Clean up loading code

- Centralized instantiation of the PEFT wrapper in `make_policy` for inference
  (e.g. in `lerobot-record`)
- Training a PEFT policy also sets `cfg.use_peft` so that all inference code loading
  the policy can rely on that attribute to identify if PEFT loading is needed
- Modified RTC example to also include PEFT policies. Mostly because this is an example
  I'm currently exploring.

examples/rtc/eval_with_real_robot.py

@@ -455,7 +455,14 @@ def demo_cli(cfg: RTCDemoConfig):
     if cfg.policy.type == "pi05" or cfg.policy.type == "pi0":
         config.compile_model = cfg.use_torch_compile
 
-    policy = policy_class.from_pretrained(cfg.policy.pretrained_path, config=config)
+    if config.use_peft:
+        from peft import PeftModel
+
+        policy = policy_class(config=config)
+        policy = PeftModel.from_pretrained(policy, cfg.policy.pretrained_path)
+
+    else:
+        policy = policy_class.from_pretrained(cfg.policy.pretrained_path, config=config)
 
     # Turn on RTC
     policy.config.rtc_config = cfg.rtc

src/lerobot/policies/factory.py

@@ -406,7 +406,7 @@ def make_policy(
         cfg.input_features = {key: ft for key, ft in features.items() if key not in cfg.output_features}
     kwargs["config"] = cfg
 
-    if cfg.pretrained_path:
+    if cfg.pretrained_path and not cfg.use_peft:
         # Load a pretrained policy and override the config if needed (for example, if there are inference-time
         # hyperparameters that we want to vary).
         kwargs["pretrained_name_or_path"] = cfg.pretrained_path
@@ -415,6 +415,19 @@ def make_policy(
         # Make a fresh policy.
         policy = policy_cls(**kwargs)
 
+    if cfg.pretrained_path and cfg.use_peft:
+        # Load a pretrained PEFT model on top of the policy. This requires that the policy was instantiated from
+        # scratch since PEFT is handling base model loading via the adapter config.
+        from peft import PeftModel
+
+        logging.info("Loading policy's PEFT adapter.")
+        policy = PeftModel.from_pretrained(policy, cfg.pretrained_path)
+    elif cfg.use_peft:
+        raise ValueError(
+            "Instantiating a policy with `use_peft=True` without a checkpoint is not supported since that requires "
+            "the PEFT config parameters to be set. For traning with PEFT, see `lerobot_train.py` on how to do that."
+        )
+
     policy.to(cfg.device)
     assert isinstance(policy, torch.nn.Module)
 

src/lerobot/scripts/lerobot_record.py

@@ -194,15 +194,9 @@ class RecordConfig:
         if policy_path:
             cli_overrides = parser.get_cli_overrides("policy")
 
-            # In case of a PEFT model We assume that the user saved the policy config (`config.json`) alongside the
-            # adapter parameters / config. If they didn't we could instantiate the default configuration for the policy
-            # but we wouldn't know if that is correct. So, in case of a missing config this will simply fail.
             self.policy = PreTrainedConfig.from_pretrained(policy_path, cli_overrides=cli_overrides)
             self.policy.pretrained_path = policy_path
 
-            if (Path(policy_path) / "adapter_config.json").exists():
-                self.policy.use_peft = True
-
         if self.teleop is None and self.policy is None:
             raise ValueError("Choose a policy, a teleoperator or both to control the robot")
 
@@ -433,19 +427,7 @@ def record(cfg: RecordConfig) -> LeRobotDataset:
         )
 
     # Load pretrained policy
-    if cfg.policy and cfg.policy.use_peft:
-        from peft import PeftModel
-
-        logging.info("Loading policy's PEFT adapter.")
-        # in case of PEFT we re-use the policy pretrained path to point to the adapter path.
-        peft_path = cfg.policy.pretrained_path
-        cfg.policy.pretrained_path = None
-
-        policy = make_policy(cfg.policy, ds_meta=dataset.meta)
-        policy = PeftModel.from_pretrained(policy, peft_path)
-
-    else:
-        policy = None if cfg.policy is None else make_policy(cfg.policy, ds_meta=dataset.meta)
+    policy = None if cfg.policy is None else make_policy(cfg.policy, ds_meta=dataset.meta)
 
     preprocessor = None
     postprocessor = None

src/lerobot/scripts/lerobot_train.py

@@ -188,7 +188,7 @@ def wrap_policy_in_peft_model(cfg, policy):
     if peft_config_cli["init_type"] is not None:
         if peft_method_type == "LORA":
             peft_config_policy["init_lora_weights"] = peft_config_cli["init_type"]
-        elif peft_method_type == "BONE":
+        elif peft_method_type == "MISS":
             peft_config_policy["init_weights"] = peft_config_cli["init_type"]
         else:
             raise ValueError(
@@ -200,6 +200,10 @@ def wrap_policy_in_peft_model(cfg, policy):
         peft_config_cls(**peft_config_policy),
     )
 
+    # Make sure that the config is tagged as using PEFT so that the loading code can take the
+    # appropriate steps to use the adapter weights and the PEFT config instead of the full model weights.
+    policy.config.use_peft = True
+
     return policy