEnv
     if n_envs is not None and n_envs < 1:
         raise ValueError("`n_envs must be at least 1")
 
-    kwargs = {
-        "obs_type": "pixels_agent_pos",
-        "render_mode": "rgb_array",
-        "max_episode_steps": cfg.env.episode_length,
-        "visualization_width": 384,
-        "visualization_height": 384,
-    }
-
     package_name = f"gym_{cfg.env.name}"
 
     try:
@@ -46,12 +38,16 @@ def make_env(cfg: DictConfig, n_envs: int | None = None) -> gym.vector.VectorEnv
         raise e
 
     gym_handle = f"{package_name}/{cfg.env.task}"
+    gym_kwgs = cfg.env.get("gym", {})
+
+    if cfg.env.get("episode_length"):
+        gym_kwgs["max_episode_steps"] = cfg.env.episode_length
 
     # batched version of the env that returns an observation of shape (b, c)
     env_cls = gym.vector.AsyncVectorEnv if cfg.eval.use_async_envs else gym.vector.SyncVectorEnv
     env = env_cls(
         [
-            lambda: gym.make(gym_handle, disable_env_checker=True, **kwargs)
+            lambda: gym.make(gym_handle, disable_env_checker=True, **gym_kwgs)
             for _ in range(n_envs if n_envs is not None else cfg.eval.batch_size)
         ]
     )

lerobot/configs/default.yaml

@@ -37,6 +37,8 @@ training:
   save_freq: ???
   log_freq: 250
   save_checkpoint: true
+  num_workers: 4
+  batch_size: ???
 
 eval:
   n_episodes: 1

lerobot/configs/env/aloha.yaml

@@ -5,10 +5,13 @@ fps: 50
 env:
   name: aloha
   task: AlohaInsertion-v0
-  from_pixels: True
-  pixels_only: False
   image_size: [3, 480, 640]
-  episode_length: 400
-  fps: ${fps}
   state_dim: 14
   action_dim: 14
+  fps: ${fps}
+  episode_length: 400
+  gym:
+    obs_type: pixels_agent_pos
+    render_mode: rgb_array
+    visualization_width: 384
+    visualization_height: 384

lerobot/configs/env/pusht.yaml

@@ -5,10 +5,13 @@ fps: 10
 env:
   name: pusht
   task: PushT-v0
-  from_pixels: True
-  pixels_only: False
   image_size: 96
-  episode_length: 300
-  fps: ${fps}
   state_dim: 2
   action_dim: 2
+  fps: ${fps}
+  episode_length: 300
+  gym:
+    obs_type: pixels_agent_pos
+    render_mode: rgb_array
+    visualization_width: 384
+    visualization_height: 384

lerobot/configs/env/xarm.yaml

@@ -5,10 +5,13 @@ fps: 15
 env:
   name: xarm
   task: XarmLift-v0
-  from_pixels: True
-  pixels_only: False
   image_size: 84
-  episode_length: 25
-  fps: ${fps}
   state_dim: 4
   action_dim: 4
+  fps: ${fps}
+  episode_length: 25
+  gym:
+    obs_type: pixels_agent_pos
+    render_mode: rgb_array
+    visualization_width: 384
+    visualization_height: 384

lerobot/scripts/train.py

@@ -281,6 +281,10 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
     logging.info("make_dataset")
     offline_dataset = make_dataset(cfg)
 
+    # Create environment used for evaluating checkpoints during training on simulation data.
+    # On real-world data, no need to create an environment as evaluations are done outside train.py,
+    # using the eval.py instead, with gym_dora environment and dora-rs.
+    if cfg.training.eval_freq > 0:
         logging.info("make_env")
         eval_env = make_env(cfg)
 
@@ -315,7 +319,7 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
 
     # Note: this helper will be used in offline and online training loops.
     def evaluate_and_checkpoint_if_needed(step):
-        if step % cfg.training.eval_freq == 0:
+        if cfg.training.eval_freq > 0 and step % cfg.training.eval_freq == 0:
             logging.info(f"Eval policy at step {step}")
             with torch.no_grad(), torch.autocast(device_type=device.type) if cfg.use_amp else nullcontext():
                 eval_info = eval_policy(
@@ -349,7 +353,7 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
     # create dataloader for offline training
     dataloader = torch.utils.data.DataLoader(
         offline_dataset,
-        num_workers=4,
+        num_workers=cfg.training.num_workers,
         batch_size=cfg.training.batch_size,
         shuffle=True,
         pin_memory=device.type != "cpu",
@@ -386,6 +390,16 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
 
         step += 1
 
+    logging.info("End of offline training")
+
+    if cfg.training.online_steps == 0:
+        if cfg.training.eval_freq > 0:
+            eval_env.close()
+        return
+
+    # create an env dedicated to online episodes collection from policy rollout
+    online_training_env = make_env(cfg, n_envs=1)
+
     # create an empty online dataset similar to offline dataset
     online_dataset = deepcopy(offline_dataset)
     online_dataset.hf_dataset = {}
@@ -406,8 +420,10 @@ def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = No
         drop_last=False,
     )
 
+    logging.info("End of online training")
+
     eval_env.close()
-    logging.info("End of training")
+    online_training_env.close()
 
 
 @hydra.main(version_base="1.2", config_name="default", config_path="../configs")