fix(train): restrict legacy RA-BC migration to JSON checkpoints only (#3490 )

* fix(train): restrict legacy RA-BC migration to JSON checkpoints only _migrate_legacy_rabc_fields was called for all config files, causing json.load to raise DecodeError when a YAML/TOML config was passed to lerobot-train for a new training run. Guard the block with an .endswith(".json") check so migration only runs when resuming from a JSON checkpoint.
fix(robots): allign lekiwi default with so100 use_degrees (#3531 )
2026-05-22 12:09:42 +00:00 · 2026-05-08 20:27:01 +02:00 · 2026-05-07 17:52:34 +02:00 · 2026-05-07 13:37:16 +02:00 · 2026-05-07 12:10:38 +02:00
9 changed files with 23 additions and 15 deletions
@@ -382,6 +382,7 @@ jobs:
                --policy.path=\"\$ROBOTWIN_POLICY\" \
                --env.type=robotwin \
                --env.task=\"\$ROBOTWIN_TASKS\" \
+                --env.max_parallel_tasks=5 \
                --eval.batch_size=1 \
                --eval.n_episodes=1 \
                --eval.use_async_envs=false \
@@ -482,6 +483,7 @@ jobs:
                --policy.path=lerobot/smolvla_robocasa \
                --env.type=robocasa \
                --env.task=CloseFridge,OpenCabinet,OpenDrawer,TurnOnMicrowave,TurnOffStove,CloseToasterOvenDoor,SlideDishwasherRack,TurnOnSinkFaucet,NavigateKitchen,TurnOnElectricKettle \
+                --env.max_parallel_tasks=5 \
                --eval.batch_size=1 \
                --eval.n_episodes=1 \
                --eval.use_async_envs=false \
@@ -693,6 +695,7 @@ jobs:
                --env.task=\"\$ROBOMME_TASKS\" \
                --env.dataset_split=test \
                --env.task_ids=[0] \
+                --env.max_parallel_tasks=5 \
                --eval.batch_size=1 \
                --eval.n_episodes=1 \
                --eval.use_async_envs=false \
@@ -800,6 +803,7 @@ jobs:
                --env.type=libero_plus \
                --env.task=\"\$LIBERO_PLUS_SUITE\" \
                --env.task_ids=\"\$LIBERO_PLUS_TASK_IDS\" \
+                --env.max_parallel_tasks=5 \
                --eval.batch_size=1 \
                --eval.n_episodes=1 \
                --eval.use_async_envs=false \
@@ -900,6 +904,8 @@ jobs:
                --policy.path=lerobot/smolvla_vlabench \
                --env.type=vlabench \
                --env.task=select_fruit,select_toy,select_book,select_painting,select_drink,select_ingredient,select_billiards,select_poker,add_condiment,insert_flower \
+                --env.episode_length=50 \
+                --env.max_parallel_tasks=5 \
                --eval.batch_size=1 \
                --eval.n_episodes=1 \
                --eval.use_async_envs=false \
@@ -256,7 +256,9 @@ class TrainPipelineConfig(HubMixin):
                ) from e

        cli_args = kwargs.pop("cli_args", [])
-        if config_file is not None:
+        # Legacy RA-BC migration only applies to framework-saved checkpoints (always JSON).
+        # Hand-written YAML/TOML configs are expected to use the current sample_weighting schema.
+        if config_file is not None and config_file.endswith(".json"):
            with open(config_file) as f:
                config = json.load(f)
            migrated_config = _migrate_legacy_rabc_fields(config)
@@ -100,8 +100,8 @@ class DiffusionConfig(PreTrainedConfig):

    # Inputs / output structure.
    n_obs_steps: int = 2
-    horizon: int = 16
-    n_action_steps: int = 8
+    horizon: int = 64
+    n_action_steps: int = 32

    normalization_mapping: dict[str, NormalizationMode] = field(
        default_factory=lambda: {
@@ -122,10 +122,10 @@ class DiffusionConfig(PreTrainedConfig):
    crop_ratio: float = 1.0
    crop_shape: tuple[int, int] | None = None
    crop_is_random: bool = True
-    pretrained_backbone_weights: str | None = None
-    use_group_norm: bool = True
+    pretrained_backbone_weights: str | None = "ResNet18_Weights.IMAGENET1K_V1"
+    use_group_norm: bool = False
    spatial_softmax_num_keypoints: int = 32
-    use_separate_rgb_encoder_per_camera: bool = False
+    use_separate_rgb_encoder_per_camera: bool = True
    # Unet.
    down_dims: tuple[int, ...] = (512, 1024, 2048)
    kernel_size: int = 5
@@ -97,8 +97,8 @@ class VQBeTConfig(PreTrainedConfig):
    vision_backbone: str = "resnet18"
    crop_shape: tuple[int, int] | None = (84, 84)
    crop_is_random: bool = True
-    pretrained_backbone_weights: str | None = None
-    use_group_norm: bool = True
+    pretrained_backbone_weights: str | None = "ResNet18_Weights.IMAGENET1K_V1"
+    use_group_norm: bool = False
    spatial_softmax_num_keypoints: int = 32
    # VQ-VAE
    n_vqvae_training_steps: int = 20000
@@ -46,7 +46,7 @@ class LeKiwiConfig(RobotConfig):
    cameras: dict[str, CameraConfig] = field(default_factory=lekiwi_cameras_config)

    # Set to `True` for backward compatibility with previous policies/dataset
-    use_degrees: bool = False
+    use_degrees: bool = True


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@@ -1,3 +1,3 @@
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+size 47680
Author	SHA1	Message	Date
masato-ka	0e6114ac36	fix(train): restrict legacy RA-BC migration to JSON checkpoints only (#3490 ) * fix(train): restrict legacy RA-BC migration to JSON checkpoints only _migrate_legacy_rabc_fields was called for all config files, causing json.load to raise DecodeError when a YAML/TOML config was passed to lerobot-train for a new training run. Guard the block with an .endswith(".json") check so migration only runs when resuming from a JSON checkpoint.	2026-05-08 20:27:01 +02:00
Steven Palma	c8ce413d73	fix(robots): allign lekiwi default with so100 use_degrees (#3531 )	2026-05-07 17:52:34 +02:00
Pepijn	82dffde7fa	fix(ci): speed up multi-task benchmark evals (parallelize + cap VLABench steps) (#3529 ) * fix(ci): run multi-task benchmark evals 5-at-a-time in parallel The eval script supports running tasks concurrently via a ThreadPoolExecutor (env.max_parallel_tasks). Apply it to the four multi-task benchmark CI jobs (RoboTwin, RoboCasa, RoboMME, LIBERO-plus — 8-10 tasks/task_ids each) so they finish in ~2 waves of 5 instead of running sequentially. Single-task jobs (Libero, MetaWorld, RoboCerebra) are unchanged. * fix(ci): cap VLABench smoke eval at 50 steps per task VLABench's default episode_length is 500 steps; with 10 tasks at ~1 it/s the smoke eval took ~80 minutes of rollouts on top of the image build. The eval is a pipeline smoke test (running_success_rate stays at 0% on this short rollout anyway), so we don't need full episodes — cap each task at 50 steps to bring total rollout time down ~10x. * fix(ci): run VLABench tasks 5-at-a-time in parallel The eval script already supports running multiple tasks concurrently via a ThreadPoolExecutor (env.max_parallel_tasks). Set it to 5 so the 10 VLABench tasks finish in ~2 waves instead of running sequentially.	2026-05-07 13:37:16 +02:00
Ville Kuosmanen	eaf0218bc8	feat(policy): use pretrained vision encoder weights by default for diffusion and vqbet (#3202 ) * feat: add pretrained vision encoder weights for diffusion and vqbet * fix test by re-generating artifacts --------- Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>	2026-05-07 12:10:38 +02:00