some more iterations

src/lerobot/configs/__init__.py

@@ -21,6 +21,7 @@ are intentionally NOT re-exported here to avoid circular dependencies
 Import them directly: ``from lerobot.configs.train import TrainPipelineConfig``
 """
 
+from .dataset import DatasetRecordConfig
 from .default import DatasetConfig, EvalConfig, PeftConfig, WandBConfig
 from .policies import PreTrainedConfig
 from .types import (
@@ -39,6 +40,7 @@ __all__ = [
     "PolicyFeature",
     "RTCAttentionSchedule",
     # Config classes
+    "DatasetRecordConfig",
     "DatasetConfig",
     "EvalConfig",
     "PeftConfig",

src/lerobot/configs/dataset.py

@@ -0,0 +1,71 @@
+# Copyright 2024 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.
+
+"""Shared dataset recording configuration used by both ``lerobot-record`` and ``lerobot-rollout``."""
+
+from dataclasses import dataclass, field
+from pathlib import Path
+
+
+@dataclass
+class DatasetRecordConfig:
+    # Dataset identifier. By convention it should match '{hf_username}/{dataset_name}' (e.g. `lerobot/test`).
+    repo_id: str = ""
+    # A short but accurate description of the task performed during the recording (e.g. "Pick the Lego block and drop it in the box on the right.")
+    single_task: str = ""
+    # Root directory where the dataset will be stored (e.g. 'dataset/path'). If None, defaults to $HF_LEROBOT_HOME/repo_id.
+    root: str | Path | None = None
+    # Limit the frames per second.
+    fps: int = 30
+    # Number of seconds for data recording for each episode.
+    episode_time_s: int | float = 60
+    # Number of seconds for resetting the environment after each episode.
+    reset_time_s: int | float = 60
+    # Number of episodes to record.
+    num_episodes: int = 50
+    # Encode frames in the dataset into video
+    video: bool = True
+    # Upload dataset to Hugging Face hub.
+    push_to_hub: bool = True
+    # Upload on private repository on the Hugging Face hub.
+    private: bool = False
+    # Add tags to your dataset on the hub.
+    tags: list[str] | None = None
+    # Number of subprocesses handling the saving of frames as PNG. Set to 0 to use threads only;
+    # set to ≥1 to use subprocesses, each using threads to write images. The best number of processes
+    # and threads depends on your system. We recommend 4 threads per camera with 0 processes.
+    # If fps is unstable, adjust the thread count. If still unstable, try using 1 or more subprocesses.
+    num_image_writer_processes: int = 0
+    # Number of threads writing the frames as png images on disk, per camera.
+    # Too many threads might cause unstable teleoperation fps due to main thread being blocked.
+    # Not enough threads might cause low camera fps.
+    num_image_writer_threads_per_camera: int = 4
+    # Number of episodes to record before batch encoding videos
+    # Set to 1 for immediate encoding (default behavior), or higher for batched encoding
+    video_encoding_batch_size: int = 1
+    # Video codec for encoding videos. Options: 'h264', 'hevc', 'libsvtav1', 'auto',
+    # or hardware-specific: 'h264_videotoolbox', 'h264_nvenc', 'h264_vaapi', 'h264_qsv'.
+    # Use 'auto' to auto-detect the best available hardware encoder.
+    vcodec: str = "libsvtav1"
+    # Enable streaming video encoding: encode frames in real-time during capture instead
+    # of writing PNG images first. Makes save_episode() near-instant. More info in the documentation: https://huggingface.co/docs/lerobot/streaming_video_encoding
+    streaming_encoding: bool = False
+    # Maximum number of frames to buffer per camera when using streaming encoding.
+    # ~1s buffer at 30fps. Provides backpressure if the encoder can't keep up.
+    encoder_queue_maxsize: int = 30
+    # Number of threads per encoder instance. None = auto (codec default).
+    # Lower values reduce CPU usage, maps to 'lp' (via svtav1-params) for libsvtav1 and 'threads' for h264/hevc..
+    encoder_threads: int | None = None
+    # Rename map for the observation to override the image and state keys
+    rename_map: dict[str, str] = field(default_factory=dict)

src/lerobot/policies/__init__.py

@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from lerobot.utils.action_interpolator import ActionInterpolator as ActionInterpolator
+
 from .act.configuration_act import ACTConfig as ACTConfig
 from .diffusion.configuration_diffusion import DiffusionConfig as DiffusionConfig
 from .factory import get_policy_class, make_policy, make_policy_config, make_pre_post_processors
@@ -21,7 +23,6 @@ from .pi0.configuration_pi0 import PI0Config as PI0Config
 from .pi0_fast.configuration_pi0_fast import PI0FastConfig as PI0FastConfig
 from .pi05.configuration_pi05 import PI05Config as PI05Config
 from .pretrained import PreTrainedPolicy as PreTrainedPolicy
-from .rtc import ActionInterpolator as ActionInterpolator
 from .sac.configuration_sac import SACConfig as SACConfig
 from .sac.reward_model.configuration_classifier import RewardClassifierConfig as RewardClassifierConfig
 from .sarm.configuration_sarm import SARMConfig as SARMConfig

src/lerobot/policies/rtc/action_interpolator.py

@@ -1,116 +1,4 @@
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+# Moved to lerobot.utils.action_interpolator — re-exported for backwards compatibility.
-#
+from lerobot.utils.action_interpolator import ActionInterpolator
-# 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.
+__all__ = ["ActionInterpolator"]
-
-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:
-            # First step: no previous action yet, so run at base FPS without interpolation.
-            self._buffer = [action.clone()]
-        self._prev = action.clone()
-        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/rollout/__init__.py

@@ -17,21 +17,21 @@
 from .configs import (
     BaseStrategyConfig,
     DAggerStrategyConfig,
+    DatasetRecordConfig,
     HighlightStrategyConfig,
     RolloutConfig,
-    RolloutDatasetConfig,
     RolloutStrategyConfig,
     SentryStrategyConfig,
 )
 from .context import RolloutContext, build_rollout_context
 from .inference import (
-    InferenceStrategy,
+    InferenceEngine,
-    InferenceStrategyConfig,
+    InferenceEngineConfig,
     RTCInferenceConfig,
-    RTCInferenceStrategy,
+    RTCInferenceEngine,
     SyncInferenceConfig,
-    SyncInferenceStrategy,
+    SyncInferenceEngine,
-    create_inference_strategy,
+    create_inference_engine,
 )
 from .ring_buffer import RolloutRingBuffer
 from .robot_wrapper import ThreadSafeRobot
@@ -41,21 +41,21 @@ __all__ = [
     "BaseStrategyConfig",
     "DAggerStrategyConfig",
     "HighlightStrategyConfig",
-    "InferenceStrategy",
+    "InferenceEngine",
-    "InferenceStrategyConfig",
+    "InferenceEngineConfig",
     "RTCInferenceConfig",
-    "RTCInferenceStrategy",
+    "RTCInferenceEngine",
     "RolloutConfig",
     "RolloutContext",
-    "RolloutDatasetConfig",
+    "DatasetRecordConfig",
     "RolloutRingBuffer",
     "RolloutStrategy",
     "RolloutStrategyConfig",
     "SentryStrategyConfig",
     "SyncInferenceConfig",
-    "SyncInferenceStrategy",
+    "SyncInferenceEngine",
     "ThreadSafeRobot",
     "build_rollout_context",
-    "create_inference_strategy",
+    "create_inference_engine",
     "create_strategy",
 ]

src/lerobot/rollout/configs.py

@@ -19,15 +19,15 @@ from __future__ import annotations
 import abc
 import logging
 from dataclasses import dataclass, field
-from pathlib import Path
 
 import draccus
 
 from lerobot.configs import PreTrainedConfig, parser
+from lerobot.configs.dataset import DatasetRecordConfig
 from lerobot.robots.config import RobotConfig
 from lerobot.teleoperators.config import TeleoperatorConfig
 
-from .inference import InferenceStrategyConfig, SyncInferenceConfig
+from .inference import InferenceEngineConfig, SyncInferenceConfig
 
 logger = logging.getLogger(__name__)
 
@@ -84,6 +84,7 @@ class HighlightStrategyConfig(RolloutStrategyConfig):
     ring_buffer_seconds: float = 30.0
     ring_buffer_max_memory_mb: float = 2048.0
     save_key: str = "s"
+    push_key: str = "h"
 
 
 @RolloutStrategyConfig.register_subclass("dagger")
@@ -109,33 +110,6 @@ class DAggerStrategyConfig(RolloutStrategyConfig):
     record_autonomous: bool = True
 
 
-# ---------------------------------------------------------------------------
-# Dataset recording config (shared across recording strategies)
-# ---------------------------------------------------------------------------
-
-
-@dataclass
-class RolloutDatasetConfig:
-    """Dataset configuration for rollout strategies that record data."""
-
-    repo_id: str = ""
-    single_task: str = ""
-    root: str | Path | None = None
-    fps: int = 30
-    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
-    vcodec: str = "auto"
-    streaming_encoding: bool = True
-    encoder_queue_maxsize: int = 30
-    encoder_threads: int | None = None
-    rename_map: dict[str, str] = field(default_factory=dict)
-
-
 # ---------------------------------------------------------------------------
 # Top-level rollout config
 # ---------------------------------------------------------------------------
@@ -161,10 +135,10 @@ class RolloutConfig:
     strategy: RolloutStrategyConfig = field(default_factory=BaseStrategyConfig)
 
     # Inference backend (polymorphic: --inference.type=sync|rtc)
-    inference: InferenceStrategyConfig = field(default_factory=SyncInferenceConfig)
+    inference: InferenceEngineConfig = field(default_factory=SyncInferenceConfig)
 
     # Dataset (required for sentry, highlight, dagger; None for base)
-    dataset: RolloutDatasetConfig | None = None
+    dataset: DatasetRecordConfig | None = None
 
     # Runtime
     fps: float = 30.0

src/lerobot/rollout/context.py

@@ -49,9 +49,9 @@ from lerobot.utils.feature_utils import combine_feature_dicts, hw_to_dataset_fea
 
 from .configs import BaseStrategyConfig, DAggerStrategyConfig, RolloutConfig
 from .inference import (
-    InferenceStrategy,
+    InferenceEngine,
     RTCInferenceConfig,
-    create_inference_strategy,
+    create_inference_engine,
 )
 from .robot_wrapper import ThreadSafeRobot
 
@@ -106,12 +106,12 @@ class HardwareContext:
 
 @dataclass
 class PolicyContext:
-    """Loaded policy and its inference strategy."""
+    """Loaded policy and its inference engine."""
 
     policy: PreTrainedPolicy
     preprocessor: PolicyProcessorPipeline
     postprocessor: PolicyProcessorPipeline
-    inference: InferenceStrategy
+    inference: InferenceEngine
 
 
 @dataclass
@@ -159,7 +159,7 @@ def build_rollout_context(
     robot_action_processor: RobotProcessorPipeline | None = None,
     robot_observation_processor: RobotProcessorPipeline | None = None,
 ) -> RolloutContext:
-    """Wire up policy, processors, hardware, dataset, and inference strategy.
+    """Wire up policy, processors, hardware, dataset, and inference engine.
 
     The order is policy-first / hardware-last so a bad ``--policy.path``
     fails fast without touching the robot.
@@ -329,7 +329,7 @@ def build_rollout_context(
 
     # --- 7. Inference strategy (needs policy + pre/post + hardware) --
     task_str = cfg.dataset.single_task if cfg.dataset else cfg.task
-    inference_strategy = create_inference_strategy(
+    inference_strategy = create_inference_engine(
         cfg.inference,
         policy=policy,
         preprocessor=preprocessor,

src/lerobot/rollout/inference/__init__.py

@@ -12,28 +12,28 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""Inference strategy package — backend-agnostic action production.
+"""Inference engine package — backend-agnostic action production.
 
 Concrete strategies (sync, RTC, …) expose the same small interface so
 rollout strategies never branch on the inference backend.
 """
 
-from .base import InferenceStrategy
+from .base import InferenceEngine
 from .factory import (
-    InferenceStrategyConfig,
+    InferenceEngineConfig,
     RTCInferenceConfig,
     SyncInferenceConfig,
-    create_inference_strategy,
+    create_inference_engine,
 )
-from .rtc import RTCInferenceStrategy
+from .rtc import RTCInferenceEngine
-from .sync import SyncInferenceStrategy
+from .sync import SyncInferenceEngine
 
 __all__ = [
-    "InferenceStrategy",
+    "InferenceEngine",
-    "InferenceStrategyConfig",
+    "InferenceEngineConfig",
     "RTCInferenceConfig",
-    "RTCInferenceStrategy",
+    "RTCInferenceEngine",
     "SyncInferenceConfig",
-    "SyncInferenceStrategy",
+    "SyncInferenceEngine",
-    "create_inference_strategy",
+    "create_inference_engine",
 ]

src/lerobot/rollout/inference/base.py

@@ -12,11 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""Inference strategy ABC.
+"""Inference engine ABC.
 
 Rollout strategies consume actions through this small interface so they
-do not need to know whether inference is synchronous, runs in a
+do not need to know whether the inference engine is synchronous, runs in
-background thread (RTC), or comes from an external source.
+a background thread (RTC), or comes from an external source.
 """
 
 from __future__ import annotations
@@ -26,7 +26,7 @@ import abc
 import torch
 
 
-class InferenceStrategy(abc.ABC):
+class InferenceEngine(abc.ABC):
     """Abstract backend for producing actions during rollout.
 
     Subclasses decide whether inference happens inline, in a background

src/lerobot/rollout/inference/factory.py

@@ -12,11 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""Inference strategy configs and factory.
+"""Inference engine configs and factory.
 
 Selection is explicit via ``--inference.type=sync|rtc``.  Adding a new
 backend requires registering its config subclass and dispatching it in
-:func:`create_inference_strategy`.
+:func:`create_inference_engine`.
 """
 
 from __future__ import annotations
@@ -33,9 +33,9 @@ from lerobot.policies.rtc.configuration_rtc import RTCConfig
 from lerobot.processor import PolicyProcessorPipeline
 
 from ..robot_wrapper import ThreadSafeRobot
-from .base import InferenceStrategy
+from .base import InferenceEngine
-from .rtc import RTCInferenceStrategy
+from .rtc import RTCInferenceEngine
-from .sync import SyncInferenceStrategy
+from .sync import SyncInferenceEngine
 
 logger = logging.getLogger(__name__)
 
@@ -46,7 +46,7 @@ logger = logging.getLogger(__name__)
 
 
 @dataclass
-class InferenceStrategyConfig(draccus.ChoiceRegistry, abc.ABC):
+class InferenceEngineConfig(draccus.ChoiceRegistry, abc.ABC):
     """Abstract base for inference backend configuration.
 
     Use ``--inference.type=<name>`` on the CLI to select a backend.
@@ -57,15 +57,15 @@ class InferenceStrategyConfig(draccus.ChoiceRegistry, abc.ABC):
         return self.get_choice_name(self.__class__)
 
 
-@InferenceStrategyConfig.register_subclass("sync")
+@InferenceEngineConfig.register_subclass("sync")
 @dataclass
-class SyncInferenceConfig(InferenceStrategyConfig):
+class SyncInferenceConfig(InferenceEngineConfig):
     """Inline synchronous inference (one policy call per control tick)."""
 
 
-@InferenceStrategyConfig.register_subclass("rtc")
+@InferenceEngineConfig.register_subclass("rtc")
 @dataclass
-class RTCInferenceConfig(InferenceStrategyConfig):
+class RTCInferenceConfig(InferenceEngineConfig):
     """Real-Time Chunking: async policy inference in a background thread."""
 
     # ``RTCConfig`` is a small dataclass with default-only fields, so eagerly
@@ -80,8 +80,8 @@ class RTCInferenceConfig(InferenceStrategyConfig):
 # ---------------------------------------------------------------------------
 
 
-def create_inference_strategy(
+def create_inference_engine(
-    config: InferenceStrategyConfig,
+    config: InferenceEngineConfig,
     *,
     policy: PreTrainedPolicy,
     preprocessor: PolicyProcessorPipeline,
@@ -96,10 +96,10 @@ def create_inference_strategy(
     use_torch_compile: bool = False,
     compile_warmup_inferences: int = 2,
     shutdown_event: Event | None = None,
-) -> InferenceStrategy:
+) -> InferenceEngine:
-    """Instantiate the appropriate inference strategy from a config object."""
+    """Instantiate the appropriate inference engine from a config object."""
     if isinstance(config, SyncInferenceConfig):
-        return SyncInferenceStrategy(
+        return SyncInferenceEngine(
             policy=policy,
             preprocessor=preprocessor,
             postprocessor=postprocessor,
@@ -110,7 +110,7 @@ def create_inference_strategy(
             robot_type=robot_wrapper.robot_type,
         )
     if isinstance(config, RTCInferenceConfig):
-        return RTCInferenceStrategy(
+        return RTCInferenceEngine(
             policy=policy,
             preprocessor=preprocessor,
             postprocessor=postprocessor,
@@ -125,4 +125,4 @@ def create_inference_strategy(
             rtc_queue_threshold=config.queue_threshold,
             shutdown_event=shutdown_event,
         )
-    raise ValueError(f"Unknown inference strategy type: {type(config).__name__}")
+    raise ValueError(f"Unknown inference engine type: {type(config).__name__}")

src/lerobot/rollout/inference/rtc.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""Real-Time Chunking inference strategy.
+"""Real-Time Chunking inference engine.
 
 A background thread produces action chunks asynchronously via
 :meth:`policy.predict_action_chunk`.  The main control loop polls
@@ -47,7 +47,7 @@ from lerobot.utils.constants import OBS_STATE
 from lerobot.utils.feature_utils import build_dataset_frame
 
 from ..robot_wrapper import ThreadSafeRobot
-from .base import InferenceStrategy
+from .base import InferenceEngine
 
 logger = logging.getLogger(__name__)
 
@@ -104,11 +104,11 @@ def _normalize_prev_actions_length(prev_actions: torch.Tensor, target_steps: int
 
 
 # ---------------------------------------------------------------------------
-# RTCInferenceStrategy
+# RTCInferenceEngine
 # ---------------------------------------------------------------------------
 
 
-class RTCInferenceStrategy(InferenceStrategy):
+class RTCInferenceEngine(InferenceEngine):
     """Async RTC inference: a background thread produces action chunks.
 
     ``get_action`` pops the next action from the shared queue (or

src/lerobot/rollout/inference/sync.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""Synchronous inference strategy: inline policy call per control tick."""
+"""Synchronous inference engine: inline policy call per control tick."""
 
 from __future__ import annotations
 
@@ -26,12 +26,12 @@ from lerobot.policies.pretrained import PreTrainedPolicy
 from lerobot.policies.utils import make_robot_action, prepare_observation_for_inference
 from lerobot.processor import PolicyProcessorPipeline
 
-from .base import InferenceStrategy
+from .base import InferenceEngine
 
 logger = logging.getLogger(__name__)
 
 
-class SyncInferenceStrategy(InferenceStrategy):
+class SyncInferenceEngine(InferenceEngine):
     """Inline synchronous inference: compute one action per call.
 
     ``get_action`` runs the full policy pipeline (pre/post-processor +

src/lerobot/rollout/strategies/base.py

@@ -45,7 +45,6 @@ class BaseStrategy(RolloutStrategy):
         interpolator = self._interpolator
 
         control_interval = interpolator.get_control_interval(cfg.fps)
-        ordered_keys = ctx.data.ordered_action_keys
 
         start_time = time.perf_counter()
         engine.resume()
@@ -63,14 +62,12 @@ class BaseStrategy(RolloutStrategy):
             if self._handle_warmup(cfg.use_torch_compile, loop_start, control_interval):
                 continue
 
-            send_next_action(
+            send_next_action(obs_processed, obs, ctx, interpolator)
-                engine, obs_processed, obs, ctx, interpolator, ordered_keys, ctx.data.dataset_features
-            )
 
             dt = time.perf_counter() - loop_start
             if (sleep_t := control_interval - dt) > 0:
                 precise_sleep(sleep_t)
 
     def teardown(self, ctx: RolloutContext) -> None:
-        self._teardown_hardware(ctx)
+        self._teardown_hardware(ctx.hardware)
         logger.info("Base strategy teardown complete")

src/lerobot/rollout/strategies/core.py

@@ -20,16 +20,16 @@ import abc
 import time
 from typing import TYPE_CHECKING
 
-from lerobot.policies.rtc import ActionInterpolator
+from lerobot.utils.action_interpolator import ActionInterpolator
 from lerobot.utils.constants import OBS_STR
 from lerobot.utils.feature_utils import build_dataset_frame
 from lerobot.utils.robot_utils import precise_sleep
 
-from ..inference import InferenceStrategy
+from ..inference import InferenceEngine
 
 if TYPE_CHECKING:
     from ..configs import RolloutStrategyConfig
-    from ..context import RolloutContext
+    from ..context import HardwareContext, RolloutContext
 
 
 class RolloutStrategy(abc.ABC):
@@ -42,12 +42,12 @@ class RolloutStrategy(abc.ABC):
 
     def __init__(self, config: RolloutStrategyConfig) -> None:
         self.config = config
-        self._engine: InferenceStrategy | None = None
+        self._engine: InferenceEngine | None = None
         self._interpolator: ActionInterpolator | None = None
         self._warmup_flushed: bool = False
 
     def _init_engine(self, ctx: RolloutContext) -> None:
-        """Attach the inference strategy + interpolator and start the backend.
+        """Attach the inference engine + interpolator and start the backend.
 
         Call this from ``setup()`` so strategies share identical setup
         without duplicating code.
@@ -80,14 +80,14 @@ class RolloutStrategy(abc.ABC):
             engine.resume()
         return False
 
-    def _teardown_hardware(self, ctx: RolloutContext) -> None:
+    def _teardown_hardware(self, hw: HardwareContext) -> None:
         """Stop the inference engine and disconnect hardware."""
         if self._engine is not None:
             self._engine.stop()
-        robot = ctx.hardware.robot_wrapper.inner
+        robot = hw.robot_wrapper.inner
         if robot.is_connected:
             robot.disconnect()
-        teleop = ctx.hardware.teleop
+        teleop = hw.teleop
         if teleop is not None and teleop.is_connected:
             teleop.disconnect()
 
@@ -110,24 +110,25 @@ class RolloutStrategy(abc.ABC):
 
 
 def send_next_action(
-    engine: InferenceStrategy,
     obs_processed: dict,
     obs_raw: dict,
     ctx: RolloutContext,
     interpolator: ActionInterpolator,
-    ordered_keys: list[str],
-    features: dict,
 ) -> dict | None:
     """Dispatch the next action to the robot.
 
-    Pulls the next action tensor from the inference strategy, feeds the
+    Pulls the next action tensor from the inference engine, feeds the
     interpolator, and sends the interpolated action through the
     ``robot_action_processor`` to the robot.  Works identically for
-    sync and async backends — the strategy never needs to branch.
+    sync and async backends — the rollout strategy never needs to branch.
 
     Returns the action dict that was sent, or ``None`` if no action was
     ready (e.g. empty async queue, interpolator not yet primed).
     """
+    engine = ctx.policy.inference
+    features = ctx.data.dataset_features
+    ordered_keys = ctx.data.ordered_action_keys
+
     if interpolator.needs_new_action():
         obs_frame = build_dataset_frame(features, obs_processed, prefix=OBS_STR)
         action_tensor = engine.get_action(obs_frame)

src/lerobot/rollout/strategies/dagger.py

@@ -407,7 +407,7 @@ class DAggerStrategy(RolloutStrategy):
                     private=ctx.runtime.cfg.dataset.private,
                 )
 
-        self._teardown_hardware(ctx)
+        self._teardown_hardware(ctx.hardware)
         logger.info("DAgger strategy teardown complete")
 
     # ------------------------------------------------------------------
@@ -429,8 +429,7 @@ class DAggerStrategy(RolloutStrategy):
         record_stride = max(1, cfg.interpolation_multiplier)
         record_autonomous = self.config.record_autonomous
 
-        ordered_keys = ctx.data.ordered_action_keys
+        features = ctx.data.dataset_features
-        features = dataset.features
 
         engine.reset()
         interpolator.reset()
@@ -499,9 +498,7 @@ class DAggerStrategy(RolloutStrategy):
                     timestamp = time.perf_counter() - start_t
                     continue
 
-                action_dict = send_next_action(
+                action_dict = send_next_action(obs_processed, obs, ctx, interpolator)
-                    engine, obs_processed, obs, ctx, interpolator, ordered_keys, features
-                )
 
                 if action_dict is not None:
                     last_action = ctx.processors.robot_action_processor((action_dict, obs))

src/lerobot/rollout/strategies/highlight.py

@@ -21,6 +21,7 @@ import logging
 import os
 import sys
 import time
+from concurrent.futures import Future, ThreadPoolExecutor
 from threading import Event as ThreadingEvent
 
 from lerobot.common.control_utils import is_headless
@@ -75,7 +76,9 @@ class HighlightStrategy(RolloutStrategy):
         self._listener = None
         self._save_requested = ThreadingEvent()
         self._recording_live = ThreadingEvent()
-        self._shutdown_event: ThreadingEvent | None = None
+        self._push_requested = ThreadingEvent()
+        self._push_executor: ThreadPoolExecutor | None = None
+        self._pending_push: Future | None = None
 
     def setup(self, ctx: RolloutContext) -> None:
         self._init_engine(ctx)
@@ -86,12 +89,13 @@ class HighlightStrategy(RolloutStrategy):
             fps=ctx.runtime.cfg.fps,
         )
 
-        self._shutdown_event = ctx.runtime.shutdown_event
+        self._push_executor = ThreadPoolExecutor(max_workers=1, thread_name_prefix="highlight-push")
-        self._setup_keyboard()
+        self._setup_keyboard(ctx.runtime.shutdown_event)
         logger.info(
-            "Highlight strategy ready (buffer=%.0fs, key='%s')",
+            "Highlight strategy ready (buffer=%.0fs, save='%s', push='%s')",
             self.config.ring_buffer_seconds,
             self.config.save_key,
+            self.config.push_key,
         )
 
     def run(self, ctx: RolloutContext) -> None:
@@ -101,10 +105,9 @@ class HighlightStrategy(RolloutStrategy):
         dataset = ctx.data.dataset
         ring = self._ring
         interpolator = self._interpolator
+        features = ctx.data.dataset_features
 
         control_interval = interpolator.get_control_interval(cfg.fps)
-        ordered_keys = ctx.data.ordered_action_keys
-        features = dataset.features
 
         engine.resume()
 
@@ -126,9 +129,7 @@ class HighlightStrategy(RolloutStrategy):
                     if self._handle_warmup(cfg.use_torch_compile, loop_start, control_interval):
                         continue
 
-                    action_dict = send_next_action(
+                    action_dict = send_next_action(obs_processed, obs, ctx, interpolator)
-                        engine, obs_processed, obs, ctx, interpolator, ordered_keys, features
-                    )
 
                     if action_dict is not None:
                         obs_frame = build_dataset_frame(features, obs_processed, prefix=OBS_STR)
@@ -158,9 +159,10 @@ class HighlightStrategy(RolloutStrategy):
                                 dataset.save_episode()
                                 logger.info("Episode saved")
                                 self._recording_live.clear()
-                                engine.reset()
+
-                                interpolator.reset()
+                        if self._push_requested.is_set():
-                                engine.resume()
+                            self._push_requested.clear()
+                            self._background_push(dataset, cfg)
 
                         if self._recording_live.is_set():
                             dataset.add_frame(frame)
@@ -180,6 +182,10 @@ class HighlightStrategy(RolloutStrategy):
         if self._listener is not None:
             self._listener.stop()
 
+        if self._push_executor is not None:
+            self._push_executor.shutdown(wait=True)
+            self._push_executor = None
+
         if ctx.data.dataset is not None:
             ctx.data.dataset.finalize()
             if ctx.runtime.cfg.dataset and ctx.runtime.cfg.dataset.push_to_hub:
@@ -188,28 +194,50 @@ class HighlightStrategy(RolloutStrategy):
                     private=ctx.runtime.cfg.dataset.private,
                 )
 
-        self._teardown_hardware(ctx)
+        self._teardown_hardware(ctx.hardware)
         logger.info("Highlight strategy teardown complete")
 
-    def _setup_keyboard(self) -> None:
+    def _setup_keyboard(self, shutdown_event: ThreadingEvent) -> None:
-        """Set up keyboard listener for the save key."""
+        """Set up keyboard listener for save and push keys."""
         if is_headless():
-            logger.warning("Headless environment — highlight save key unavailable")
+            logger.warning("Headless environment — highlight keys unavailable")
             return
 
         try:
             save_key = self.config.save_key
+            push_key = self.config.push_key
 
             def on_press(key):
                 with contextlib.suppress(Exception):
                     if hasattr(key, "char") and key.char == save_key:
                         self._save_requested.set()
+                    elif hasattr(key, "char") and key.char == push_key:
+                        self._push_requested.set()
                     elif key == keyboard.Key.esc:
                         self._save_requested.clear()
-                        if self._shutdown_event is not None:
+                        shutdown_event.set()
-                            self._shutdown_event.set()
 
             self._listener = keyboard.Listener(on_press=on_press)
             self._listener.start()
         except ImportError:
             logger.warning("pynput not available — keyboard listener disabled")
+
+    def _background_push(self, dataset, cfg) -> None:
+        """Queue a Hub push on the single-worker executor."""
+        if self._push_executor is None:
+            return
+
+        if self._pending_push is not None and not self._pending_push.done():
+            logger.info("Previous push still in progress; queueing next")
+
+        def _push():
+            try:
+                dataset.push_to_hub(
+                    tags=cfg.dataset.tags if cfg.dataset else None,
+                    private=cfg.dataset.private if cfg.dataset else False,
+                )
+                logger.info("Background push to hub complete")
+            except Exception as e:
+                logger.error("Background push failed: %s", e)
+
+        self._pending_push = self._push_executor.submit(_push)

src/lerobot/rollout/strategies/sentry.py

@@ -73,10 +73,9 @@ class SentryStrategy(RolloutStrategy):
         robot = ctx.hardware.robot_wrapper
         dataset = ctx.data.dataset
         interpolator = self._interpolator
+        features = ctx.data.dataset_features
 
         control_interval = interpolator.get_control_interval(cfg.fps)
-        ordered_keys = ctx.data.ordered_action_keys
-        features = dataset.features
 
         engine.resume()
 
@@ -100,9 +99,7 @@ class SentryStrategy(RolloutStrategy):
                     if self._handle_warmup(cfg.use_torch_compile, loop_start, control_interval):
                         continue
 
-                    action_dict = send_next_action(
+                    action_dict = send_next_action(obs_processed, obs, ctx, interpolator)
-                        engine, obs_processed, obs, ctx, interpolator, ordered_keys, features
-                    )
 
                     if action_dict is not None:
                         obs_frame = build_dataset_frame(features, obs_processed, prefix=OBS_STR)
@@ -156,7 +153,7 @@ class SentryStrategy(RolloutStrategy):
                     private=ctx.runtime.cfg.dataset.private,
                 )
 
-        self._teardown_hardware(ctx)
+        self._teardown_hardware(ctx.hardware)
         logger.info("Sentry strategy teardown complete")
 
     def _background_push(self, dataset, cfg) -> None:

src/lerobot/scripts/lerobot_record.py

@@ -67,8 +67,7 @@ lerobot-record \\
 
 import logging
 import time
-from dataclasses import asdict, dataclass, field
+from dataclasses import asdict, dataclass
-from pathlib import Path
 from pprint import pformat
 
 from lerobot.cameras import CameraConfig  # noqa: F401
@@ -82,6 +81,7 @@ from lerobot.common.control_utils import (
     sanity_check_dataset_robot_compatibility,
 )
 from lerobot.configs import parser
+from lerobot.configs.dataset import DatasetRecordConfig
 from lerobot.datasets import (
     LeRobotDataset,
     VideoEncodingManager,
@@ -137,63 +137,6 @@ from lerobot.utils.utils import (
 from lerobot.utils.visualization_utils import init_rerun, log_rerun_data
 
 
-@dataclass
-class DatasetRecordConfig:
-    # Dataset identifier. By convention it should match '{hf_username}/{dataset_name}' (e.g. `lerobot/test`).
-    repo_id: str
-    # A short but accurate description of the task performed during the recording (e.g. "Pick the Lego block and drop it in the box on the right.")
-    single_task: str
-    # Root directory where the dataset will be stored (e.g. 'dataset/path'). If None, defaults to $HF_LEROBOT_HOME/repo_id.
-    root: str | Path | None = None
-    # Limit the frames per second.
-    fps: int = 30
-    # Number of seconds for data recording for each episode.
-    episode_time_s: int | float = 60
-    # Number of seconds for resetting the environment after each episode.
-    reset_time_s: int | float = 60
-    # Number of episodes to record.
-    num_episodes: int = 50
-    # Encode frames in the dataset into video
-    video: bool = True
-    # Upload dataset to Hugging Face hub.
-    push_to_hub: bool = True
-    # Upload on private repository on the Hugging Face hub.
-    private: bool = False
-    # Add tags to your dataset on the hub.
-    tags: list[str] | None = None
-    # Number of subprocesses handling the saving of frames as PNG. Set to 0 to use threads only;
-    # set to ≥1 to use subprocesses, each using threads to write images. The best number of processes
-    # and threads depends on your system. We recommend 4 threads per camera with 0 processes.
-    # If fps is unstable, adjust the thread count. If still unstable, try using 1 or more subprocesses.
-    num_image_writer_processes: int = 0
-    # Number of threads writing the frames as png images on disk, per camera.
-    # Too many threads might cause unstable teleoperation fps due to main thread being blocked.
-    # Not enough threads might cause low camera fps.
-    num_image_writer_threads_per_camera: int = 4
-    # Number of episodes to record before batch encoding videos
-    # Set to 1 for immediate encoding (default behavior), or higher for batched encoding
-    video_encoding_batch_size: int = 1
-    # Video codec for encoding videos. Options: 'h264', 'hevc', 'libsvtav1', 'auto',
-    # or hardware-specific: 'h264_videotoolbox', 'h264_nvenc', 'h264_vaapi', 'h264_qsv'.
-    # Use 'auto' to auto-detect the best available hardware encoder.
-    vcodec: str = "libsvtav1"
-    # Enable streaming video encoding: encode frames in real-time during capture instead
-    # of writing PNG images first. Makes save_episode() near-instant. More info in the documentation: https://huggingface.co/docs/lerobot/streaming_video_encoding
-    streaming_encoding: bool = False
-    # Maximum number of frames to buffer per camera when using streaming encoding.
-    # ~1s buffer at 30fps. Provides backpressure if the encoder can't keep up.
-    encoder_queue_maxsize: int = 30
-    # Number of threads per encoder instance. None = auto (codec default).
-    # Lower values reduce CPU usage, maps to 'lp' (via svtav1-params) for libsvtav1 and 'threads' for h264/hevc..
-    encoder_threads: int | None = None
-    # Rename map for the observation to override the image and state keys
-    rename_map: dict[str, str] = field(default_factory=dict)
-
-    def __post_init__(self):
-        if self.single_task is None:
-            raise ValueError("You need to provide a task as argument in `single_task`.")
-
-
 @dataclass
 class RecordConfig:
     robot: RobotConfig

src/lerobot/utils/action_interpolator.py

@@ -0,0 +1,116 @@
+# 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:
+            # First step: no previous action yet, so run at base FPS without interpolation.
+            self._buffer = [action.clone()]
+        self._prev = action.clone()
+        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)

tests/policies/rtc/test_action_interpolator.py

@@ -17,9 +17,9 @@
 import pytest
 import torch
 
-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.utils.action_interpolator import ActionInterpolator
 
 # ====================== Fixtures ======================
 

tests/test_control_robot.py

@@ -21,8 +21,9 @@ import pytest
 pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
 pytest.importorskip("deepdiff", reason="deepdiff is required (install lerobot[hardware])")
 
+from lerobot.configs.dataset import DatasetRecordConfig
 from lerobot.scripts.lerobot_calibrate import CalibrateConfig, calibrate
-from lerobot.scripts.lerobot_record import DatasetRecordConfig, RecordConfig, record
+from lerobot.scripts.lerobot_record import RecordConfig, record
 from lerobot.scripts.lerobot_replay import DatasetReplayConfig, ReplayConfig, replay
 from lerobot.scripts.lerobot_teleoperate import TeleoperateConfig, teleoperate
 from tests.fixtures.constants import DUMMY_REPO_ID