fix(scripts): add missing observation overwrite in eval and async (#2265)

src/lerobot/async_inference/helpers.py

@@ -16,7 +16,7 @@ import logging
 import logging.handlers
 import os
 import time
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 from pathlib import Path
 
 import torch
@@ -268,6 +268,7 @@ class RemotePolicyConfig:
     lerobot_features: dict[str, PolicyFeature]
     actions_per_chunk: int
     device: str = "cpu"
+    rename_map: dict[str, str] = field(default_factory=dict)
 
 
 def _compare_observation_states(obs1_state: torch.Tensor, obs2_state: torch.Tensor, atol: float) -> bool:

src/lerobot/async_inference/policy_server.py

@@ -159,7 +159,10 @@ class PolicyServer(services_pb2_grpc.AsyncInferenceServicer):
         self.preprocessor, self.postprocessor = make_pre_post_processors(
             self.policy.config,
             pretrained_path=policy_specs.pretrained_name_or_path,
-            preprocessor_overrides={"device_processor": device_override},
+            preprocessor_overrides={
+                "device_processor": device_override,
+                "rename_observations_processor": {"rename_map": policy_specs.rename_map},
+            },
             postprocessor_overrides={"device_processor": device_override},
         )
 

src/lerobot/configs/eval.py

@@ -36,6 +36,8 @@ class EvalPipelineConfig:
     output_dir: Path | None = None
     job_name: str | None = None
     seed: int | None = 1000
+    # Rename map for the observation to override the image and state keys
+    rename_map: dict[str, str] = field(default_factory=dict)
 
     def __post_init__(self) -> None:
         # HACK: We parse again the cli args here to get the pretrained path if there was one.

src/lerobot/scripts/lerobot_eval.py

@@ -508,7 +508,10 @@ def eval_main(cfg: EvalPipelineConfig):
         policy_cfg=cfg.policy,
         pretrained_path=cfg.policy.pretrained_path,
         # The inference device is automatically set to match the detected hardware, overriding any previous device settings from training to ensure compatibility.
-        preprocessor_overrides={"device_processor": {"device": str(policy.config.device)}},
+        preprocessor_overrides={
+            "device_processor": {"device": str(policy.config.device)},
+            "rename_observations_processor": {"rename_map": cfg.rename_map},
+        },
     )
     with torch.no_grad(), torch.autocast(device_type=device.type) if cfg.policy.use_amp else nullcontext():
         info = eval_policy_all(