docs: reframe HIL guide around general intervention workflow

Signed-off-by: Steven Palma <imstevenpmwork@ieee.org>

docs/source/_toctree.yml

@@ -17,6 +17,8 @@
     title: Train RL in Simulation
   - local: multi_gpu_training
     title: Multi GPU training
+  - local: hil_collection
+    title: Human In the Loop Data Collection
   - local: peft_training
     title: Training with PEFT (e.g., LoRA)
   title: "Tutorials"

docs/source/hil_collection.mdx

@@ -0,0 +1,237 @@
+# Human-In-the-Loop Data Collection
+
+Human-In-the-Loop (HIL) data collection lets you improve a trained policy by deploying it on a real robot while a human operator monitors and intervenes when needed. The intervention data — recovery movements and corrections — is recorded alongside autonomous segments, producing a richer training dataset that teaches the policy how to handle failures.
+
+---
+
+## Why Human-In-the-Loop?
+
+Standard behavioral cloning trains policies on successful demonstrations only. During deployment, small errors can compound and push the robot into states never seen during training (distribution shift). HIL data collection addresses this by:
+
+- Running the trained policy on the real robot
+- Having a human intervene when the robot is about to fail
+- Recording the human's recovery and correction as training data
+- Fine-tuning the policy on the combined dataset
+
+This produces a policy that not only knows how to perform the task, but also how to recover when things go wrong.
+
+---
+
+## How It Works
+
+During a HIL session, the human operator follows this loop within each episode:
+
+1. **Watch** the policy run autonomously
+2. **Pause** when failure is imminent — the robot holds its position
+3. **Take control** — teleoperate the robot back to a good state (recovery), then correct the behavior
+4. **Return control to the policy** — the policy resumes autonomous execution
+5. Repeat steps 2–4 as many times as needed during the episode
+6. **End the episode** when the task is complete, save and move on to the next rollout
+
+Both autonomous and human-controlled segments are recorded. The policy and human can alternate control multiple times within a single episode, and the episode continues from the current state after each handoff (no reset required just because intervention happened). This captures autonomous execution, recovery, and correction in one continuous trajectory. After collection, the combined dataset (original demonstrations + HIL data) is used to fine-tune the policy.
+
+This process can be repeated iteratively: deploy, collect, fine-tune, repeat — each round targeting the current policy's failure modes.
+
+```
+┌─────────────────────────────────────────────────────────────────────────┐
+│  Policy v0 (trained on demos)                                           │
+│       ↓                                                                 │
+│  HIL Collection (target current failure modes) → Fine-tune → Policy v1  │
+│       ↓                                                                 │
+│  HIL Collection (target new failure modes) → Fine-tune → Policy v2      │
+│       ↓                                                                 │
+│  ... (repeat until satisfactory performance)                            │
+└─────────────────────────────────────────────────────────────────────────┘
+```
+
+---
+
+## Hardware Requirements
+
+### Teleoperator Requirements
+
+The HIL data collection scripts require **teleoperators with active motors** that can:
+
+- Enable/disable torque programmatically
+- Move to target positions (to mirror the robot state when pausing)
+
+**Compatible teleoperators:**
+
+- `so101_leader` - SO-101 Leader Arm
+- `openarms_mini` - OpenArms Mini (via third-party plugin)
+
+---
+
+## Scripts
+
+Two scripts are provided depending on your policy's inference speed:
+
+| Script                       | Use Case                                   | Models                |
+| ---------------------------- | ------------------------------------------ | --------------------- |
+| `hil_data_collection.py`     | Standard synchronous inference             | ACT, Diffusion Policy |
+| `hil_data_collection_rtc.py` | Real-Time Chunking for high-latency models | Pi0, Pi0.5, SmolVLA   |
+
+---
+
+## Step-by-Step Guide
+
+### Step 1: Pre-train a Base Policy
+
+First, train a policy on your demonstration dataset:
+
+```bash
+python src/lerobot/scripts/lerobot_train.py \
+    --dataset.repo_id=your-username/demo-dataset \
+    --policy.type=pi0 \
+    --output_dir=outputs/pretrain \
+    --batch_size=32 \
+    --steps=50000
+```
+
+### Step 2: Collect HIL Data
+
+**Standard inference (ACT, Diffusion Policy):**
+
+```bash
+python examples/rac/hil_data_collection.py \
+    --robot.type=so100_follower \
+    --robot.port=/dev/tty.usbmodem58760431541 \
+    --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
+    --teleop.type=so100_leader \
+    --teleop.port=/dev/tty.usbmodem58760431551 \
+    --policy.path=outputs/pretrain/checkpoints/last/pretrained_model \
+    --dataset.repo_id=your-username/hil-dataset \
+    --dataset.single_task="Pick up the cube and place it in the bowl" \
+    --dataset.num_episodes=50
+```
+
+**With RTC for large models (Pi0, Pi0.5, SmolVLA):**
+
+For models with high inference latency, use the RTC script for smooth execution:
+
+```bash
+python examples/rac/hil_data_collection_rtc.py \
+    --robot.type=so100_follower \
+    --teleop.type=so100_leader \
+    --policy.path=outputs/pretrain/checkpoints/last/pretrained_model \
+    --dataset.repo_id=your-username/hil-rtc-dataset \
+    --dataset.single_task="Pick up the cube" \
+    --rtc.execution_horizon=20 \
+    --interpolation=true
+```
+
+**Controls (Conceptual):**
+
+The interaction model is:
+
+- **Pause input**: pause autonomous policy execution
+- **Takeover input**: transfer control to the human operator and record intervention data
+- **Return-to-policy input**: hand control back to the policy and continue the same episode
+- **Episode control inputs**: save/re-record/stop/reset as needed
+
+Exact key/pedal bindings can differ across scripts and hardware integrations. Use each script's printed controls as the source of truth for the concrete mapping on your setup.
+
+**The HIL Protocol:**
+
+1. Watch the policy run autonomously (teleop is idle/free)
+2. When you see imminent failure, trigger the **pause input**
+   - Policy stops
+   - Teleoperator moves to match robot position (torque enabled)
+   - No frames recorded during pause
+3. Trigger the **takeover input** to take control
+   - Teleoperator torque disabled, free to move
+   - **Recovery**: Teleoperate the robot back to a good state
+   - **Correction**: Correct the behavior
+   - All movements are recorded
+4. Trigger the **return-to-policy input**
+   - Policy resumes autonomous execution from the current state
+   - You can intervene again at any time (repeat steps 2–4)
+5. End and save the episode when the task is complete (or episode time limit is reached)
+6. **Reset**: Teleop moves to robot position, you can move the robot to the starting position
+7. Start the next episode
+
+**Foot Pedal Setup (Linux):**
+
+If using a USB foot pedal (PCsensor FootSwitch), ensure access:
+
+```bash
+sudo setfacl -m u:$USER:rw /dev/input/by-id/usb-PCsensor_FootSwitch-event-kbd
+```
+
+### Step 3: Fine-tune the Policy
+
+Fine-tune on the combined demonstration + HIL data:
+
+```bash
+python src/lerobot/scripts/lerobot_train.py \
+    --dataset.repo_id=your-username/hil-dataset \
+    --policy.type=pi0 \
+    --policy.pretrained_path=outputs/pretrain/checkpoints/last/pretrained_model \
+    --output_dir=outputs/hil_finetune \
+    --steps=20000
+```
+
+Then deploy the fine-tuned policy and repeat from Step 2 to target its remaining failure modes.
+
+---
+
+## Tips for Effective HIL Collection
+
+### When to Intervene
+
+Intervene when you see:
+
+- Robot about to make an irreversible mistake
+- Robot hesitating or showing uncertain behavior
+- Robot deviating from the expected trajectory
+
+### Recovery: Teleoperating Back to a Good State
+
+During recovery, teleoperate the robot back to a state where:
+
+- The robot is in a familiar, in-distribution configuration
+- The current subtask can still be completed
+- The recovery trajectory itself is informative training data
+
+### Quality of Corrections
+
+During correction:
+
+- Provide **confident, clean** trajectories
+- Complete the current subtask fully
+- Don't overcorrect or add unnecessary movements
+
+---
+
+## Related Work
+
+This HIL data collection approach builds on ideas from interactive imitation learning, including DAgger (Ross et al., 2011), HG-DAgger (Kelly et al., 2019), RaC (Hu et al., 2025), and RECAP (Physical Intelligence, 2025). See those works for a deeper treatment of the theory behind human-in-the-loop policy improvement.
+
+```bibtex
+@article{ross2011dagger,
+  title={A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning},
+  author={Ross, Stéphane and Gordon, Geoffrey and Bagnell, Drew},
+  journal={Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics},
+  year={2011}
+}
+
+@article{kelly2019hgdagger,
+  title={HG-DAgger: Interactive Imitation Learning with Human Experts},
+  author={Kelly, Michael and Sidrane, Chelsea and Driggs-Campbell, Katherine and Kochenderfer, Mykel J},
+  journal={arXiv preprint arXiv:1810.02890},
+  year={2019}
+}
+
+@article{hu2025rac,
+  title={RaC: Robot Learning for Long-Horizon Tasks by Scaling Recovery and Correction},
+  author={Hu, Zheyuan and Wu, Robyn and Enock, Naveen and Li, Jasmine and Kadakia, Riya and Erickson, Zackory and Kumar, Aviral},
+  journal={arXiv preprint arXiv:2509.07953},
+  year={2025}
+}
+
+@article{pi2025recap,
+  title={π0.6: a VLA That Learns From Experience},
+  author={Physical Intelligence},
+  year={2025}
+}
+```

examples/rac/hil_data_collection.py

@@ -0,0 +1,351 @@
+#!/usr/bin/env python
+"""
+Human-in-the-Loop (HIL) Data Collection with Policy Rollout.
+
+Implements the RaC paradigm (Hu et al., 2025) for LeRobot with standard synchronous
+inference. For large models with high inference latency, use hil_data_collection_rtc.py.
+
+The workflow:
+1. Policy runs autonomously
+2. Press SPACE to pause - robot holds position
+3. Press 'c' to take control - human provides RECOVERY + CORRECTION
+4. Press → to end episode (save and continue to next)
+5. Reset, then do next rollout
+
+Keyboard Controls:
+    SPACE  - Pause policy (robot holds position, no recording)
+    c      - Take control (start correction, recording resumes)
+    →      - End episode (save and continue to next)
+    ←      - Re-record episode
+    ESC    - Stop recording and push dataset to hub
+
+Usage:
+    python examples/rac/hil_data_collection.py \
+        --robot.type=so100_follower \
+        --robot.port=/dev/tty.usbmodem58760431541 \
+        --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
+        --teleop.type=so100_leader \
+        --teleop.port=/dev/tty.usbmodem58760431551 \
+        --policy.path=outputs/train/my_policy/checkpoints/last/pretrained_model \
+        --dataset.repo_id=my_user/hil_dataset \
+        --dataset.single_task="Pick up the cube"
+"""
+
+import logging
+import time
+from dataclasses import dataclass
+from pprint import pformat
+from typing import Any
+
+import torch
+from hil_utils import (
+    HILDatasetConfig,
+    init_keyboard_listener,
+    make_identity_processors,
+    print_controls,
+    reset_loop,
+    teleop_disable_torque,
+    teleop_smooth_move_to,
+)
+
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig  # noqa: F401
+from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraConfig  # noqa: F401
+from lerobot.configs import parser
+from lerobot.configs.policies import PreTrainedConfig
+from lerobot.datasets.image_writer import safe_stop_image_writer
+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.datasets.video_utils import VideoEncodingManager
+from lerobot.policies.factory import make_policy, make_pre_post_processors
+from lerobot.policies.pretrained import PreTrainedPolicy
+from lerobot.policies.rtc import ActionInterpolator
+from lerobot.policies.utils import make_robot_action
+from lerobot.processor import PolicyProcessorPipeline
+from lerobot.processor.rename_processor import rename_stats
+from lerobot.robots import Robot, RobotConfig, make_robot_from_config
+from lerobot.teleoperators import Teleoperator, TeleoperatorConfig, make_teleoperator_from_config
+from lerobot.utils.constants import ACTION, OBS_STR
+from lerobot.utils.control_utils import is_headless, predict_action
+from lerobot.utils.robot_utils import precise_sleep
+from lerobot.utils.utils import get_safe_torch_device, init_logging, log_say
+from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class HILConfig:
+    robot: RobotConfig
+    teleop: TeleoperatorConfig
+    dataset: HILDatasetConfig
+    policy: PreTrainedConfig | None = None
+    interpolation_multiplier: int = 1  # Control rate multiplier (1=off, 2=2x, 3=3x)
+    display_data: bool = True
+    play_sounds: bool = True
+    resume: bool = False
+    device: str = "cuda"
+
+    def __post_init__(self):
+        policy_path = parser.get_path_arg("policy")
+        if policy_path:
+            cli_overrides = parser.get_cli_overrides("policy")
+            self.policy = PreTrainedConfig.from_pretrained(policy_path, cli_overrides=cli_overrides)
+            self.policy.pretrained_path = policy_path
+        if self.policy is None:
+            raise ValueError("policy.path is required")
+
+    @classmethod
+    def __get_path_fields__(cls) -> list[str]:
+        return ["policy"]
+
+
+@safe_stop_image_writer
+def rollout_loop(
+    robot: Robot,
+    teleop: Teleoperator,
+    policy: PreTrainedPolicy,
+    preprocessor: PolicyProcessorPipeline,
+    postprocessor: PolicyProcessorPipeline,
+    dataset: LeRobotDataset,
+    events: dict,
+    cfg: HILConfig,
+):
+    """Rollout loop with standard synchronous inference."""
+    fps = cfg.dataset.fps
+    device = get_safe_torch_device(cfg.device)
+
+    policy.reset()
+    preprocessor.reset()
+    postprocessor.reset()
+
+    frame_buffer = []
+    teleop_disable_torque(teleop)
+
+    was_paused = False
+    waiting_for_takeover = False
+    last_action: dict[str, Any] | None = None
+    robot_action: dict[str, Any] = {}
+    action_keys = sorted(robot.action_features.keys())
+
+    interpolator = ActionInterpolator(multiplier=cfg.interpolation_multiplier)
+    control_interval = interpolator.get_control_interval(fps)
+
+    timestamp = 0
+    start_t = time.perf_counter()
+
+    while timestamp < cfg.dataset.episode_time_s:
+        loop_start = time.perf_counter()
+
+        if events["exit_early"]:
+            events["exit_early"] = False
+            events["policy_paused"] = False
+            events["correction_active"] = False
+            break
+
+        # Transition to paused state
+        if events["policy_paused"] and not was_paused:
+            obs = robot.get_observation()
+            robot_pos = {
+                k: v for k, v in obs.items() if k.endswith(".pos") and k in robot.observation_features
+            }
+            teleop_smooth_move_to(teleop, robot_pos, duration_s=2.0, fps=50)
+            events["start_next_episode"] = False
+            waiting_for_takeover = True
+            was_paused = True
+            interpolator.reset()
+
+        # Takeover
+        if waiting_for_takeover and events["start_next_episode"]:
+            teleop_disable_torque(teleop)
+            events["start_next_episode"] = False
+            events["correction_active"] = True
+            waiting_for_takeover = False
+
+        obs = robot.get_observation()
+        obs_filtered = {k: v for k, v in obs.items() if k in robot.observation_features}
+        obs_frame = build_dataset_frame(dataset.features, obs_filtered, prefix=OBS_STR)
+
+        if events["correction_active"]:
+            robot_action = teleop.get_action()
+            robot.send_action(robot_action)
+            action_frame = build_dataset_frame(dataset.features, robot_action, prefix=ACTION)
+            frame_buffer.append({**obs_frame, **action_frame, "task": cfg.dataset.single_task})
+
+        elif waiting_for_takeover or events["policy_paused"]:
+            if last_action:
+                robot.send_action(last_action)
+
+        else:
+            # Policy execution with optional interpolation
+            if interpolator.needs_new_action():
+                action_values = predict_action(
+                    observation=obs_frame,
+                    policy=policy,
+                    device=device,
+                    preprocessor=preprocessor,
+                    postprocessor=postprocessor,
+                    use_amp=policy.config.use_amp,
+                    task=cfg.dataset.single_task,
+                    robot_type=robot.robot_type,
+                )
+                robot_action = make_robot_action(action_values, dataset.features)
+                action_tensor = torch.tensor([robot_action[k] for k in action_keys])
+                interpolator.add(action_tensor)
+
+            interp_action = interpolator.get()
+            if interp_action is not None:
+                robot_action = {k: interp_action[i].item() for i, k in enumerate(action_keys)}
+                robot.send_action(robot_action)
+                last_action = robot_action
+                action_frame = build_dataset_frame(dataset.features, robot_action, prefix=ACTION)
+                frame_buffer.append({**obs_frame, **action_frame, "task": cfg.dataset.single_task})
+
+        if cfg.display_data and robot_action:
+            log_rerun_data(observation=obs_filtered, action=robot_action)
+
+        dt = time.perf_counter() - loop_start
+        if (sleep_time := control_interval - dt) > 0:
+            precise_sleep(sleep_time)
+        timestamp = time.perf_counter() - start_t
+
+    teleop_disable_torque(teleop)
+
+    for frame in frame_buffer:
+        dataset.add_frame(frame)
+
+
+@parser.wrap()
+def hil_collect(cfg: HILConfig) -> LeRobotDataset:
+    """Main HIL data collection function."""
+    init_logging()
+    logger.info(pformat(cfg.__dict__))
+
+    if cfg.display_data:
+        init_rerun(session_name="hil_collection")
+
+    robot = make_robot_from_config(cfg.robot)
+    teleop = make_teleoperator_from_config(cfg.teleop)
+
+    teleop_proc, obs_proc = make_identity_processors()
+
+    dataset_features = combine_feature_dicts(
+        aggregate_pipeline_dataset_features(
+            pipeline=teleop_proc,
+            initial_features=create_initial_features(action=robot.action_features),
+            use_videos=cfg.dataset.video,
+        ),
+        aggregate_pipeline_dataset_features(
+            pipeline=obs_proc,
+            initial_features=create_initial_features(observation=robot.observation_features),
+            use_videos=cfg.dataset.video,
+        ),
+    )
+
+    dataset = None
+    listener = None
+
+    try:
+        if cfg.resume:
+            dataset = LeRobotDataset(
+                cfg.dataset.repo_id,
+                root=cfg.dataset.root,
+                batch_encoding_size=cfg.dataset.video_encoding_batch_size,
+            )
+            if hasattr(robot, "cameras") and robot.cameras:
+                dataset.start_image_writer(
+                    num_processes=cfg.dataset.num_image_writer_processes,
+                    num_threads=cfg.dataset.num_image_writer_threads_per_camera * len(robot.cameras),
+                )
+        else:
+            dataset = LeRobotDataset.create(
+                cfg.dataset.repo_id,
+                cfg.dataset.fps,
+                root=cfg.dataset.root,
+                robot_type=robot.name,
+                features=dataset_features,
+                use_videos=cfg.dataset.video,
+                image_writer_processes=cfg.dataset.num_image_writer_processes,
+                image_writer_threads=cfg.dataset.num_image_writer_threads_per_camera
+                * len(robot.cameras if hasattr(robot, "cameras") else []),
+                batch_encoding_size=cfg.dataset.video_encoding_batch_size,
+            )
+
+        policy = make_policy(cfg.policy, ds_meta=dataset.meta)
+        preprocessor, postprocessor = make_pre_post_processors(
+            policy_cfg=cfg.policy,
+            pretrained_path=cfg.policy.pretrained_path,
+            dataset_stats=rename_stats(dataset.meta.stats, cfg.dataset.rename_map),
+            preprocessor_overrides={
+                "device_processor": {"device": cfg.device},
+                "rename_observations_processor": {"rename_map": cfg.dataset.rename_map},
+            },
+        )
+
+        robot.connect()
+        teleop.connect()
+        listener, events = init_keyboard_listener()
+
+        print_controls(rtc=False)
+        print(f"  Policy: {cfg.policy.pretrained_path}")
+        print(f"  Task: {cfg.dataset.single_task}")
+        print(f"  Interpolation: {cfg.interpolation_multiplier}x\n")
+
+        with VideoEncodingManager(dataset):
+            recorded = 0
+            while recorded < cfg.dataset.num_episodes and not events["stop_recording"]:
+                log_say(f"Episode {dataset.num_episodes}", cfg.play_sounds)
+
+                rollout_loop(
+                    robot=robot,
+                    teleop=teleop,
+                    policy=policy,
+                    preprocessor=preprocessor,
+                    postprocessor=postprocessor,
+                    dataset=dataset,
+                    events=events,
+                    cfg=cfg,
+                )
+
+                if events["rerecord_episode"]:
+                    log_say("Re-recording", cfg.play_sounds)
+                    events["rerecord_episode"] = False
+                    events["exit_early"] = False
+                    dataset.clear_episode_buffer()
+                    continue
+
+                dataset.save_episode()
+                recorded += 1
+
+                if recorded < cfg.dataset.num_episodes and not events["stop_recording"]:
+                    reset_loop(robot, teleop, events, cfg.dataset.fps)
+
+    finally:
+        log_say("Stop recording", cfg.play_sounds, blocking=True)
+
+        if dataset:
+            dataset.finalize()
+
+        if robot.is_connected:
+            robot.disconnect()
+        if teleop.is_connected:
+            teleop.disconnect()
+
+        if not is_headless() and listener:
+            listener.stop()
+
+        if cfg.dataset.push_to_hub:
+            dataset.push_to_hub(tags=cfg.dataset.tags, private=cfg.dataset.private)
+
+    return dataset
+
+
+def main():
+    from lerobot.utils.import_utils import register_third_party_plugins
+
+    register_third_party_plugins()
+    hil_collect()
+
+
+if __name__ == "__main__":
+    main()

examples/rac/hil_data_collection_rtc.py

@@ -0,0 +1,513 @@
+#!/usr/bin/env python
+"""
+Human-in-the-Loop (HIL) Data Collection with Real-Time Chunking (RTC).
+
+Implements the RaC paradigm (Hu et al., 2025) with RTC for large flow-matching models
+(Pi0, Pi0.5, SmolVLA) that have high inference latency. RTC generates action chunks
+asynchronously in a background thread for smooth robot control.
+
+For fast models (ACT, Diffusion), use hil_data_collection.py instead.
+
+The workflow:
+1. Policy runs autonomously with RTC
+2. Press SPACE to pause - robot holds position
+3. Press 'c' to take control - human provides RECOVERY + CORRECTION
+4. Press → to end episode (save and continue to next)
+5. Reset, then do next rollout
+
+Keyboard Controls:
+    SPACE  - Pause policy (robot holds position, no recording)
+    c      - Take control (start correction, recording resumes)
+    →      - End episode (save and continue to next)
+    ←      - Re-record episode
+    ESC    - Stop recording and push dataset to hub
+
+Usage:
+    python examples/rac/hil_data_collection_rtc.py \
+        --robot.type=so100_follower \
+        --robot.port=/dev/tty.usbmodem58760431541 \
+        --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
+        --teleop.type=so100_leader \
+        --teleop.port=/dev/tty.usbmodem58760431551 \
+        --policy.path=outputs/train/pi0_policy/checkpoints/last/pretrained_model \
+        --dataset.repo_id=my_user/hil_rtc_dataset \
+        --dataset.single_task="Pick up the cube" \
+        --rtc.execution_horizon=20
+"""
+
+import logging
+import math
+import time
+from dataclasses import dataclass, field
+from pprint import pformat
+from threading import Event, Lock, Thread
+from typing import Any
+
+import torch
+from hil_utils import (
+    HILDatasetConfig,
+    init_keyboard_listener,
+    make_identity_processors,
+    print_controls,
+    reset_loop,
+    teleop_disable_torque,
+    teleop_smooth_move_to,
+)
+
+from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig  # noqa: F401
+from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraConfig  # noqa: F401
+from lerobot.configs import parser
+from lerobot.configs.policies import PreTrainedConfig
+from lerobot.datasets.image_writer import safe_stop_image_writer
+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, hw_to_dataset_features
+from lerobot.datasets.video_utils import VideoEncodingManager
+from lerobot.policies.factory import get_policy_class, make_pre_post_processors
+from lerobot.policies.pretrained import PreTrainedPolicy
+from lerobot.policies.rtc import ActionInterpolator, ActionQueue, LatencyTracker, RTCConfig
+from lerobot.processor import PolicyProcessorPipeline
+from lerobot.processor.rename_processor import rename_stats
+from lerobot.robots import Robot, RobotConfig, make_robot_from_config
+from lerobot.teleoperators import Teleoperator, TeleoperatorConfig, make_teleoperator_from_config
+from lerobot.utils.constants import ACTION, OBS_STR
+from lerobot.utils.control_utils import is_headless
+from lerobot.utils.robot_utils import precise_sleep
+from lerobot.utils.utils import init_logging, log_say
+from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class HILRTCConfig:
+    robot: RobotConfig
+    teleop: TeleoperatorConfig
+    dataset: HILDatasetConfig
+    policy: PreTrainedConfig | None = None
+    rtc: RTCConfig = field(default_factory=lambda: RTCConfig(enabled=True, execution_horizon=20))
+    interpolation_multiplier: int = 2  # Control rate multiplier (1=off, 2=2x, 3=3x)
+    display_data: bool = True
+    play_sounds: bool = True
+    resume: bool = False
+    device: str = "cuda"
+    use_torch_compile: bool = False  # First compile takes minutes, disable for real-time
+
+    def __post_init__(self):
+        policy_path = parser.get_path_arg("policy")
+        if policy_path:
+            cli_overrides = parser.get_cli_overrides("policy")
+            self.policy = PreTrainedConfig.from_pretrained(policy_path, cli_overrides=cli_overrides)
+            self.policy.pretrained_path = policy_path
+        if self.policy is None:
+            raise ValueError("policy.path is required")
+        self.rtc.enabled = True
+
+    @classmethod
+    def __get_path_fields__(cls) -> list[str]:
+        return ["policy"]
+
+
+class ThreadSafeRobot:
+    """Thread-safe wrapper for robot operations."""
+
+    def __init__(self, robot: Robot):
+        self._robot = robot
+        self._lock = Lock()
+
+    def get_observation(self) -> dict[str, Any]:
+        with self._lock:
+            return self._robot.get_observation()
+
+    def send_action(self, action: dict) -> None:
+        with self._lock:
+            self._robot.send_action(action)
+
+    @property
+    def observation_features(self) -> dict:
+        return self._robot.observation_features
+
+    @property
+    def action_features(self) -> dict:
+        return self._robot.action_features
+
+    @property
+    def name(self) -> str:
+        return self._robot.name
+
+    @property
+    def robot_type(self) -> str:
+        return self._robot.robot_type
+
+    @property
+    def cameras(self):
+        return getattr(self._robot, "cameras", {})
+
+
+def rtc_inference_thread(
+    policy: PreTrainedPolicy,
+    obs_holder: dict,
+    hw_features: dict,
+    preprocessor: PolicyProcessorPipeline,
+    postprocessor: PolicyProcessorPipeline,
+    queue_holder: dict,
+    shutdown_event: Event,
+    policy_active: Event,
+    cfg: HILRTCConfig,
+):
+    """Background thread for RTC action chunk generation."""
+    latency_tracker = LatencyTracker()
+    time_per_chunk = 1.0 / cfg.dataset.fps
+    threshold = 30
+
+    while not shutdown_event.is_set():
+        if not policy_active.is_set():
+            time.sleep(0.01)
+            continue
+
+        queue = queue_holder.get("queue")
+        obs = obs_holder.get("obs")
+        if queue is None or obs is None:
+            time.sleep(0.01)
+            continue
+
+        if queue.qsize() <= threshold:
+            try:
+                current_time = time.perf_counter()
+                idx_before = queue.get_action_index()
+                prev_actions = queue.get_left_over()
+
+                latency = latency_tracker.max()
+                delay = math.ceil(latency / time_per_chunk) if latency else 0
+
+                obs_batch = build_dataset_frame(hw_features, obs, prefix="observation")
+                for name in obs_batch:
+                    obs_batch[name] = torch.from_numpy(obs_batch[name])
+                    if "image" in name:
+                        obs_batch[name] = obs_batch[name].float() / 255
+                        obs_batch[name] = obs_batch[name].permute(2, 0, 1).contiguous()
+                    obs_batch[name] = obs_batch[name].unsqueeze(0).to(cfg.device)
+
+                obs_batch["task"] = [cfg.dataset.single_task]
+                obs_batch["robot_type"] = obs_holder.get("robot_type", "unknown")
+
+                preprocessed = preprocessor(obs_batch)
+                actions = policy.predict_action_chunk(
+                    preprocessed, inference_delay=delay, prev_chunk_left_over=prev_actions
+                )
+
+                original = actions.squeeze(0).clone()
+                processed = postprocessor(actions).squeeze(0)
+                new_latency = time.perf_counter() - current_time
+                new_delay = math.ceil(new_latency / time_per_chunk)
+                latency_tracker.add(new_latency)
+                queue.merge(original, processed, new_delay, idx_before)
+                logger.debug(f"[RTC] Inference latency={new_latency:.2f}s, queue={queue.qsize()}")
+            except Exception as e:
+                logger.error(f"[RTC] Error: {e}")
+                time.sleep(0.5)
+        else:
+            time.sleep(0.01)
+
+
+@safe_stop_image_writer
+def rollout_loop(
+    robot: ThreadSafeRobot,
+    teleop: Teleoperator,
+    policy: PreTrainedPolicy,
+    preprocessor: PolicyProcessorPipeline,
+    postprocessor: PolicyProcessorPipeline,
+    dataset: LeRobotDataset,
+    events: dict,
+    cfg: HILRTCConfig,
+    queue_holder: dict,
+    obs_holder: dict,
+    policy_active: Event,
+    hw_features: dict,
+):
+    """Rollout loop with RTC for asynchronous inference."""
+    fps = cfg.dataset.fps
+
+    policy.reset()
+    preprocessor.reset()
+    postprocessor.reset()
+
+    frame_buffer = []
+    teleop_disable_torque(teleop)
+
+    was_paused = False
+    waiting_for_takeover = False
+    last_action: dict[str, Any] | None = None
+    action_keys = [k for k in robot.action_features if k.endswith(".pos")]
+
+    interpolator = ActionInterpolator(multiplier=cfg.interpolation_multiplier)
+    control_interval = interpolator.get_control_interval(fps)
+
+    robot_action: dict[str, Any] = {}
+    timestamp = 0
+    start_t = time.perf_counter()
+
+    while timestamp < cfg.dataset.episode_time_s:
+        loop_start = time.perf_counter()
+
+        if events["exit_early"]:
+            events["exit_early"] = False
+            events["policy_paused"] = False
+            events["correction_active"] = False
+            break
+
+        # Transition to paused state
+        if events["policy_paused"] and not was_paused:
+            policy_active.clear()
+            obs = robot.get_observation()
+            robot_pos = {
+                k: v for k, v in obs.items() if k.endswith(".pos") and k in robot.observation_features
+            }
+            teleop_smooth_move_to(teleop, robot_pos, duration_s=2.0, fps=50)
+            events["start_next_episode"] = False
+            waiting_for_takeover = True
+            was_paused = True
+            interpolator.reset()
+
+        # Takeover
+        if waiting_for_takeover and events["start_next_episode"]:
+            teleop_disable_torque(teleop)
+            events["start_next_episode"] = False
+            events["correction_active"] = True
+            waiting_for_takeover = False
+
+        obs = robot.get_observation()
+        obs_filtered = {k: v for k, v in obs.items() if k in robot.observation_features}
+        obs_frame = build_dataset_frame(dataset.features, obs_filtered, prefix=OBS_STR)
+
+        obs_holder["obs"] = obs_filtered
+
+        if events["correction_active"]:
+            robot_action = teleop.get_action()
+            robot.send_action(robot_action)
+            action_frame = build_dataset_frame(dataset.features, robot_action, prefix=ACTION)
+            frame_buffer.append({**obs_frame, **action_frame, "task": cfg.dataset.single_task})
+
+        elif waiting_for_takeover or events["policy_paused"]:
+            if last_action:
+                robot.send_action(last_action)
+
+        else:
+            # Policy execution with RTC
+            if not policy_active.is_set():
+                policy_active.set()
+
+            queue = queue_holder["queue"]
+
+            if interpolator.needs_new_action():
+                new_action = queue.get() if queue else None
+                if new_action is not None:
+                    interpolator.add(new_action.cpu())
+
+            action_tensor = interpolator.get()
+            if action_tensor is not None:
+                robot_action = {
+                    k: action_tensor[i].item() for i, k in enumerate(action_keys) if i < len(action_tensor)
+                }
+                robot.send_action(robot_action)
+                last_action = robot_action
+                action_frame = build_dataset_frame(dataset.features, robot_action, prefix=ACTION)
+                frame_buffer.append({**obs_frame, **action_frame, "task": cfg.dataset.single_task})
+
+        if cfg.display_data and robot_action:
+            log_rerun_data(observation=obs_filtered, action=robot_action)
+
+        dt = time.perf_counter() - loop_start
+        if (sleep_time := control_interval - dt) > 0:
+            precise_sleep(sleep_time)
+        timestamp = time.perf_counter() - start_t
+
+    policy_active.clear()
+    teleop_disable_torque(teleop)
+
+    for frame in frame_buffer:
+        dataset.add_frame(frame)
+
+
+@parser.wrap()
+def hil_rtc_collect(cfg: HILRTCConfig) -> LeRobotDataset:
+    """Main HIL data collection function with RTC."""
+    init_logging()
+    logger.info(pformat(cfg.__dict__))
+
+    if cfg.display_data:
+        init_rerun(session_name="hil_rtc_collection")
+
+    robot_raw = make_robot_from_config(cfg.robot)
+    teleop = make_teleoperator_from_config(cfg.teleop)
+
+    teleop_proc, obs_proc = make_identity_processors()
+
+    dataset_features = combine_feature_dicts(
+        aggregate_pipeline_dataset_features(
+            pipeline=teleop_proc,
+            initial_features=create_initial_features(action=robot_raw.action_features),
+            use_videos=cfg.dataset.video,
+        ),
+        aggregate_pipeline_dataset_features(
+            pipeline=obs_proc,
+            initial_features=create_initial_features(observation=robot_raw.observation_features),
+            use_videos=cfg.dataset.video,
+        ),
+    )
+
+    dataset = None
+    listener = None
+    shutdown_event = Event()
+    policy_active = Event()
+    rtc_thread = None
+
+    try:
+        if cfg.resume:
+            dataset = LeRobotDataset(
+                cfg.dataset.repo_id,
+                root=cfg.dataset.root,
+                batch_encoding_size=cfg.dataset.video_encoding_batch_size,
+            )
+            if hasattr(robot_raw, "cameras") and robot_raw.cameras:
+                dataset.start_image_writer(
+                    num_processes=cfg.dataset.num_image_writer_processes,
+                    num_threads=cfg.dataset.num_image_writer_threads_per_camera * len(robot_raw.cameras),
+                )
+        else:
+            dataset = LeRobotDataset.create(
+                cfg.dataset.repo_id,
+                cfg.dataset.fps,
+                root=cfg.dataset.root,
+                robot_type=robot_raw.name,
+                features=dataset_features,
+                use_videos=cfg.dataset.video,
+                image_writer_processes=cfg.dataset.num_image_writer_processes,
+                image_writer_threads=cfg.dataset.num_image_writer_threads_per_camera
+                * len(robot_raw.cameras if hasattr(robot_raw, "cameras") else []),
+                batch_encoding_size=cfg.dataset.video_encoding_batch_size,
+            )
+
+        # Load policy with RTC
+        policy_class = get_policy_class(cfg.policy.type)
+        policy_config = PreTrainedConfig.from_pretrained(cfg.policy.pretrained_path)
+        if hasattr(policy_config, "compile_model"):
+            policy_config.compile_model = cfg.use_torch_compile
+        policy = policy_class.from_pretrained(cfg.policy.pretrained_path, config=policy_config)
+        policy.config.rtc_config = cfg.rtc
+        if hasattr(policy, "init_rtc_processor"):
+            policy.init_rtc_processor()
+        policy = policy.to(cfg.device)
+        policy.eval()
+
+        preprocessor, postprocessor = make_pre_post_processors(
+            policy_cfg=cfg.policy,
+            pretrained_path=cfg.policy.pretrained_path,
+            dataset_stats=rename_stats(dataset.meta.stats, cfg.dataset.rename_map),
+            preprocessor_overrides={
+                "device_processor": {"device": cfg.device},
+                "rename_observations_processor": {"rename_map": cfg.dataset.rename_map},
+            },
+        )
+
+        robot_raw.connect()
+        robot = ThreadSafeRobot(robot_raw)
+        teleop.connect()
+        listener, events = init_keyboard_listener()
+
+        queue_holder = {"queue": ActionQueue(cfg.rtc)}
+        obs_holder = {"obs": None, "robot_type": robot.robot_type}
+        hw_features = hw_to_dataset_features(robot_raw.observation_features, "observation")
+
+        rtc_thread = Thread(
+            target=rtc_inference_thread,
+            args=(
+                policy,
+                obs_holder,
+                hw_features,
+                preprocessor,
+                postprocessor,
+                queue_holder,
+                shutdown_event,
+                policy_active,
+                cfg,
+            ),
+            daemon=True,
+        )
+        rtc_thread.start()
+
+        print_controls(rtc=True)
+        print(f"  Policy: {cfg.policy.pretrained_path}")
+        print(f"  Task: {cfg.dataset.single_task}")
+        print(f"  Interpolation: {cfg.interpolation_multiplier}x\n")
+
+        with VideoEncodingManager(dataset):
+            recorded = 0
+            while recorded < cfg.dataset.num_episodes and not events["stop_recording"]:
+                log_say(f"Episode {dataset.num_episodes}", cfg.play_sounds)
+
+                queue_holder["queue"] = ActionQueue(cfg.rtc)
+
+                rollout_loop(
+                    robot=robot,
+                    teleop=teleop,
+                    policy=policy,
+                    preprocessor=preprocessor,
+                    postprocessor=postprocessor,
+                    dataset=dataset,
+                    events=events,
+                    cfg=cfg,
+                    queue_holder=queue_holder,
+                    obs_holder=obs_holder,
+                    policy_active=policy_active,
+                    hw_features=hw_features,
+                )
+
+                if events["rerecord_episode"]:
+                    log_say("Re-recording", cfg.play_sounds)
+                    events["rerecord_episode"] = False
+                    events["exit_early"] = False
+                    dataset.clear_episode_buffer()
+                    continue
+
+                dataset.save_episode()
+                recorded += 1
+
+                if recorded < cfg.dataset.num_episodes and not events["stop_recording"]:
+                    reset_loop(robot, teleop, events, cfg.dataset.fps)
+
+    finally:
+        log_say("Stop recording", cfg.play_sounds, blocking=True)
+
+        shutdown_event.set()
+        policy_active.clear()
+
+        if rtc_thread and rtc_thread.is_alive():
+            rtc_thread.join(timeout=2.0)
+
+        if dataset:
+            dataset.finalize()
+
+        if robot_raw.is_connected:
+            robot_raw.disconnect()
+        if teleop.is_connected:
+            teleop.disconnect()
+
+        if not is_headless() and listener:
+            listener.stop()
+
+        if cfg.dataset.push_to_hub:
+            dataset.push_to_hub(tags=cfg.dataset.tags, private=cfg.dataset.private)
+
+    return dataset
+
+
+def main():
+    from lerobot.utils.import_utils import register_third_party_plugins
+
+    register_third_party_plugins()
+    hil_rtc_collect()
+
+
+if __name__ == "__main__":
+    main()

examples/rac/hil_utils.py

@@ -0,0 +1,206 @@
+"""Shared utilities for Human-in-the-Loop data collection scripts."""
+
+import logging
+import time
+from dataclasses import dataclass, field
+from pathlib import Path
+
+from lerobot.processor import (
+    IdentityProcessorStep,
+    RobotAction,
+    RobotObservation,
+    RobotProcessorPipeline,
+)
+from lerobot.processor.converters import (
+    observation_to_transition,
+    robot_action_observation_to_transition,
+    transition_to_observation,
+    transition_to_robot_action,
+)
+from lerobot.robots import Robot
+from lerobot.teleoperators import Teleoperator
+from lerobot.utils.control_utils import is_headless
+from lerobot.utils.robot_utils import precise_sleep
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class HILDatasetConfig:
+    repo_id: str
+    single_task: str
+    root: str | Path | None = None
+    fps: int = 30
+    episode_time_s: float = 120
+    num_episodes: int = 50
+    video: bool = True
+    push_to_hub: bool = True
+    private: bool = False
+    tags: list[str] | None = None
+    num_image_writer_processes: int = 0
+    num_image_writer_threads_per_camera: int = 4
+    video_encoding_batch_size: int = 1
+    rename_map: dict[str, str] = field(default_factory=dict)
+
+
+def teleop_has_motor_control(teleop: Teleoperator) -> bool:
+    """Check if teleoperator has motor control capabilities."""
+    return hasattr(teleop, "bus") and hasattr(teleop.bus, "disable_torque")
+
+
+def teleop_disable_torque(teleop: Teleoperator) -> None:
+    """Disable teleop torque if supported."""
+    if teleop_has_motor_control(teleop):
+        teleop.bus.disable_torque()
+
+
+def teleop_enable_torque(teleop: Teleoperator) -> None:
+    """Enable teleop torque if supported."""
+    if teleop_has_motor_control(teleop):
+        teleop.bus.enable_torque()
+
+
+def teleop_smooth_move_to(teleop: Teleoperator, target_pos: dict, duration_s: float = 2.0, fps: int = 50):
+    """Smoothly move teleop to target position if motor control is available."""
+    if not teleop_has_motor_control(teleop):
+        logger.warning("Teleop does not support motor control - cannot mirror robot position")
+        return
+
+    teleop_enable_torque(teleop)
+    current = teleop.get_action()
+    steps = int(duration_s * fps)
+
+    for step in range(steps + 1):
+        t = step / steps
+        interp = {}
+        for k in current:
+            if k in target_pos:
+                interp[k] = current[k] * (1 - t) + target_pos[k] * t
+            else:
+                interp[k] = current[k]
+        teleop.bus.sync_write("Goal_Position", {k.replace(".pos", ""): v for k, v in interp.items()})
+        time.sleep(1 / fps)
+
+
+def init_keyboard_listener():
+    """Initialize keyboard listener with HIL controls."""
+    events = {
+        "exit_early": False,
+        "rerecord_episode": False,
+        "stop_recording": False,
+        "policy_paused": False,
+        "correction_active": False,
+        "in_reset": False,
+        "start_next_episode": False,
+    }
+
+    if is_headless():
+        logger.warning("Headless environment - keyboard controls unavailable")
+        return None, events
+
+    from pynput import keyboard
+
+    def on_press(key):
+        try:
+            if events["in_reset"]:
+                if key in [keyboard.Key.space, keyboard.Key.right]:
+                    print("\n[HIL] Starting next episode...")
+                    events["start_next_episode"] = True
+                elif hasattr(key, "char") and key.char == "c":
+                    events["start_next_episode"] = True
+                elif key == keyboard.Key.esc:
+                    print("[HIL] ESC - Stop recording, pushing to hub...")
+                    events["stop_recording"] = True
+                    events["start_next_episode"] = True
+            else:
+                if key == keyboard.Key.space:
+                    if not events["policy_paused"] and not events["correction_active"]:
+                        print("\n[HIL] ⏸ PAUSED - Press 'c' to take control")
+                        events["policy_paused"] = True
+                elif hasattr(key, "char") and key.char == "c":
+                    if events["policy_paused"] and not events["correction_active"]:
+                        print("\n[HIL] ▶ Taking control...")
+                        events["start_next_episode"] = True
+                elif key == keyboard.Key.right:
+                    print("[HIL] → End episode")
+                    events["exit_early"] = True
+                elif key == keyboard.Key.left:
+                    print("[HIL] ← Re-record episode")
+                    events["rerecord_episode"] = True
+                    events["exit_early"] = True
+                elif key == keyboard.Key.esc:
+                    print("[HIL] ESC - Stop recording...")
+                    events["stop_recording"] = True
+                    events["exit_early"] = True
+        except Exception as e:
+            print(f"Key error: {e}")
+
+    listener = keyboard.Listener(on_press=on_press)
+    listener.start()
+    return listener, events
+
+
+def make_identity_processors():
+    """Create identity processors for recording."""
+    teleop_proc = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
+        steps=[IdentityProcessorStep()],
+        to_transition=robot_action_observation_to_transition,
+        to_output=transition_to_robot_action,
+    )
+    obs_proc = RobotProcessorPipeline[RobotObservation, RobotObservation](
+        steps=[IdentityProcessorStep()],
+        to_transition=observation_to_transition,
+        to_output=transition_to_observation,
+    )
+    return teleop_proc, obs_proc
+
+
+def reset_loop(robot: Robot, teleop: Teleoperator, events: dict, fps: int):
+    """Reset period where human repositions environment."""
+    print("\n" + "=" * 60)
+    print("  [HIL] RESET")
+    print("=" * 60)
+
+    events["in_reset"] = True
+    events["start_next_episode"] = False
+
+    obs = robot.get_observation()
+    robot_pos = {k: v for k, v in obs.items() if k.endswith(".pos") and k in robot.observation_features}
+    teleop_smooth_move_to(teleop, robot_pos, duration_s=2.0, fps=50)
+
+    print("  Press any key to enable teleoperation")
+    while not events["start_next_episode"] and not events["stop_recording"]:
+        precise_sleep(0.05)
+
+    if events["stop_recording"]:
+        return
+
+    events["start_next_episode"] = False
+    teleop_disable_torque(teleop)
+    print("  Teleop enabled - press any key to start episode")
+
+    while not events["start_next_episode"] and not events["stop_recording"]:
+        loop_start = time.perf_counter()
+        action = teleop.get_action()
+        robot.send_action(action)
+        precise_sleep(1 / fps - (time.perf_counter() - loop_start))
+
+    events["in_reset"] = False
+    events["start_next_episode"] = False
+    events["exit_early"] = False
+    events["policy_paused"] = False
+    events["correction_active"] = False
+
+
+def print_controls(rtc: bool = False):
+    """Print control instructions."""
+    print("\n" + "=" * 60)
+    print("  Human-in-the-Loop Data Collection" + (" (RTC)" if rtc else ""))
+    print("=" * 60)
+    print()
+    print("  Controls:")
+    print("    SPACE  - Pause policy")
+    print("    c      - Take control")
+    print("    →      - End episode")
+    print("    ESC    - Stop and push to hub")
+    print("=" * 60 + "\n")

examples/rtc/eval_with_real_robot.py

@@ -83,9 +83,7 @@ from lerobot.configs.policies import PreTrainedConfig
 from lerobot.configs.types import RTCAttentionSchedule
 from lerobot.datasets.utils import build_dataset_frame, hw_to_dataset_features
 from lerobot.policies.factory import get_policy_class, make_pre_post_processors
-from lerobot.policies.rtc.action_queue import ActionQueue
-from lerobot.policies.rtc.configuration_rtc import RTCConfig
-from lerobot.policies.rtc.latency_tracker import LatencyTracker
+from lerobot.policies.rtc import ActionInterpolator, ActionQueue, LatencyTracker, RTCConfig
 from lerobot.processor.factory import (
     make_default_robot_action_processor,
     make_default_robot_observation_processor,
@@ -151,6 +149,7 @@ class RTCDemoConfig(HubMixin):
     # Demo parameters
     duration: float = 30.0  # Duration to run the demo (seconds)
     fps: float = 10.0  # Action execution frequency (Hz)
+    interpolation_multiplier: int = 1  # Control rate multiplier (1=off, 2=2x, 3=3x)
 
     # Compute device
     device: str | None = None  # Device to run on (cuda, cpu, auto)
@@ -351,20 +350,22 @@ def actor_control(
         logger.info("[ACTOR] Starting actor thread")
 
         action_count = 0
-        action_interval = 1.0 / cfg.fps
+        interpolator = ActionInterpolator(multiplier=cfg.interpolation_multiplier)
+        action_interval = interpolator.get_control_interval(cfg.fps)
 
         while not shutdown_event.is_set():
             start_time = time.perf_counter()
 
-            # Try to get an action from the queue with timeout
-            action = action_queue.get()
+            if interpolator.needs_new_action():
+                new_action = action_queue.get()
+                if new_action is not None:
+                    interpolator.add(new_action.cpu())
 
+            action = interpolator.get()
             if action is not None:
-                action = action.cpu()
                 action_dict = {key: action[i].item() for i, key in enumerate(robot.action_features())}
                 action_processed = robot_action_processor((action_dict, None))
                 robot.send_action(action_processed)
-
                 action_count += 1
 
             dt_s = time.perf_counter() - start_time

src/lerobot/policies/rtc/__init__.py

@@ -0,0 +1,29 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+
+"""Real-Time Chunking (RTC) utilities for action-chunking policies."""
+
+from lerobot.policies.rtc.action_interpolator import ActionInterpolator
+from lerobot.policies.rtc.action_queue import ActionQueue
+from lerobot.policies.rtc.configuration_rtc import RTCConfig
+from lerobot.policies.rtc.latency_tracker import LatencyTracker
+from lerobot.policies.rtc.modeling_rtc import RTCProcessor
+
+__all__ = [
+    "ActionInterpolator",
+    "ActionQueue",
+    "LatencyTracker",
+    "RTCConfig",
+    "RTCProcessor",
+]

src/lerobot/policies/rtc/action_interpolator.py

@@ -0,0 +1,117 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+
+"""Action interpolation for smoother robot control.
+
+Provides configurable Nx control rate by interpolating between consecutive actions.
+Useful with RTC and action-chunking policies to reduce jerkiness.
+"""
+
+from torch import Tensor
+
+
+class ActionInterpolator:
+    """Interpolates between consecutive actions for smoother control.
+
+    When enabled with multiplier N, produces N actions per policy action
+    by linearly interpolating between the previous and current action.
+
+    Example with multiplier=3:
+        prev_action -> [1/3 interpolated, 2/3 interpolated, current_action]
+
+    This effectively multiplies the control rate for smoother motion.
+
+    Usage:
+        interpolator = ActionInterpolator(multiplier=2)  # 2x control rate
+
+        # In control loop:
+        if interpolator.needs_new_action():
+            new_action = queue.get()
+            if new_action:
+                interpolator.add(new_action.cpu())
+
+        action = interpolator.get()
+        if action:
+            robot.send_action(action)
+    """
+
+    def __init__(self, multiplier: int = 1):
+        """Initialize the interpolator.
+
+        Args:
+            multiplier: Control rate multiplier (1 = no interpolation, 2 = 2x, 3 = 3x, etc.)
+        """
+        if multiplier < 1:
+            raise ValueError(f"multiplier must be >= 1, got {multiplier}")
+        self.multiplier = multiplier
+        self._prev: Tensor | None = None
+        self._buffer: list[Tensor] = []
+        self._idx = 0
+
+    @property
+    def enabled(self) -> bool:
+        """Whether interpolation is active (multiplier > 1)."""
+        return self.multiplier > 1
+
+    def reset(self):
+        """Reset interpolation state (call between episodes)."""
+        self._prev = None
+        self._buffer = []
+        self._idx = 0
+
+    def needs_new_action(self) -> bool:
+        """Check if a new action is needed from the queue."""
+        return self._idx >= len(self._buffer)
+
+    def add(self, action: Tensor) -> None:
+        """Add a new action and compute interpolated sequence.
+
+        Args:
+            action: New action tensor from policy/queue (already on CPU).
+        """
+        if self.multiplier > 1 and self._prev is not None:
+            self._buffer = []
+            for i in range(1, self.multiplier + 1):
+                t = i / self.multiplier
+                interp = self._prev + t * (action - self._prev)
+                self._buffer.append(interp)
+        else:
+            self._buffer = [action]
+        self._prev = action
+        self._idx = 0
+
+    def get(self) -> Tensor | None:
+        """Get the next interpolated action.
+
+        Returns:
+            Next action tensor, or None if buffer is exhausted.
+        """
+        if self._idx >= len(self._buffer):
+            return None
+        action = self._buffer[self._idx]
+        self._idx += 1
+        return action
+
+    def get_control_interval(self, fps: float) -> float:
+        """Get the control interval based on interpolation multiplier.
+
+        Args:
+            fps: Base frames per second.
+
+        Returns:
+            Control interval in seconds (divided by multiplier).
+        """
+        return 1.0 / (fps * self.multiplier)

src/lerobot/scripts/lerobot_record.py

@@ -74,6 +74,8 @@ from pathlib import Path
 from pprint import pformat
 from typing import Any
 
+import torch
+
 from lerobot.cameras import (  # noqa: F401
     CameraConfig,  # noqa: F401
 )
@@ -90,6 +92,7 @@ from lerobot.datasets.utils import build_dataset_frame, combine_feature_dicts
 from lerobot.datasets.video_utils import VideoEncodingManager
 from lerobot.policies.factory import make_policy, make_pre_post_processors
 from lerobot.policies.pretrained import PreTrainedPolicy
+from lerobot.policies.rtc import ActionInterpolator
 from lerobot.policies.utils import make_robot_action
 from lerobot.processor import (
     PolicyAction,
@@ -225,6 +228,9 @@ class RecordConfig:
     play_sounds: bool = True
     # Resume recording on an existing dataset.
     resume: bool = False
+    # Action interpolation multiplier for smoother policy control (1=off, 2=2x, 3=3x)
+    # Only applies when using a policy (not teleop)
+    interpolation_multiplier: int = 1
 
     def __post_init__(self):
         # HACK: We parse again the cli args here to get the pretrained path if there was one.
@@ -297,6 +303,7 @@ def record_loop(
     control_time_s: int | None = None,
     single_task: str | None = None,
     display_data: bool = False,
+    interpolator: ActionInterpolator | None = None,
     display_compressed_images: bool = False,
 ):
     if dataset is not None and dataset.fps != fps:
@@ -333,6 +340,14 @@ def record_loop(
         preprocessor.reset()
         postprocessor.reset()
 
+    # Reset interpolator if provided
+    if interpolator is not None:
+        interpolator.reset()
+
+    # Calculate control interval based on interpolation
+    use_interpolation = interpolator is not None and interpolator.enabled and policy is not None
+    control_interval = interpolator.get_control_interval(fps) if interpolator else 1 / fps
+
     timestamp = 0
     start_episode_t = time.perf_counter()
     while timestamp < control_time_s:
@@ -353,24 +368,58 @@ def record_loop(
 
         # Get action from either policy or teleop
         if policy is not None and preprocessor is not None and postprocessor is not None:
-            action_values = predict_action(
-                observation=observation_frame,
-                policy=policy,
-                device=get_safe_torch_device(policy.config.device),
-                preprocessor=preprocessor,
-                postprocessor=postprocessor,
-                use_amp=policy.config.use_amp,
-                task=single_task,
-                robot_type=robot.robot_type,
-            )
+            # With interpolation: only call policy when interpolator needs new action
+            if use_interpolation:
+                # Get action keys from robot
+                action_keys = sorted(robot.action_features)
 
-            act_processed_policy: RobotAction = make_robot_action(action_values, dataset.features)
+                if interpolator.needs_new_action():
+                    action_values = predict_action(
+                        observation=observation_frame,
+                        policy=policy,
+                        device=get_safe_torch_device(policy.config.device),
+                        preprocessor=preprocessor,
+                        postprocessor=postprocessor,
+                        use_amp=policy.config.use_amp,
+                        task=single_task,
+                        robot_type=robot.robot_type,
+                    )
+                    act_processed_policy = make_robot_action(action_values, dataset.features)
+                    robot_action_to_send = robot_action_processor((act_processed_policy, obs))
+
+                    # Convert to tensor for interpolator
+                    action_tensor = torch.tensor([robot_action_to_send[k] for k in action_keys])
+                    interpolator.add(action_tensor)
+
+                # Get interpolated action
+                interp_action = interpolator.get()
+                if interp_action is not None:
+                    robot_action_to_send = {k: interp_action[i].item() for i, k in enumerate(action_keys)}
+                    action_values = robot_action_to_send
+                else:
+                    # No action available yet, skip this iteration
+                    continue
+            else:
+                action_values = predict_action(
+                    observation=observation_frame,
+                    policy=policy,
+                    device=get_safe_torch_device(policy.config.device),
+                    preprocessor=preprocessor,
+                    postprocessor=postprocessor,
+                    use_amp=policy.config.use_amp,
+                    task=single_task,
+                    robot_type=robot.robot_type,
+                )
+                act_processed_policy: RobotAction = make_robot_action(action_values, dataset.features)
+                robot_action_to_send = robot_action_processor((act_processed_policy, obs))
 
         elif policy is None and isinstance(teleop, Teleoperator):
             act = teleop.get_action()
 
             # Applies a pipeline to the raw teleop action, default is IdentityProcessor
             act_processed_teleop = teleop_action_processor((act, obs))
+            action_values = act_processed_teleop
+            robot_action_to_send = robot_action_processor((act_processed_teleop, obs))
 
         elif policy is None and isinstance(teleop, list):
             arm_action = teleop_arm.get_action()
@@ -379,6 +428,8 @@ def record_loop(
             base_action = robot._from_keyboard_to_base_action(keyboard_action)
             act = {**arm_action, **base_action} if len(base_action) > 0 else arm_action
             act_processed_teleop = teleop_action_processor((act, obs))
+            action_values = act_processed_teleop
+            robot_action_to_send = robot_action_processor((act_processed_teleop, obs))
         else:
             logging.info(
                 "No policy or teleoperator provided, skipping action generation."
@@ -387,14 +438,6 @@ def record_loop(
             )
             continue
 
-        # Applies a pipeline to the action, default is IdentityProcessor
-        if policy is not None and act_processed_policy is not None:
-            action_values = act_processed_policy
-            robot_action_to_send = robot_action_processor((act_processed_policy, obs))
-        else:
-            action_values = act_processed_teleop
-            robot_action_to_send = robot_action_processor((act_processed_teleop, obs))
-
         # Send action to robot
         # Action can eventually be clipped using `max_relative_target`,
         # so action actually sent is saved in the dataset. action = postprocessor.process(action)
@@ -414,7 +457,7 @@ def record_loop(
 
         dt_s = time.perf_counter() - start_loop_t
 
-        sleep_time_s: float = 1 / fps - dt_s
+        sleep_time_s: float = control_interval - dt_s
         if sleep_time_s < 0:
             logging.warning(
                 f"Record loop is running slower ({1 / dt_s:.1f} Hz) than the target FPS ({fps} Hz). Dataset frames might be dropped and robot control might be unstable. Common causes are: 1) Camera FPS not keeping up 2) Policy inference taking too long 3) CPU starvation"
@@ -501,6 +544,7 @@ def record(cfg: RecordConfig) -> LeRobotDataset:
         policy = None if cfg.policy is None else make_policy(cfg.policy, ds_meta=dataset.meta)
         preprocessor = None
         postprocessor = None
+        interpolator = None
         if cfg.policy is not None:
             preprocessor, postprocessor = make_pre_post_processors(
                 policy_cfg=cfg.policy,
@@ -511,6 +555,10 @@ def record(cfg: RecordConfig) -> LeRobotDataset:
                     "rename_observations_processor": {"rename_map": cfg.dataset.rename_map},
                 },
             )
+            # Create interpolator for smoother policy control
+            if cfg.interpolation_multiplier > 1:
+                interpolator = ActionInterpolator(multiplier=cfg.interpolation_multiplier)
+                logging.info(f"Action interpolation enabled: {cfg.interpolation_multiplier}x control rate")
 
         robot.connect()
         if teleop is not None:
@@ -542,6 +590,7 @@ def record(cfg: RecordConfig) -> LeRobotDataset:
                     control_time_s=cfg.dataset.episode_time_s,
                     single_task=cfg.dataset.single_task,
                     display_data=cfg.display_data,
+                    interpolator=interpolator,
                     display_compressed_images=display_compressed_images,
                 )