Add test to make sure policy dataclass configs match yaml configs (#292)

lerobot/common/policies/factory.py

@@ -28,9 +28,15 @@ def _policy_cfg_from_hydra_cfg(policy_cfg_class, hydra_cfg):
         logging.warning(
             f"Hydra config is missing arguments: {set(expected_kwargs).difference(hydra_cfg.policy)}"
         )
+
+    # OmegaConf.to_container returns lists where sequences are found, but our dataclasses use tuples to avoid
+    # issues with mutable defaults. This filter changes all lists to tuples.
+    def list_to_tuple(item):
+        return tuple(item) if isinstance(item, list) else item
+
     policy_cfg = policy_cfg_class(
         **{
-            k: v
+            k: list_to_tuple(v)
             for k, v in OmegaConf.to_container(hydra_cfg.policy, resolve=True).items()
             if k in expected_kwargs
         }
@@ -80,7 +86,9 @@ def make_policy(
             policy. Therefore, this argument is mutually exclusive with `pretrained_policy_name_or_path`.
     """
     if not (pretrained_policy_name_or_path is None) ^ (dataset_stats is None):
-        raise ValueError("Only one of `pretrained_policy_name_or_path` and `dataset_stats` may be provided.")
+        raise ValueError(
+            "Exactly one of `pretrained_policy_name_or_path` and `dataset_stats` must be provided."
+        )
 
     policy_cls, policy_cfg_class = get_policy_and_config_classes(hydra_cfg.policy.name)
 
@@ -91,9 +99,10 @@ def make_policy(
     else:
         # Load a pretrained policy and override the config if needed (for example, if there are inference-time
         # hyperparameters that we want to vary).
-        # TODO(alexander-soare): This hack makes use of huggingface_hub's tooling to load the policy with, pretrained
-        # weights which are then loaded into a fresh policy with the desired config. This PR in huggingface_hub should
-        # make it possible to avoid the hack: https://github.com/huggingface/huggingface_hub/pull/2274.
+        # TODO(alexander-soare): This hack makes use of huggingface_hub's tooling to load the policy with,
+        # pretrained weights which are then loaded into a fresh policy with the desired config. This PR in
+        # huggingface_hub should make it possible to avoid the hack:
+        # https://github.com/huggingface/huggingface_hub/pull/2274.
         policy = policy_cls(policy_cfg)
         policy.load_state_dict(policy_cls.from_pretrained(pretrained_policy_name_or_path).state_dict())
 

lerobot/configs/policy/diffusion.yaml

@@ -99,7 +99,7 @@ policy:
   clip_sample_range: 1.0
 
   # Inference
-  num_inference_steps: 100
+  num_inference_steps: null  # if not provided, defaults to `num_train_timesteps`
 
   # Loss computation
   do_mask_loss_for_padding: false

lerobot/configs/policy/tdmpc.yaml

@@ -54,7 +54,7 @@ policy:
   discount: 0.9
 
   # Inference.
-  use_mpc: false
+  use_mpc: true
   cem_iterations: 6
   max_std: 2.0
   min_std: 0.05