pi0 hack

* reshape noise after taking it as output of the network

src/lerobot/policies/diffusion/configuration_diffusion.py

@@ -45,7 +45,7 @@ class DiffusionConfig(PreTrainedConfig):
     Args:
         n_obs_steps: Number of environment steps worth of observations to pass to the policy (takes the
             current step and additional steps going back).
-        horizon: Diffusion model action prediction size as detailed in `DiffusionPolicy.select_action`.
+        chunk_size: Diffusion model action prediction size as detailed in `DiffusionPolicy.select_action`.
         n_action_steps: The number of action steps to run in the environment for one invocation of the policy.
             See `DiffusionPolicy.select_action` for more details.
         input_shapes: A dictionary defining the shapes of the input data for the policy. The key represents
@@ -105,7 +105,7 @@ class DiffusionConfig(PreTrainedConfig):
 
     # Inputs / output structure.
     n_obs_steps: int = 2
-    horizon: int = 16
+    chunk_size: int = 16
     n_action_steps: int = 8
 
     normalization_mapping: dict[str, NormalizationMode] = field(
@@ -118,7 +118,7 @@ class DiffusionConfig(PreTrainedConfig):
 
     # The original implementation doesn't sample frames for the last 7 steps,
     # which avoids excessive padding and leads to improved training results.
-    drop_n_last_frames: int = 7  # horizon - n_action_steps - n_obs_steps + 1
+    drop_n_last_frames: int = 7  # chunk_size - n_action_steps - n_obs_steps + 1
 
     # Architecture / modeling.
     # Vision backbone.
@@ -180,13 +180,13 @@ class DiffusionConfig(PreTrainedConfig):
                 f"Got {self.noise_scheduler_type}."
             )
 
-        # Check that the horizon size and U-Net downsampling is compatible.
+        # Check that the chunk size and U-Net downsampling is compatible.
         # U-Net downsamples by 2 with each stage.
         downsampling_factor = 2 ** len(self.down_dims)
-        if self.horizon % downsampling_factor != 0:
+        if self.chunk_size % downsampling_factor != 0:
             raise ValueError(
-                "The horizon should be an integer multiple of the downsampling factor (which is determined "
+                "The chunk_size should be an integer multiple of the downsampling factor (which is determined "
-                f"by `len(down_dims)`). Got {self.horizon=} and {self.down_dims=}"
+                f"by `len(down_dims)`). Got {self.chunk_size=} and {self.down_dims=}"
             )
 
     def get_optimizer_preset(self) -> AdamConfig:
@@ -231,7 +231,7 @@ class DiffusionConfig(PreTrainedConfig):
 
     @property
     def action_delta_indices(self) -> list:
-        return list(range(1 - self.n_obs_steps, 1 - self.n_obs_steps + self.horizon))
+        return list(range(1 - self.n_obs_steps, 1 - self.n_obs_steps + self.chunk_size))
 
     @property
     def reward_delta_indices(self) -> None:

src/lerobot/policies/diffusion/modeling_diffusion.py

@@ -99,25 +99,25 @@ class DiffusionPolicy(PreTrainedPolicy):
         return actions
 
     @torch.no_grad()
-    def select_action(self, batch: dict[str, Tensor], noise: Tensor | None = None) -> Tensor:
+    def select_action(self, batch: dict[str, Tensor], noise: Tensor | None = None, **kwargs) -> Tensor:
         """Select a single action given environment observations.
 
         This method handles caching a history of observations and an action trajectory generated by the
         underlying diffusion model. Here's how it works:
           - `n_obs_steps` steps worth of observations are cached (for the first steps, the observation is
             copied `n_obs_steps` times to fill the cache).
-          - The diffusion model generates `horizon` steps worth of actions.
+          - The diffusion model generates `chunk_size` steps worth of actions.
           - `n_action_steps` worth of actions are actually kept for execution, starting from the current step.
         Schematically this looks like:
             ----------------------------------------------------------------------------------------------
-            (legend: o = n_obs_steps, h = horizon, a = n_action_steps)
+            (legend: o = n_obs_steps, c = chunk_size, a = n_action_steps)
             |timestep            | n-o+1 | n-o+2 | ..... | n     | ..... | n+a-1 | n+a   | ..... | n-o+h |
             |observation is used | YES   | YES   | YES   | YES   | NO    | NO    | NO    | NO    | NO    |
             |action is generated | YES   | YES   | YES   | YES   | YES   | YES   | YES   | YES   | YES   |
             |action is used      | NO    | NO    | NO    | YES   | YES   | YES   | NO    | NO    | NO    |
             ----------------------------------------------------------------------------------------------
-        Note that this means we require: `n_action_steps <= horizon - n_obs_steps + 1`. Also, note that
+        Note that this means we require: `n_action_steps <= chunk_size - n_obs_steps + 1`. Also, note that
-        "horizon" may not the best name to describe what the variable actually means, because this period is
+        this period is
         actually measured from the first observation which (if `n_obs_steps` > 1) happened in the past.
         """
         # NOTE: for offline evaluation, we have action in the batch, so we need to pop it out
@@ -213,7 +213,7 @@ class DiffusionModel(nn.Module):
             noise
             if noise is not None
             else torch.randn(
-                size=(batch_size, self.config.horizon, self.config.action_feature.shape[0]),
+                size=(batch_size, self.config.chunk_size, self.config.action_feature.shape[0]),
                 dtype=dtype,
                 device=device,
                 generator=generator,
@@ -309,16 +309,16 @@ class DiffusionModel(nn.Module):
                 AND/OR
             "observation.environment_state": (B, n_obs_steps, environment_dim)
 
-            "action": (B, horizon, action_dim)
+            "action": (B, chunk_size, action_dim)
-            "action_is_pad": (B, horizon)
+            "action_is_pad": (B, chunk_size)
         }
         """
         # Input validation.
         assert set(batch).issuperset({OBS_STATE, ACTION, "action_is_pad"})
         assert OBS_IMAGES in batch or OBS_ENV_STATE in batch
         n_obs_steps = batch[OBS_STATE].shape[1]
-        horizon = batch[ACTION].shape[1]
+        chunk_size = batch[ACTION].shape[1]
-        assert horizon == self.config.horizon
+        assert chunk_size == self.config.chunk_size
         assert n_obs_steps == self.config.n_obs_steps
 
         # Encode image features and concatenate them all together along with the state vector.

src/lerobot/policies/dsrl/configuration_dsrl.py

@@ -0,0 +1,244 @@
+# !/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.
+
+from dataclasses import dataclass, field
+
+from lerobot.configs.policies import PreTrainedConfig
+from lerobot.configs.types import NormalizationMode
+from lerobot.optim.optimizers import MultiAdamConfig
+from lerobot.utils.constants import ACTION, OBS_IMAGE, OBS_STATE
+
+
+def is_image_feature(key: str) -> bool:
+    """Check if a feature key represents an image feature.
+
+    Args:
+        key: The feature key to check
+
+    Returns:
+        True if the key represents an image feature, False otherwise
+    """
+    return key.startswith(OBS_IMAGE)
+
+
+@dataclass
+class ConcurrencyConfig:
+    """Configuration for the concurrency of the actor and learner.
+    Possible values are:
+    - "threads": Use threads for the actor and learner.
+    - "processes": Use processes for the actor and learner.
+    """
+
+    actor: str = "threads"
+    learner: str = "threads"
+
+
+@dataclass
+class ActorLearnerConfig:
+    learner_host: str = "127.0.0.1"
+    learner_port: int = 50051
+    policy_parameters_push_frequency: int = 4
+    queue_get_timeout: float = 2
+
+
+@dataclass
+class CriticNetworkConfig:
+    hidden_dims: list[int] = field(default_factory=lambda: [256, 256])
+    activate_final: bool = True
+    final_activation: str | None = None
+
+
+@dataclass
+class ActorNetworkConfig:
+    hidden_dims: list[int] = field(default_factory=lambda: [256, 256])
+    activate_final: bool = True
+    use_layer_norm: bool = True
+
+
+@dataclass
+class NoiseActorConfig:
+    """Configuration for the noise actor in DSRL.
+    The noise actor outputs noise that gets fed to the diffusion policy.
+    """
+
+    use_tanh_squash: bool = False  # Whether to bound the noise output
+    std_min: float = 1e-5
+    std_max: float = 2.0
+    init_final: float = 0.05
+
+
+@PreTrainedConfig.register_subclass("dsrl")
+@dataclass
+class DSRLConfig(PreTrainedConfig):
+    """Diffusion Steering via Reinforcement Learning (DSRL) configuration."""
+
+    # Mapping of feature types to normalization modes
+    normalization_mapping: dict[str, NormalizationMode] = field(
+        default_factory=lambda: {
+            "VISUAL": NormalizationMode.MEAN_STD,
+            "STATE": NormalizationMode.MIN_MAX,
+            "ENV": NormalizationMode.MIN_MAX,
+            "ACTION": NormalizationMode.MIN_MAX,
+        }
+    )
+
+    # Statistics for normalizing different types of inputs
+    dataset_stats: dict[str, dict[str, list[float]]] | None = field(
+        default_factory=lambda: {
+            OBS_IMAGE: {
+                "mean": [0.485, 0.456, 0.406],
+                "std": [0.229, 0.224, 0.225],
+            },
+            OBS_STATE: {
+                "min": [0.0, 0.0],
+                "max": [1.0, 1.0],
+            },
+            ACTION: {
+                "min": [0.0, 0.0, 0.0],
+                "max": [1.0, 1.0, 1.0],
+            },
+        }
+    )
+
+    # Architecture specifics
+    # Device to run the model on (e.g., "cuda", "cpu")
+    device: str = "cpu"
+    # Device to store the model on
+    storage_device: str = "cpu"
+    # Name of the vision encoder model (Set to "helper2424/resnet10" for hil serl resnet10)
+    vision_encoder_name: str | None = None
+    # Whether to freeze the vision encoder during training
+    freeze_vision_encoder: bool = True
+    # Hidden dimension size for the image encoder
+    image_encoder_hidden_dim: int = 32
+    # Whether to use a shared encoder for actor and critic
+    shared_encoder: bool = True
+    # Number of discrete actions, eg for gripper actions
+    num_discrete_actions: int | None = None
+    # Dimension of the image embedding pooling
+    image_embedding_pooling_dim: int = 8
+
+    # Name of the action policy
+    action_policy_name: str = "pi0"
+    action_policy_weights: str | None = "lerobot/pi0_base"
+
+    # Training parameter
+    # Number of steps for online training
+    online_steps: int = 1000000
+    # Number of steps for offline training
+    offline_steps: int = 100000
+    # Capacity of the online replay buffer
+    online_buffer_capacity: int = 100000
+    # Capacity of the offline replay buffer
+    offline_buffer_capacity: int = 100000
+    # Whether to use asynchronous prefetching for the buffers
+    async_prefetch: bool = False
+    # Number of steps before learning starts
+    online_step_before_learning: int = 100
+    # Frequency of policy updates
+    policy_update_freq: int = 1
+
+    # SAC algorithm parameters
+    discount: float = 0.99
+    # Initial temperature value
+    temperature_init: float = 1.0
+    # Number of critics in the ensemble
+    num_critics: int = 2
+    # Number of subsampled critics for training
+    num_subsample_critics: int | None = None
+    # Learning rate for the critic network
+    critic_lr: float = 3e-4
+    # Learning rate for the actor network
+    actor_lr: float = 3e-4
+    # Learning rate for the temperature parameter
+    temperature_lr: float = 3e-4
+    # Weight for the critic target update
+    critic_target_update_weight: float = 0.005
+    # Update-to-data ratio for the UTD algorithm (If you want enable utd_ratio, you need to set it to >1)
+    utd_ratio: int = 1
+    # Hidden dimension size for the state encoder
+    state_encoder_hidden_dim: int = 256
+    # Dimension of the latent space
+    latent_dim: int = 256
+    # Target entropy for the SAC algorithm
+    target_entropy: float | None = None
+    # Whether to use backup entropy for the SAC algorithm
+    use_backup_entropy: bool = True
+    # Gradient clipping norm for the SAC algorithm
+    grad_clip_norm: float = 40.0
+
+    # Network configuration
+    # Configuration for the critic network architecture
+    critic_network_kwargs: CriticNetworkConfig = field(default_factory=CriticNetworkConfig)
+    # Configuration for the noise critic network architecture
+    noise_critic_network_kwargs: CriticNetworkConfig = field(default_factory=CriticNetworkConfig)
+    # Configuration for the noise actor network architecture
+    noise_actor_network_kwargs: ActorNetworkConfig = field(default_factory=ActorNetworkConfig)
+    # Configuration for the noise actor specific parameters
+    noise_actor_kwargs: NoiseActorConfig = field(default_factory=NoiseActorConfig)
+    # Configuration for actor-learner architecture
+    actor_learner_config: ActorLearnerConfig = field(default_factory=ActorLearnerConfig)
+    # Configuration for concurrency settings (you can use threads or processes for the actor and learner)
+    concurrency: ConcurrencyConfig = field(default_factory=ConcurrencyConfig)
+
+    # Optimizations
+    use_torch_compile: bool = True
+
+    def __post_init__(self):
+        super().__post_init__()
+
+    def get_optimizer_preset(self) -> MultiAdamConfig:
+        return MultiAdamConfig(
+            weight_decay=0.0,
+            optimizer_groups={
+                "critic_action": {"lr": self.critic_lr},
+                "critic_noise": {"lr": self.critic_lr},
+                "noise_actor": {"lr": self.actor_lr},
+                "temperature": {"lr": self.temperature_lr},
+            },
+        )
+
+    def get_scheduler_preset(self) -> None:
+        return None
+
+    def validate_features(self) -> None:
+        has_image = any(is_image_feature(key) for key in self.input_features)
+        has_state = OBS_STATE in self.input_features
+
+        if not (has_state or has_image):
+            raise ValueError(
+                "You must provide either 'observation.state' or an image observation (key starting with 'observation.image') in the input features"
+            )
+
+        if ACTION not in self.output_features:
+            raise ValueError("You must provide 'action' in the output features")
+
+    @property
+    def image_features(self) -> list[str]:
+        return [key for key in self.input_features if is_image_feature(key)]
+
+    @property
+    def observation_delta_indices(self) -> list:
+        return None
+
+    @property
+    def action_delta_indices(self) -> list:
+        return None
+
+    @property
+    def reward_delta_indices(self) -> None:
+        return None

src/lerobot/policies/dsrl/processor_dsrl.py

@@ -0,0 +1,89 @@
+# !/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.
+"""
+Processor for DSRL policy.
+
+DSRL uses a similar processing pipeline as SAC since it operates on
+state-action transitions. The main difference is that internally it
+also works with noise, but that's handled within the policy itself.
+"""
+
+from typing import Any
+
+import torch
+
+from lerobot.policies.dsrl.configuration_dsrl import DSRLConfig
+from lerobot.processor import (
+    AddBatchDimensionProcessorStep,
+    DeviceProcessorStep,
+    NormalizerProcessorStep,
+    PolicyAction,
+    PolicyProcessorPipeline,
+    RenameObservationsProcessorStep,
+    UnnormalizerProcessorStep,
+)
+from lerobot.processor.converters import (
+    policy_action_to_transition,
+    transition_to_policy_action,
+)
+from lerobot.utils.constants import POLICY_POSTPROCESSOR_DEFAULT_NAME, POLICY_PREPROCESSOR_DEFAULT_NAME
+
+
+def make_dsrl_pre_post_processors(
+    config: DSRLConfig,
+    dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None,
+) -> tuple[
+    PolicyProcessorPipeline[dict, dict],
+    PolicyProcessorPipeline[PolicyAction, PolicyAction],
+]:
+    """Create preprocessor and postprocessor pipelines for DSRL policy.
+
+    Args:
+        config: DSRL policy configuration
+        dataset_stats: Optional dataset statistics for normalization
+
+    Returns:
+        Tuple of (preprocessor, postprocessor) pipelines
+    """
+    input_steps = [
+        RenameObservationsProcessorStep(rename_map={}),
+        AddBatchDimensionProcessorStep(),
+        DeviceProcessorStep(device=config.device),
+        NormalizerProcessorStep(
+            features={**config.input_features, **config.output_features},
+            norm_map=config.normalization_mapping,
+            stats=dataset_stats,
+        ),
+    ]
+    output_steps = [
+        UnnormalizerProcessorStep(
+            features=config.output_features, norm_map=config.normalization_mapping, stats=dataset_stats
+        ),
+        DeviceProcessorStep(device="cpu"),
+    ]
+    return (
+        PolicyProcessorPipeline[dict[str, Any], dict[str, Any]](
+            steps=input_steps,
+            name=POLICY_PREPROCESSOR_DEFAULT_NAME,
+        ),
+        PolicyProcessorPipeline[PolicyAction, PolicyAction](
+            steps=output_steps,
+            name=POLICY_POSTPROCESSOR_DEFAULT_NAME,
+            to_transition=policy_action_to_transition,
+            to_output=transition_to_policy_action,
+        ),
+    )

src/lerobot/policies/factory.py

@@ -30,6 +30,7 @@ from lerobot.envs.configs import EnvConfig
 from lerobot.envs.utils import env_to_policy_features
 from lerobot.policies.act.configuration_act import ACTConfig
 from lerobot.policies.diffusion.configuration_diffusion import DiffusionConfig
+from lerobot.policies.dsrl.configuration_dsrl import DSRLConfig
 from lerobot.policies.pi0.configuration_pi0 import PI0Config
 from lerobot.policies.pi0fast.configuration_pi0fast import PI0FASTConfig
 from lerobot.policies.pi05.configuration_pi05 import PI05Config
@@ -58,7 +59,7 @@ def get_policy_class(name: str) -> type[PreTrainedPolicy]:
 
     Args:
         name: The name of the policy. Supported names are "tdmpc", "diffusion", "act",
-              "vqbet", "pi0", "pi0fast", "sac", "reward_classifier", "smolvla".
+              "vqbet", "pi0", "pi0fast", "sac", "reward_classifier", "smolvla", "dsrl".
 
     Returns:
         The policy class corresponding to the given name.
@@ -106,6 +107,10 @@ def get_policy_class(name: str) -> type[PreTrainedPolicy]:
         from lerobot.policies.smolvla.modeling_smolvla import SmolVLAPolicy
 
         return SmolVLAPolicy
+    elif name == "dsrl":
+        from lerobot.policies.dsrl.modeling_dsrl import DSRLPolicy
+
+        return DSRLPolicy
     else:
         raise NotImplementedError(f"Policy with name {name} is not implemented.")
 
@@ -120,7 +125,7 @@ def make_policy_config(policy_type: str, **kwargs) -> PreTrainedConfig:
     Args:
         policy_type: The type of the policy. Supported types include "tdmpc",
                      "diffusion", "act", "vqbet", "pi0", "pi0fast", "sac", "smolvla",
-                     "reward_classifier".
+                     "reward_classifier", "dsrl".
         **kwargs: Keyword arguments to be passed to the configuration class constructor.
 
     Returns:
@@ -149,6 +154,8 @@ def make_policy_config(policy_type: str, **kwargs) -> PreTrainedConfig:
         return SmolVLAConfig(**kwargs)
     elif policy_type == "reward_classifier":
         return RewardClassifierConfig(**kwargs)
+    elif policy_type == "dsrl":
+        return DSRLConfig(**kwargs)
     else:
         raise ValueError(f"Policy type '{policy_type}' is not available.")
 
@@ -307,6 +314,13 @@ def make_pre_post_processors(
             config=policy_cfg,
             dataset_stats=kwargs.get("dataset_stats"),
         )
+    elif isinstance(policy_cfg, DSRLConfig):
+        from lerobot.policies.dsrl.processor_dsrl import make_dsrl_pre_post_processors
+
+        processors = make_dsrl_pre_post_processors(
+            config=policy_cfg,
+            dataset_stats=kwargs.get("dataset_stats"),
+        )
 
     else:
         raise NotImplementedError(f"Processor for policy type '{policy_cfg.type}' is not implemented.")

src/lerobot/policies/pi0/modeling_pi0.py

@@ -1148,7 +1148,7 @@ class PI0Policy(PreTrainedPolicy):
         return self._action_queue.popleft()
 
     @torch.no_grad()
-    def predict_action_chunk(self, batch: dict[str, Tensor]) -> Tensor:
+    def predict_action_chunk(self, batch: dict[str, Tensor], noise: Tensor | None = None) -> Tensor:
         """Predict a chunk of actions given environment observations."""
         self.eval()
 
@@ -1158,7 +1158,7 @@ class PI0Policy(PreTrainedPolicy):
         state = self.prepare_state(batch)
 
         # Sample actions using the model
-        actions = self.model.sample_actions(images, img_masks, lang_tokens, lang_masks, state)
+        actions = self.model.sample_actions(images, img_masks, lang_tokens, lang_masks, state, noise)
 
         # Unpad actions to actual action dimension
         original_action_dim = self.config.output_features[ACTION].shape[0]

src/lerobot/policies/pi05/modeling_pi05.py

@@ -1120,7 +1120,7 @@ class PI05Policy(PreTrainedPolicy):
         return self._action_queue.popleft()
 
     @torch.no_grad()
-    def predict_action_chunk(self, batch: dict[str, Tensor]) -> Tensor:
+    def predict_action_chunk(self, batch: dict[str, Tensor], noise: Tensor | None = None) -> Tensor:
         """Predict a chunk of actions given environment observations."""
         self.eval()
 
@@ -1129,7 +1129,7 @@ class PI05Policy(PreTrainedPolicy):
         tokens, masks = batch[f"{OBS_LANGUAGE_TOKENS}"], batch[f"{OBS_LANGUAGE_ATTENTION_MASK}"]
 
         # Sample actions using the model (no separate state needed for PI05)
-        actions = self.model.sample_actions(images, img_masks, tokens, masks)
+        actions = self.model.sample_actions(images, img_masks, tokens, masks, noise)
 
         # Unpad actions to actual action dimension
         original_action_dim = self.config.output_features[ACTION].shape[0]

src/lerobot/rl/buffer.py

@@ -86,7 +86,7 @@ class ReplayBuffer:
         image_augmentation_function: Callable | None = None,
         use_drq: bool = True,
         storage_device: str = "cpu",
-        optimize_memory: bool = False,
+        optimize_memory: bool = True
     ):
         """
         Replay buffer for storing transitions.
@@ -136,6 +136,7 @@ class ReplayBuffer:
         complementary_info: dict[str, torch.Tensor] | None = None,
     ):
         """Initialize the storage tensors based on the first transition."""
+        self.capacity = 1000
         # Determine shapes from the first transition
         state_shapes = {key: val.squeeze(0).shape for key, val in state.items()}
         action_shape = action.squeeze(0).shape
@@ -444,7 +445,7 @@ class ReplayBuffer:
         if capacity is None:
             capacity = len(lerobot_dataset)
 
-        if capacity < len(lerobot_dataset):
+        if capacity < 1000: #len(lerobot_dataset):
             raise ValueError(
                 "The capacity of the ReplayBuffer must be greater than or equal to the length of the LeRobotDataset."
             )
@@ -476,13 +477,14 @@ class ReplayBuffer:
                 and first_transition["complementary_info"] is not None
             ):
                 first_complementary_info = {
-                    k: v.to(device) for k, v in first_transition["complementary_info"].items()
+                    k: v.to for k, v in first_transition["complementary_info"].items()
                 }
 
             replay_buffer._initialize_storage(
                 state=first_state, action=first_action, complementary_info=first_complementary_info
             )
 
+        num_samples = 0
         # Fill the buffer with all transitions
         for data in list_transition:
             for k, v in data.items():
@@ -503,6 +505,9 @@ class ReplayBuffer:
                 truncated=False,  # NOTE: Truncation are not supported yet in lerobot dataset
                 complementary_info=data.get("complementary_info", None),
             )
+            num_samples += 1
+            if num_samples >= 1000:
+                return replay_buffer
 
         return replay_buffer
 
@@ -645,7 +650,7 @@ class ReplayBuffer:
             raise ValueError("State keys must be provided when converting LeRobotDataset to Transitions.")
 
         transitions = []
-        num_frames = len(dataset)
+        num_frames = 1000 # len(dataset)
 
         # Check if the dataset has "next.done" key
         sample = dataset[0]
@@ -659,7 +664,7 @@ class ReplayBuffer:
         if not has_done_key:
             print("'next.done' key not found in dataset. Inferring from episode boundaries...")
 
-        for i in tqdm(range(num_frames)):
+        for i in tqdm(range(1000)): # num_frames)):
             current_sample = dataset[i]
 
             # ----- 1) Current state -----