Merge branch 'feat/relative_umi' into feat/openarm_relative_ee

examples/openarms/evaluate_relative.py

@@ -0,0 +1,321 @@
+#!/usr/bin/env python
+"""
+OpenArms Policy Evaluation with Relative Actions
+
+Two modes supported (based on training config):
+  Mode 1: Relative actions only (use_relative_state=False)
+    - Policy outputs relative action deltas
+    - State input is absolute
+  Mode 2: Relative actions + state (use_relative_state=True)
+    - Policy outputs relative action deltas  
+    - State input is also converted to relative
+
+Example usage:
+    python examples/openarms/evaluate_relative.py
+"""
+
+import time
+from pathlib import Path
+
+import torch
+
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig
+from lerobot.configs.policies import PreTrainedConfig
+from lerobot.configs.train import TrainPipelineConfig
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.pipeline_features import aggregate_pipeline_dataset_features, create_initial_features
+from lerobot.datasets.utils import build_dataset_frame, combine_feature_dicts
+from lerobot.policies.factory import make_policy, make_pre_post_processors
+from lerobot.policies.utils import predict_action
+from lerobot.processor import make_default_processors
+from lerobot.processor.core import RobotAction
+from lerobot.robots.openarms.config_openarms_follower import OpenArmsFollowerConfig
+from lerobot.robots.openarms.openarms_follower import OpenArmsFollower
+from lerobot.utils.constants import ACTION, OBS_STR
+from lerobot.utils.control_utils import init_keyboard_listener, precise_sleep
+from lerobot.utils.device_utils import get_safe_torch_device
+from lerobot.utils.relative_actions import (
+    convert_from_relative_actions_dict,
+    convert_state_to_relative,
+    PerTimestepNormalizer,
+)
+from lerobot.utils.utils import log_say
+from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
+
+
+# Configuration
+HF_MODEL_ID = "your-org/your-relative-policy"
+HF_EVAL_DATASET_ID = "your-org/your-eval-dataset"
+TASK_DESCRIPTION = "your task description"
+
+NUM_EPISODES = 1
+FPS = 30
+EPISODE_TIME_SEC = 300
+
+FOLLOWER_LEFT_PORT = "can0"
+FOLLOWER_RIGHT_PORT = "can1"
+
+CAMERA_CONFIG = {
+    "left_wrist": OpenCVCameraConfig(index_or_path="/dev/video5", width=640, height=480, fps=FPS),
+    "right_wrist": OpenCVCameraConfig(index_or_path="/dev/video1", width=640, height=480, fps=FPS),
+    "base": OpenCVCameraConfig(index_or_path="/dev/video3", width=640, height=480, fps=FPS),
+}
+
+
+def make_robot_action(action_values: dict, features: dict) -> RobotAction:
+    robot_action = {}
+    for key in features:
+        if key.startswith(ACTION + "."):
+            action_key = key.removeprefix(ACTION + ".")
+            if action_key in action_values:
+                robot_action[action_key] = action_values[action_key]
+    return robot_action
+
+
+def load_relative_config(model_path: Path | str) -> tuple[PerTimestepNormalizer | None, bool]:
+    """Load normalizer and relative_state setting from checkpoint."""
+    model_path = Path(model_path) if isinstance(model_path, str) else model_path
+    normalizer = None
+    use_relative_state = False
+    
+    # Try local path first
+    if model_path.exists():
+        stats_path = model_path / "relative_stats.pt"
+        if stats_path.exists():
+            normalizer = PerTimestepNormalizer.load(stats_path)
+            print(f"Loaded per-timestep stats from: {stats_path}")
+        
+        config_path = model_path / "train_config.json"
+        if config_path.exists():
+            cfg = TrainPipelineConfig.from_pretrained(model_path)
+            use_relative_state = getattr(cfg, "use_relative_state", False)
+    else:
+        # Try hub
+        try:
+            from huggingface_hub import hf_hub_download
+            stats_file = hf_hub_download(repo_id=str(model_path), filename="relative_stats.pt")
+            normalizer = PerTimestepNormalizer.load(stats_file)
+            print("Loaded per-timestep stats from hub")
+            
+            config_file = hf_hub_download(repo_id=str(model_path), filename="train_config.json")
+            cfg = TrainPipelineConfig.from_pretrained(Path(config_file).parent)
+            use_relative_state = getattr(cfg, "use_relative_state", False)
+        except Exception as e:
+            print(f"Warning: Could not load relative config: {e}")
+    
+    return normalizer, use_relative_state
+
+
+def inference_loop_relative(
+    robot,
+    policy,
+    preprocessor,
+    postprocessor,
+    dataset,
+    events,
+    fps: int,
+    control_time_s: float,
+    single_task: str,
+    display_data: bool = True,
+    state_key: str = "observation.state",
+    relative_normalizer: PerTimestepNormalizer | None = None,
+    use_relative_state: bool = False,
+):
+    """
+    Inference loop for relative action policies.
+    
+    If use_relative_state=True, also converts observation state to relative.
+    """
+    device = get_safe_torch_device(policy.config.device)
+    timestamp = 0
+    start_t = time.perf_counter()
+    
+    while timestamp < control_time_s:
+        loop_start = time.perf_counter()
+        
+        if events["exit_early"] or events["stop_recording"]:
+            break
+        
+        obs = robot.get_observation()
+        observation_frame = build_dataset_frame(dataset.features, obs, prefix=OBS_STR)
+        current_pos = {k: v for k, v in obs.items() if k.endswith(".pos")}
+        
+        # Convert state to relative if using full UMI mode
+        if use_relative_state and state_key in observation_frame:
+            state_tensor = observation_frame[state_key]
+            if isinstance(state_tensor, torch.Tensor):
+                observation_frame[state_key] = convert_state_to_relative(state_tensor)
+        
+        # Policy inference (outputs normalized relative actions)
+        action_values = predict_action(
+            observation=observation_frame,
+            policy=policy,
+            device=device,
+            preprocessor=preprocessor,
+            postprocessor=postprocessor,
+            use_amp=policy.config.use_amp,
+            task=single_task,
+            robot_type=robot.robot_type,
+        )
+        
+        # Unnormalize actions
+        if relative_normalizer is not None:
+            action_keys = [k for k in action_values.keys() if not k.startswith("task")]
+            action_tensor = torch.tensor([[action_values[k] for k in action_keys]])
+            action_tensor = action_tensor.unsqueeze(1)
+            action_unnorm = relative_normalizer.unnormalize(action_tensor)
+            for i, k in enumerate(action_keys):
+                action_values[k] = action_unnorm[0, 0, i].item()
+        
+        # Convert to absolute
+        relative_action = make_robot_action(action_values, dataset.features)
+        absolute_action = convert_from_relative_actions_dict(relative_action, current_pos)
+        
+        robot.send_action(absolute_action)
+        
+        if dataset is not None:
+            action_frame = build_dataset_frame(dataset.features, absolute_action, prefix=ACTION)
+            frame = {**observation_frame, **action_frame, "task": single_task}
+            dataset.add_frame(frame)
+        
+        if display_data:
+            log_rerun_data(observation=obs, action=absolute_action)
+        
+        dt = time.perf_counter() - loop_start
+        precise_sleep(1 / fps - dt)
+        timestamp = time.perf_counter() - start_t
+
+
+def main():
+    print("=" * 60)
+    print("  OpenArms Evaluation - Relative Actions")
+    print("=" * 60)
+    print(f"\nModel: {HF_MODEL_ID}")
+    print(f"Dataset: {HF_EVAL_DATASET_ID}")
+    print(f"Episodes: {NUM_EPISODES}, Duration: {EPISODE_TIME_SEC}s")
+    
+    # Load relative action config
+    relative_normalizer, use_relative_state = load_relative_config(HF_MODEL_ID)
+    mode = "actions + state" if use_relative_state else "actions only"
+    print(f"Mode: relative {mode}")
+    
+    # Setup robot
+    follower_config = OpenArmsFollowerConfig(
+        port_left=FOLLOWER_LEFT_PORT,
+        port_right=FOLLOWER_RIGHT_PORT,
+        can_interface="socketcan",
+        id="openarms_follower",
+        disable_torque_on_disconnect=True,
+        max_relative_target=10.0,
+        cameras=CAMERA_CONFIG,
+    )
+    
+    follower = OpenArmsFollower(follower_config)
+    follower.connect(calibrate=False)
+    
+    if not follower.is_connected:
+        raise RuntimeError("Robot failed to connect!")
+
+    teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors()
+    action_features_hw = {k: v for k, v in follower.action_features.items() if k.endswith(".pos")}
+    
+    dataset_features = combine_feature_dicts(
+        aggregate_pipeline_dataset_features(
+            pipeline=teleop_action_processor,
+            initial_features=create_initial_features(action=action_features_hw),
+            use_videos=True,
+        ),
+        aggregate_pipeline_dataset_features(
+            pipeline=robot_observation_processor,
+            initial_features=create_initial_features(observation=follower.observation_features),
+            use_videos=True,
+        ),
+    )
+    
+    dataset_path = Path.home() / ".cache" / "huggingface" / "lerobot" / HF_EVAL_DATASET_ID
+    if dataset_path.exists():
+        print(f"\nDataset exists at: {dataset_path}")
+        if input("Continue? (y/n): ").strip().lower() != 'y':
+            follower.disconnect()
+            return
+    
+    dataset = LeRobotDataset.create(
+        repo_id=HF_EVAL_DATASET_ID,
+        fps=FPS,
+        features=dataset_features,
+        robot_type=follower.name,
+        use_videos=True,
+        image_writer_processes=0,
+        image_writer_threads=12, 
+    )
+    
+    policy_config = PreTrainedConfig.from_pretrained(HF_MODEL_ID)
+    policy_config.pretrained_path = HF_MODEL_ID
+    policy = make_policy(policy_config, ds_meta=dataset.meta)
+    
+    preprocessor, postprocessor = make_pre_post_processors(
+        policy_cfg=policy.config,
+        pretrained_path=HF_MODEL_ID,
+        dataset_stats=dataset.meta.stats,
+        preprocessor_overrides={"device_processor": {"device": str(policy.config.device)}},
+    )
+
+    listener, events = init_keyboard_listener()
+    init_rerun(session_name="openarms_eval_relative")
+    episode_idx = 0
+    
+    print("\nControls: ESC=stop, →=next episode, ←=rerecord")
+    
+    try:
+        while episode_idx < NUM_EPISODES and not events["stop_recording"]:
+            log_say(f"Episode {episode_idx + 1} of {NUM_EPISODES}")
+            
+            inference_loop_relative(
+                robot=follower,
+                policy=policy,
+                preprocessor=preprocessor,
+                postprocessor=postprocessor,
+                dataset=dataset,
+                events=events,
+                fps=FPS,
+                control_time_s=EPISODE_TIME_SEC,
+                single_task=TASK_DESCRIPTION,
+                display_data=True,
+                relative_normalizer=relative_normalizer,
+                use_relative_state=use_relative_state,
+            )
+            
+            if events.get("rerecord_episode", False):
+                log_say("Re-recording")
+                events["rerecord_episode"] = False
+                events["exit_early"] = False
+                dataset.clear_episode_buffer()
+                continue
+            
+            if dataset.episode_buffer is not None and dataset.episode_buffer.get("size", 0) > 0:
+                print(f"Saving episode {episode_idx + 1}...")
+                dataset.save_episode()
+                episode_idx += 1
+            
+            events["exit_early"] = False
+            
+            if not events["stop_recording"] and episode_idx < NUM_EPISODES:
+                input("Press ENTER for next episode...")
+        
+        print(f"\nDone! {episode_idx} episodes recorded")
+        log_say("Complete", blocking=True)
+    
+    except KeyboardInterrupt:
+        print("\n\nInterrupted")
+    
+    finally:
+        follower.disconnect()
+        if listener is not None:
+            listener.stop()
+        dataset.finalize()
+        print("Uploading to Hub...")
+        dataset.push_to_hub(private=True)
+
+
+if __name__ == "__main__":
+    main()

scripts/unify_tasks.py

@@ -0,0 +1,59 @@
+#!/usr/bin/env python
+"""Unify all tasks in a dataset to a single task."""
+
+import argparse
+import json
+from pathlib import Path
+
+import pandas as pd
+
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.utils import write_tasks
+
+
+def unify_tasks(repo_id: str, new_task: str):
+    """Set all episodes to use a single task."""
+    print(f"Loading dataset: {repo_id}")
+    dataset = LeRobotDataset(repo_id)
+    root = dataset.root
+    
+    print(f"Current tasks: {list(dataset.meta.tasks['task']) if dataset.meta.tasks is not None else []}")
+    
+    # 1. Update tasks.parquet to have only one task
+    tasks_df = pd.DataFrame({"task": [new_task]})
+    write_tasks(tasks_df, root)
+    print(f"Set single task: '{new_task}'")
+    
+    # 2. Update all data parquet files to set task_index=0
+    data_dir = root / "data"
+    parquet_files = sorted(data_dir.glob("*/*.parquet"))
+    for parquet_path in parquet_files:
+        df = pd.read_parquet(parquet_path)
+        df["task_index"] = 0
+        df.to_parquet(parquet_path)
+        print(f"Updated: {parquet_path.relative_to(root)}")
+    
+    # 3. Update info.json
+    info_path = root / "info.json"
+    with open(info_path) as f:
+        info = json.load(f)
+    info["total_tasks"] = 1
+    with open(info_path, "w") as f:
+        json.dump(info, f, indent=2)
+    
+    print(f"\nDone! All {dataset.meta.total_episodes} episodes now use task: '{new_task}'")
+    print(f"\nTo push: huggingface-cli upload {repo_id} {root} --repo-type dataset")
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Unify all tasks in a dataset to a single task")
+    parser.add_argument("--repo_id", type=str, required=True, help="Dataset repo_id")
+    parser.add_argument("--task", type=str, required=True, help="New task description")
+    args = parser.parse_args()
+    
+    unify_tasks(args.repo_id, args.task)
+
+
+if __name__ == "__main__":
+    main()
+

src/lerobot/configs/train.py

@@ -66,6 +66,17 @@ class TrainPipelineConfig(HubMixin):
     eval: EvalConfig = field(default_factory=EvalConfig)
     wandb: WandBConfig = field(default_factory=WandBConfig)
 
+    # UMI-style relative actions with per-timestep normalization
+    # Mode 1: use_relative_actions=True, use_relative_state=False
+    #   - Actions: relative to current position + per-timestep normalized
+    #   - State: absolute (unchanged)
+    # Mode 2: use_relative_actions=True, use_relative_state=True (full UMI)
+    #   - Actions: relative to current position + per-timestep normalized  
+    #   - State: relative to current position (provides velocity info)
+    # Stats are computed automatically from first 1000 batches at training start
+    use_relative_actions: bool = False
+    use_relative_state: bool = False
+
     # RA-BC (Reward-Aligned Behavior Cloning) parameters
     use_rabc: bool = False  # Enable reward-weighted training
     rabc_progress_path: str | None = None  # Path to precomputed SARM progress parquet file

src/lerobot/scripts/lerobot_train.py

@@ -13,6 +13,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import gc
 import logging
 import time
 from contextlib import nullcontext
@@ -46,6 +47,11 @@ from lerobot.utils.train_utils import (
     save_checkpoint,
     update_last_checkpoint,
 )
+from lerobot.utils.relative_actions import (
+    convert_to_relative_actions,
+    compute_relative_action_stats,
+    PerTimestepNormalizer,
+)
 from lerobot.utils.utils import (
     format_big_number,
     has_method,
@@ -298,6 +304,41 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
             device=device,
         )
 
+    # Compute per-timestep normalizer for relative actions
+    relative_normalizer = None
+    if cfg.use_relative_actions:
+        mode = "actions + state" if cfg.use_relative_state else "actions only"
+        cfg.output_dir.mkdir(parents=True, exist_ok=True)
+        stats_path = cfg.output_dir / "relative_stats.pt"
+        
+        if is_main_process:
+            logging.info(colored(f"Relative mode: {mode}", "cyan", attrs=["bold"]))
+            
+            if stats_path.exists():
+                logging.info(f"Loading pre-computed stats from: {stats_path}")
+            else:
+                logging.info("Computing per-timestep stats (first 1000 batches)...")
+                logging.info("Using fresh dataset to avoid video decoder state issues...")
+                # Create separate dataset instance to avoid corrupting main dataset's video decoders
+                stats_dataset = make_dataset(cfg)
+                temp_loader = torch.utils.data.DataLoader(
+                    stats_dataset, batch_size=cfg.batch_size, shuffle=False, num_workers=0
+                )
+                mean, std = compute_relative_action_stats(temp_loader, num_batches=1000)
+                del temp_loader, stats_dataset
+                gc.collect()
+                torch.save({"mean": mean, "std": std}, stats_path)
+                logging.info(f"Saved stats to: {stats_path}")
+        
+        # Poll for stats file instead of using NCCL barrier (avoids timeout during long computation)
+        if not is_main_process:
+            while not stats_path.exists():
+                time.sleep(5)
+        
+        data = torch.load(stats_path, weights_only=True, map_location="cpu")
+        relative_normalizer = PerTimestepNormalizer(data["mean"], data["std"])
+        accelerator.wait_for_everyone()  # Sync after everyone has loaded
+
     step = 0  # number of policy updates (forward + backward + optim)
 
     if cfg.resume:
@@ -385,6 +426,13 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
         start_time = time.perf_counter()
         batch = next(dl_iter)
         batch = preprocessor(batch)
+        
+        # Convert to relative actions (and optionally state) if enabled
+        if cfg.use_relative_actions:
+            batch = convert_to_relative_actions(batch, convert_state=cfg.use_relative_state)
+            if relative_normalizer is not None:
+                batch["action"] = relative_normalizer.normalize(batch["action"])
+        
         train_tracker.dataloading_s = time.perf_counter() - start_time
 
         train_tracker, output_dict = update_policy(
@@ -439,6 +487,9 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
                     preprocessor=preprocessor,
                     postprocessor=postprocessor,
                 )
+                # Save relative action stats with checkpoint
+                if relative_normalizer is not None:
+                    relative_normalizer.save(checkpoint_dir / "relative_stats.pt")
                 update_last_checkpoint(checkpoint_dir)
                 if wandb_logger:
                     wandb_logger.log_policy(checkpoint_dir)

src/lerobot/utils/relative_actions.py

@@ -0,0 +1,159 @@
+"""
+UMI-style relative actions with per-timestep normalization.
+
+Two modes supported:
+  Mode 1: Relative actions only (use_relative_state=False)
+    - Actions converted to relative, state stays absolute
+  Mode 2: Relative actions + state (use_relative_state=True, full UMI)
+    - Both actions and state converted to relative
+
+Per-timestep normalization (TRI LBM / BEHAVIOR style):
+  Training: action_norm[t] = (action_rel[t] - mean[t]) / std[t]
+  Inference: action_rel[t] = action_norm[t] * std[t] + mean[t]
+"""
+
+import torch
+from pathlib import Path
+
+
+class PerTimestepNormalizer:
+    """Per-timestep normalization using precomputed dataset statistics."""
+    
+    def __init__(self, mean: torch.Tensor, std: torch.Tensor, eps: float = 1e-8):
+        self.mean = mean
+        self.std = std
+        self.eps = eps
+        self._cache = {}  # Cache for device/dtype converted tensors
+    
+    def _get_stats(self, device, dtype):
+        """Get cached stats for device/dtype, or create and cache them."""
+        key = (device, dtype)
+        if key not in self._cache:
+            self._cache[key] = (
+                self.mean.to(device, dtype),
+                self.std.to(device, dtype),
+            )
+        return self._cache[key]
+    
+    def normalize(self, x: torch.Tensor) -> torch.Tensor:
+        mean, std = self._get_stats(x.device, x.dtype)
+        if x.dim() == 3 and mean.dim() == 2:
+            mean, std = mean.unsqueeze(0), std.unsqueeze(0)
+        return (x - mean) / (std + self.eps)
+    
+    def unnormalize(self, x: torch.Tensor) -> torch.Tensor:
+        mean, std = self._get_stats(x.device, x.dtype)
+        if x.dim() == 3 and mean.dim() == 2:
+            mean, std = mean.unsqueeze(0), std.unsqueeze(0)
+        return x * (std + self.eps) + mean
+    
+    def save(self, path: Path | str):
+        path = Path(path)
+        path.parent.mkdir(parents=True, exist_ok=True)
+        torch.save({"mean": self.mean.cpu(), "std": self.std.cpu(), "eps": self.eps}, path)
+    
+    @classmethod
+    def load(cls, path: Path | str) -> "PerTimestepNormalizer":
+        data = torch.load(path, weights_only=True, map_location="cpu")
+        return cls(data["mean"], data["std"], data.get("eps", 1e-8))
+
+
+def compute_relative_action_stats(
+    dataloader,
+    state_key: str = "observation.state",
+    num_batches: int | None = None,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Compute per-timestep mean/std from relative actions."""
+    all_rel = []
+    for i, batch in enumerate(dataloader):
+        if num_batches is not None and i >= num_batches:
+            break
+        action, state = batch["action"], batch[state_key]
+        current_pos = state[:, -1, :] if state.dim() == 3 else state
+        min_dim = min(action.shape[-1], current_pos.shape[-1])
+        rel = action.clone()
+        rel[..., :min_dim] -= current_pos[:, None, :min_dim]
+        all_rel.append(rel)
+    
+    all_rel = torch.cat(all_rel, dim=0)
+    return all_rel.mean(dim=0), all_rel.std(dim=0).clamp(min=1e-6)
+
+
+def convert_to_relative(
+    batch: dict,
+    state_key: str = "observation.state",
+    convert_state: bool = True,
+) -> dict:
+    """
+    Convert actions (and optionally state) to relative.
+    
+    Args:
+        batch: Training batch with "action" and state_key
+        state_key: Key for observation state
+        convert_state: If True, also convert state to relative (full UMI mode)
+    """
+    if "action" not in batch or state_key not in batch:
+        return batch
+    
+    action = batch["action"]
+    state = batch[state_key]
+    batch = batch.copy()
+    
+    # Get current position as reference
+    current_pos = state[:, -1, :] if state.dim() == 3 else state
+    
+    # Convert state if requested
+    if convert_state:
+        if state.dim() == 3:
+            batch[state_key] = state - current_pos[:, None, :]
+        else:
+            batch[state_key] = torch.zeros_like(state)
+    
+    # Convert actions to relative
+    min_dim = min(action.shape[-1], current_pos.shape[-1])
+    rel_action = action.clone()
+    rel_action[..., :min_dim] -= current_pos[:, None, :min_dim]
+    batch["action"] = rel_action
+    
+    return batch
+
+
+# Backward compatibility alias
+convert_to_relative_actions = convert_to_relative
+
+
+def convert_state_to_relative(state: torch.Tensor) -> torch.Tensor:
+    """Convert state to relative (for inference with use_relative_state=True)."""
+    if state.dim() == 1:
+        return torch.zeros_like(state)
+    current_pos = state[-1, :] if state.dim() == 2 else state[:, -1, :]
+    if state.dim() == 2:
+        return state - current_pos[None, :]
+    return state - current_pos[:, None, :]
+
+
+def convert_from_relative_actions(
+    relative_actions: torch.Tensor,
+    current_pos: torch.Tensor,
+) -> torch.Tensor:
+    """Convert relative actions back to absolute for robot execution."""
+    current_pos = current_pos.to(relative_actions.device, relative_actions.dtype)
+    min_dim = min(relative_actions.shape[-1], current_pos.shape[-1])
+    absolute = relative_actions.clone()
+    
+    if relative_actions.dim() == 2:
+        absolute[..., :min_dim] += current_pos[:min_dim]
+    elif relative_actions.dim() == 3:
+        absolute[..., :min_dim] += current_pos[None, None, :min_dim]
+    else:
+        absolute[..., :min_dim] += current_pos[:min_dim]
+    
+    return absolute
+
+
+def convert_from_relative_actions_dict(
+    relative_actions: dict[str, float],
+    current_pos: dict[str, float],
+) -> dict[str, float]:
+    """Convert relative actions back to absolute (dict version for inference)."""
+    return {k: v + current_pos.get(k, 0.0) for k, v in relative_actions.items()}