Merge remote-tracking branch 'origin/main' into user/aliberts/2024_05_14_compare_policies

lerobot/__init__.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 """
 This file contains lists of available environments, dataset and policies to reflect the current state of LeRobot library.
 We do not want to import all the dependencies, but instead we keep it lightweight to ensure fast access to these variables.

lerobot/__version__.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 """To enable `lerobot.__version__`"""
 
 from importlib.metadata import PackageNotFoundError, version

lerobot/common/datasets/_video_benchmark/run_video_benchmark.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 import json
 import random
 import shutil

lerobot/common/datasets/factory.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 import logging
 
 import torch

lerobot/common/datasets/lerobot_dataset.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 import os
 from pathlib import Path
 

lerobot/common/datasets/push_dataset_to_hub/_diffusion_policy_replay_buffer.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 """Helper code for loading PushT dataset from Diffusion Policy (https://diffusion-policy.cs.columbia.edu/)
 
 Copied from the original Diffusion Policy repository and used in our `download_and_upload_dataset.py` script.

lerobot/common/datasets/push_dataset_to_hub/_download_raw.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 """
 This file contains all obsolete download scripts. They are centralized here to not have to load
 useless dependencies when using datasets.

lerobot/common/datasets/push_dataset_to_hub/_umi_imagecodecs_numcodecs.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 # imagecodecs/numcodecs.py
 
 # Copyright (c) 2021-2022, Christoph Gohlke

lerobot/common/datasets/push_dataset_to_hub/aloha_hdf5_format.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 """
 Contains utilities to process raw data format of HDF5 files like in: https://github.com/tonyzhaozh/act
 """

lerobot/common/datasets/push_dataset_to_hub/compute_stats.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 from copy import deepcopy
 from math import ceil
 

lerobot/common/datasets/push_dataset_to_hub/pusht_zarr_format.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 """Process zarr files formatted like in: https://github.com/real-stanford/diffusion_policy"""
 
 import shutil

lerobot/common/datasets/push_dataset_to_hub/umi_zarr_format.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 """Process UMI (Universal Manipulation Interface) data stored in Zarr format like in: https://github.com/real-stanford/universal_manipulation_interface"""
 
 import logging

lerobot/common/datasets/push_dataset_to_hub/utils.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 from concurrent.futures import ThreadPoolExecutor
 from pathlib import Path
 

lerobot/common/datasets/push_dataset_to_hub/xarm_pkl_format.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 """Process pickle files formatted like in: https://github.com/fyhMer/fowm"""
 
 import pickle

lerobot/common/datasets/utils.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 import json
 from pathlib import Path
 

lerobot/common/datasets/video_utils.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 import logging
 import subprocess
 import warnings

lerobot/common/envs/factory.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 import importlib
 
 import gymnasium as gym

lerobot/common/envs/utils.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 import einops
 import numpy as np
 import torch

lerobot/common/logger.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 # TODO(rcadene, alexander-soare): clean this file
 """Borrowed from https://github.com/fyhMer/fowm/blob/main/src/logger.py"""
 

lerobot/common/policies/act/configuration_act.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# Copyright 2024 Tony Z. Zhao and 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
 
 
@@ -130,10 +145,3 @@ class ACTConfig:
             raise ValueError(
                 f"Multiple observation steps not handled yet. Got `nobs_steps={self.n_obs_steps}`"
             )
-        # Check that there is only one image.
-        # TODO(alexander-soare): generalize this to multiple images.
-        if (
-            sum(k.startswith("observation.images.") for k in self.input_shapes) != 1
-            or "observation.images.top" not in self.input_shapes
-        ):
-            raise ValueError('For now, only "observation.images.top" is accepted for an image input.')

lerobot/common/policies/act/modeling_act.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# Copyright 2024 Tony Z. Zhao and 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 Chunking Transformer Policy
 
 As per Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware (https://arxiv.org/abs/2304.13705).
@@ -47,6 +62,7 @@ class ACTPolicy(nn.Module, PyTorchModelHubMixin):
         if config is None:
             config = ACTConfig()
         self.config = config
+
         self.normalize_inputs = Normalize(
             config.input_shapes, config.input_normalization_modes, dataset_stats
         )
@@ -56,8 +72,13 @@ class ACTPolicy(nn.Module, PyTorchModelHubMixin):
         self.unnormalize_outputs = Unnormalize(
             config.output_shapes, config.output_normalization_modes, dataset_stats
         )
+
         self.model = ACT(config)
 
+        self.expected_image_keys = [k for k in config.input_shapes if k.startswith("observation.image")]
+
+        self.reset()
+
     def reset(self):
         """This should be called whenever the environment is reset."""
         if self.config.n_action_steps is not None:
@@ -71,30 +92,27 @@ class ACTPolicy(nn.Module, PyTorchModelHubMixin):
         environment. It works by managing the actions in a queue and only calling `select_actions` when the
         queue is empty.
         """
-        assert "observation.images.top" in batch
-        assert "observation.state" in batch
-
         self.eval()
 
         batch = self.normalize_inputs(batch)
-        self._stack_images(batch)
+        batch["observation.images"] = torch.stack([batch[k] for k in self.expected_image_keys], dim=-4)
 
         if len(self._action_queue) == 0:
-            # `self.model.forward` returns a (batch_size, n_action_steps, action_dim) tensor, but the queue
+            actions = self.model(batch)[0][:, : self.config.n_action_steps]
-            # effectively has shape (n_action_steps, batch_size, *), hence the transpose.
-            actions = self.model(batch)[0][: self.config.n_action_steps]
 
             # TODO(rcadene): make _forward return output dictionary?
             actions = self.unnormalize_outputs({"action": actions})["action"]
 
+            # `self.model.forward` returns a (batch_size, n_action_steps, action_dim) tensor, but the queue
+            # effectively has shape (n_action_steps, batch_size, *), hence the transpose.
             self._action_queue.extend(actions.transpose(0, 1))
         return self._action_queue.popleft()
 
     def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]:
         """Run the batch through the model and compute the loss for training or validation."""
         batch = self.normalize_inputs(batch)
+        batch["observation.images"] = torch.stack([batch[k] for k in self.expected_image_keys], dim=-4)
         batch = self.normalize_targets(batch)
-        self._stack_images(batch)
         actions_hat, (mu_hat, log_sigma_x2_hat) = self.model(batch)
 
         l1_loss = (
@@ -117,21 +135,6 @@ class ACTPolicy(nn.Module, PyTorchModelHubMixin):
 
         return loss_dict
 
-    def _stack_images(self, batch: dict[str, Tensor]) -> dict[str, Tensor]:
-        """Stacks all the images in a batch and puts them in a new key: "observation.images".
-
-        This function expects `batch` to have (at least):
-        {
-            "observation.state": (B, state_dim) batch of robot states.
-            "observation.images.{name}": (B, C, H, W) tensor of images.
-        }
-        """
-        # Stack images in the order dictated by input_shapes.
-        batch["observation.images"] = torch.stack(
-            [batch[k] for k in self.config.input_shapes if k.startswith("observation.images.")],
-            dim=-4,
-        )
-
 
 class ACT(nn.Module):
     """Action Chunking Transformer: The underlying neural network for ACTPolicy.
@@ -161,10 +164,10 @@ class ACT(nn.Module):
               │ encoder │ │     │ │Transf.│             │
               │         │ │     │ │encoder│             │
               └───▲─────┘ │     │ │       │             │
-                  │       │     │ └───▲───┘             │
+                  │       │     │ └▲──▲─▲─┘             │
-                  │       │     │     │                 │
+                  │       │     │  │  │ │               │
-                inputs    └─────┼─────┘                 │
+                inputs    └─────┼──┘  │ image emb.      │
-                                │                       │
+                                │    state emb.         │
                                 └───────────────────────┘
     """
 
@@ -306,18 +309,18 @@ class ACT(nn.Module):
             all_cam_features.append(cam_features)
             all_cam_pos_embeds.append(cam_pos_embed)
         # Concatenate camera observation feature maps and positional embeddings along the width dimension.
-        encoder_in = torch.cat(all_cam_features, axis=3)
+        encoder_in = torch.cat(all_cam_features, axis=-1)
-        cam_pos_embed = torch.cat(all_cam_pos_embeds, axis=3)
+        cam_pos_embed = torch.cat(all_cam_pos_embeds, axis=-1)
 
         # Get positional embeddings for robot state and latent.
-        robot_state_embed = self.encoder_robot_state_input_proj(batch["observation.state"])
+        robot_state_embed = self.encoder_robot_state_input_proj(batch["observation.state"])  # (B, C)
-        latent_embed = self.encoder_latent_input_proj(latent_sample)
+        latent_embed = self.encoder_latent_input_proj(latent_sample)  # (B, C)
 
         # Stack encoder input and positional embeddings moving to (S, B, C).
         encoder_in = torch.cat(
             [
                 torch.stack([latent_embed, robot_state_embed], axis=0),
-                encoder_in.flatten(2).permute(2, 0, 1),
+                einops.rearrange(encoder_in, "b c h w -> (h w) b c"),
             ]
         )
         pos_embed = torch.cat(

lerobot/common/policies/diffusion/configuration_diffusion.py

@@ -1,3 +1,19 @@
+#!/usr/bin/env python
+
+# Copyright 2024 Columbia Artificial Intelligence, Robotics Lab,
+# and 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
 
 
@@ -132,14 +148,21 @@ class DiffusionConfig:
             raise ValueError(
                 f"`vision_backbone` must be one of the ResNet variants. Got {self.vision_backbone}."
             )
+        # There should only be one image key.
+        image_keys = {k for k in self.input_shapes if k.startswith("observation.image")}
+        if len(image_keys) != 1:
+            raise ValueError(
+                f"{self.__class__.__name__} only handles one image for now. Got image keys {image_keys}."
+            )
+        image_key = next(iter(image_keys))
         if (
-            self.crop_shape[0] > self.input_shapes["observation.image"][1]
+            self.crop_shape[0] > self.input_shapes[image_key][1]
-            or self.crop_shape[1] > self.input_shapes["observation.image"][2]
+            or self.crop_shape[1] > self.input_shapes[image_key][2]
         ):
             raise ValueError(
-                f'`crop_shape` should fit within `input_shapes["observation.image"]`. Got {self.crop_shape} '
+                f"`crop_shape` should fit within `input_shapes[{image_key}]`. Got {self.crop_shape} "
-                f'for `crop_shape` and {self.input_shapes["observation.image"]} for '
+                f"for `crop_shape` and {self.input_shapes[image_key]} for "
-                '`input_shapes["observation.image"]`.'
+                "`input_shapes[{image_key}]`."
             )
         supported_prediction_types = ["epsilon", "sample"]
         if self.prediction_type not in supported_prediction_types:

lerobot/common/policies/diffusion/modeling_diffusion.py

@@ -1,8 +1,24 @@
+#!/usr/bin/env python
+
+# Copyright 2024 Columbia Artificial Intelligence, Robotics Lab,
+# and 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.
 """Diffusion Policy as per "Diffusion Policy: Visuomotor Policy Learning via Action Diffusion"
 
 TODO(alexander-soare):
-  - Remove reliance on Robomimic for SpatialSoftmax.
   - Remove reliance on diffusers for DDPMScheduler and LR scheduler.
+  - Make compatible with multiple image keys.
 """
 
 import math
@@ -10,13 +26,13 @@ from collections import deque
 from typing import Callable
 
 import einops
+import numpy as np
 import torch
 import torch.nn.functional as F  # noqa: N812
 import torchvision
 from diffusers.schedulers.scheduling_ddim import DDIMScheduler
 from diffusers.schedulers.scheduling_ddpm import DDPMScheduler
 from huggingface_hub import PyTorchModelHubMixin
-from robomimic.models.base_nets import SpatialSoftmax
 from torch import Tensor, nn
 
 from lerobot.common.policies.diffusion.configuration_diffusion import DiffusionConfig
@@ -67,10 +83,18 @@ class DiffusionPolicy(nn.Module, PyTorchModelHubMixin):
 
         self.diffusion = DiffusionModel(config)
 
+        image_keys = [k for k in config.input_shapes if k.startswith("observation.image")]
+        # Note: This check is covered in the post-init of the config but have a sanity check just in case.
+        if len(image_keys) != 1:
+            raise NotImplementedError(
+                f"{self.__class__.__name__} only handles one image for now. Got image keys {image_keys}."
+            )
+        self.input_image_key = image_keys[0]
+
+        self.reset()
+
     def reset(self):
-        """
+        """Clear observation and action queues. Should be called on `env.reset()`"""
-        Clear observation and action queues. Should be called on `env.reset()`
-        """
         self._queues = {
             "observation.image": deque(maxlen=self.config.n_obs_steps),
             "observation.state": deque(maxlen=self.config.n_obs_steps),
@@ -99,16 +123,14 @@ class DiffusionPolicy(nn.Module, PyTorchModelHubMixin):
         "horizon" may not the best name to describe what the variable actually means, because this period is
         actually measured from the first observation which (if `n_obs_steps` > 1) happened in the past.
         """
-        assert "observation.image" in batch
-        assert "observation.state" in batch
-
         batch = self.normalize_inputs(batch)
+        batch["observation.image"] = batch[self.input_image_key]
 
         self._queues = populate_queues(self._queues, batch)
 
         if len(self._queues["action"]) == 0:
             # stack n latest observations from the queue
-            batch = {key: torch.stack(list(self._queues[key]), dim=1) for key in batch}
+            batch = {k: torch.stack(list(self._queues[k]), dim=1) for k in batch if k in self._queues}
             actions = self.diffusion.generate_actions(batch)
 
             # TODO(rcadene): make above methods return output dictionary?
@@ -122,6 +144,7 @@ class DiffusionPolicy(nn.Module, PyTorchModelHubMixin):
     def forward(self, batch: dict[str, Tensor]) -> dict[str, Tensor]:
         """Run the batch through the model and compute the loss for training or validation."""
         batch = self.normalize_inputs(batch)
+        batch["observation.image"] = batch[self.input_image_key]
         batch = self.normalize_targets(batch)
         loss = self.diffusion.compute_loss(batch)
         return {"loss": loss}
@@ -199,13 +222,12 @@ class DiffusionModel(nn.Module):
 
     def generate_actions(self, batch: dict[str, Tensor]) -> Tensor:
         """
-        This function expects `batch` to have (at least):
+        This function expects `batch` to have:
         {
             "observation.state": (B, n_obs_steps, state_dim)
             "observation.image": (B, n_obs_steps, C, H, W)
         }
         """
-        assert set(batch).issuperset({"observation.state", "observation.image"})
         batch_size, n_obs_steps = batch["observation.state"].shape[:2]
         assert n_obs_steps == self.config.n_obs_steps
 
@@ -289,6 +311,77 @@ class DiffusionModel(nn.Module):
         return loss.mean()
 
 
+class SpatialSoftmax(nn.Module):
+    """
+    Spatial Soft Argmax operation described in "Deep Spatial Autoencoders for Visuomotor Learning" by Finn et al.
+    (https://arxiv.org/pdf/1509.06113). A minimal port of the robomimic implementation.
+
+    At a high level, this takes 2D feature maps (from a convnet/ViT) and returns the "center of mass"
+    of activations of each channel, i.e., keypoints in the image space for the policy to focus on.
+
+    Example: take feature maps of size (512x10x12). We generate a grid of normalized coordinates (10x12x2):
+    -----------------------------------------------------
+    | (-1., -1.)   | (-0.82, -1.)   | ... | (1., -1.)   |
+    | (-1., -0.78) | (-0.82, -0.78) | ... | (1., -0.78) |
+    | ...          | ...            | ... | ...         |
+    | (-1., 1.)    | (-0.82, 1.)    | ... | (1., 1.)    |
+    -----------------------------------------------------
+    This is achieved by applying channel-wise softmax over the activations (512x120) and computing the dot
+    product with the coordinates (120x2) to get expected points of maximal activation (512x2).
+
+    The example above results in 512 keypoints (corresponding to the 512 input channels). We can optionally
+    provide num_kp != None to control the number of keypoints. This is achieved by a first applying a learnable
+    linear mapping (in_channels, H, W) -> (num_kp, H, W).
+    """
+
+    def __init__(self, input_shape, num_kp=None):
+        """
+        Args:
+            input_shape (list): (C, H, W) input feature map shape.
+            num_kp (int): number of keypoints in output. If None, output will have the same number of channels as input.
+        """
+        super().__init__()
+
+        assert len(input_shape) == 3
+        self._in_c, self._in_h, self._in_w = input_shape
+
+        if num_kp is not None:
+            self.nets = torch.nn.Conv2d(self._in_c, num_kp, kernel_size=1)
+            self._out_c = num_kp
+        else:
+            self.nets = None
+            self._out_c = self._in_c
+
+        # we could use torch.linspace directly but that seems to behave slightly differently than numpy
+        # and causes a small degradation in pc_success of pre-trained models.
+        pos_x, pos_y = np.meshgrid(np.linspace(-1.0, 1.0, self._in_w), np.linspace(-1.0, 1.0, self._in_h))
+        pos_x = torch.from_numpy(pos_x.reshape(self._in_h * self._in_w, 1)).float()
+        pos_y = torch.from_numpy(pos_y.reshape(self._in_h * self._in_w, 1)).float()
+        # register as buffer so it's moved to the correct device.
+        self.register_buffer("pos_grid", torch.cat([pos_x, pos_y], dim=1))
+
+    def forward(self, features: Tensor) -> Tensor:
+        """
+        Args:
+            features: (B, C, H, W) input feature maps.
+        Returns:
+            (B, K, 2) image-space coordinates of keypoints.
+        """
+        if self.nets is not None:
+            features = self.nets(features)
+
+        # [B, K, H, W] -> [B * K, H * W] where K is number of keypoints
+        features = features.reshape(-1, self._in_h * self._in_w)
+        # 2d softmax normalization
+        attention = F.softmax(features, dim=-1)
+        # [B * K, H * W] x [H * W, 2] -> [B * K, 2] for spatial coordinate mean in x and y dimensions
+        expected_xy = attention @ self.pos_grid
+        # reshape to [B, K, 2]
+        feature_keypoints = expected_xy.view(-1, self._out_c, 2)
+
+        return feature_keypoints
+
+
 class DiffusionRgbEncoder(nn.Module):
     """Encoder an RGB image into a 1D feature vector.
 
@@ -329,9 +422,12 @@ class DiffusionRgbEncoder(nn.Module):
 
         # Set up pooling and final layers.
         # Use a dry run to get the feature map shape.
-        # The dummy input should take the number of image channels from `config.input_shapes` and it should use the
+        # The dummy input should take the number of image channels from `config.input_shapes` and it should
-        # height and width from `config.crop_shape`.
+        # use the height and width from `config.crop_shape`.
-        dummy_input = torch.zeros(size=(1, config.input_shapes["observation.image"][0], *config.crop_shape))
+        image_keys = [k for k in config.input_shapes if k.startswith("observation.image")]
+        assert len(image_keys) == 1
+        image_key = image_keys[0]
+        dummy_input = torch.zeros(size=(1, config.input_shapes[image_key][0], *config.crop_shape))
         with torch.inference_mode():
             dummy_feature_map = self.backbone(dummy_input)
         feature_map_shape = tuple(dummy_feature_map.shape[1:])

lerobot/common/policies/factory.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 import inspect
 import logging
 

lerobot/common/policies/normalize.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 import torch
 from torch import Tensor, nn
 

lerobot/common/policies/policy_protocol.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 """A protocol that all policies should follow.
 
 This provides a mechanism for type-hinting and isinstance checks without requiring the policies classes

lerobot/common/policies/tdmpc/configuration_tdmpc.py

@@ -1,3 +1,19 @@
+#!/usr/bin/env python
+
+# Copyright 2024 Nicklas Hansen, Xiaolong Wang, Hao Su,
+# and 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
 
 
@@ -131,12 +147,18 @@ class TDMPCConfig:
 
     def __post_init__(self):
         """Input validation (not exhaustive)."""
-        if self.input_shapes["observation.image"][-2] != self.input_shapes["observation.image"][-1]:
+        # There should only be one image key.
+        image_keys = {k for k in self.input_shapes if k.startswith("observation.image")}
+        if len(image_keys) != 1:
+            raise ValueError(
+                f"{self.__class__.__name__} only handles one image for now. Got image keys {image_keys}."
+            )
+        image_key = next(iter(image_keys))
+        if self.input_shapes[image_key][-2] != self.input_shapes[image_key][-1]:
             # TODO(alexander-soare): This limitation is solely because of code in the random shift
             # augmentation. It should be able to be removed.
             raise ValueError(
-                "Only square images are handled now. Got image shape "
+                f"Only square images are handled now. Got image shape {self.input_shapes[image_key]}."
-                f"{self.input_shapes['observation.image']}."
             )
         if self.n_gaussian_samples <= 0:
             raise ValueError(

lerobot/common/policies/tdmpc/modeling_tdmpc.py

@@ -1,3 +1,19 @@
+#!/usr/bin/env python
+
+# Copyright 2024 Nicklas Hansen, Xiaolong Wang, Hao Su,
+# and 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.
 """Implementation of Finetuning Offline World Models in the Real World.
 
 The comments in this code may sometimes refer to these references:
@@ -96,13 +112,12 @@ class TDMPCPolicy(nn.Module, PyTorchModelHubMixin):
             config.output_shapes, config.output_normalization_modes, dataset_stats
         )
 
-    def save(self, fp):
+        image_keys = [k for k in config.input_shapes if k.startswith("observation.image")]
-        """Save state dict of TOLD model to filepath."""
+        # Note: This check is covered in the post-init of the config but have a sanity check just in case.
-        torch.save(self.state_dict(), fp)
+        assert len(image_keys) == 1
+        self.input_image_key = image_keys[0]
 
-    def load(self, fp):
+        self.reset()
-        """Load a saved state dict from filepath into current agent."""
-        self.load_state_dict(torch.load(fp))
 
     def reset(self):
         """
@@ -121,10 +136,8 @@ class TDMPCPolicy(nn.Module, PyTorchModelHubMixin):
     @torch.no_grad()
     def select_action(self, batch: dict[str, Tensor]):
         """Select a single action given environment observations."""
-        assert "observation.image" in batch
-        assert "observation.state" in batch
-
         batch = self.normalize_inputs(batch)
+        batch["observation.image"] = batch[self.input_image_key]
 
         self._queues = populate_queues(self._queues, batch)
 
@@ -303,13 +316,11 @@ class TDMPCPolicy(nn.Module, PyTorchModelHubMixin):
         device = get_device_from_parameters(self)
 
         batch = self.normalize_inputs(batch)
+        batch["observation.image"] = batch[self.input_image_key]
         batch = self.normalize_targets(batch)
 
         info = {}
 
-        # TODO(alexander-soare): Refactor TDMPC and make it comply with the policy interface documentation.
-        batch_size = batch["index"].shape[0]
-
         # (b, t) -> (t, b)
         for key in batch:
             if batch[key].ndim > 1:
@@ -337,6 +348,7 @@ class TDMPCPolicy(nn.Module, PyTorchModelHubMixin):
         # Run latent rollout using the latent dynamics model and policy model.
         # Note this has shape `horizon+1` because there are `horizon` actions and a current `z`. Each action
         # gives us a next `z`.
+        batch_size = batch["index"].shape[0]
         z_preds = torch.empty(horizon + 1, batch_size, self.config.latent_dim, device=device)
         z_preds[0] = self.model.encode(current_observation)
         reward_preds = torch.empty_like(reward, device=device)

lerobot/common/policies/utils.py

@@ -1,9 +1,28 @@
+#!/usr/bin/env python
+
+# 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.
 import torch
 from torch import nn
 
 
 def populate_queues(queues, batch):
     for key in batch:
+        # Ignore keys not in the queues already (leaving the responsibility to the caller to make sure the
+        # queues have the keys they want).
+        if key not in queues:
+            continue
         if len(queues[key]) != queues[key].maxlen:
             # initialize by copying the first observation several times until the queue is full
             while len(queues[key]) != queues[key].maxlen:

lerobot/common/utils/import_utils.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 import importlib
 import logging
 

lerobot/common/utils/io_utils.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 import warnings
 
 import imageio

lerobot/common/utils/utils.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 import logging
 import os.path as osp
 import random

lerobot/scripts/display_sys_info.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 import platform
 
 import huggingface_hub

lerobot/scripts/eval.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 """Evaluate a policy on an environment by running rollouts and computing metrics.
 
 Usage examples:

lerobot/scripts/push_dataset_to_hub.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 """
 Use this script to convert your dataset into LeRobot dataset format and upload it to the Hugging Face hub,
 or store it locally. LeRobot dataset format is lightweight, fast to load from, and does not require any

lerobot/scripts/train.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 import logging
 import time
 from copy import deepcopy
@@ -8,6 +23,7 @@ import hydra
 import torch
 from datasets import concatenate_datasets
 from datasets.utils import disable_progress_bars, enable_progress_bars
+from omegaconf import DictConfig
 
 from lerobot.common.datasets.factory import make_dataset
 from lerobot.common.datasets.utils import cycle
@@ -292,7 +308,7 @@ def add_episodes_inplace(
     sampler.num_samples = len(concat_dataset)
 
 
-def train(cfg: dict, out_dir=None, job_name=None):
+def train(cfg: DictConfig, out_dir: str | None = None, job_name: str | None = None):
     if out_dir is None:
         raise NotImplementedError()
     if job_name is None:

lerobot/scripts/visualize_dataset.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 """ Visualize data of **all** frames of any episode of a dataset of type LeRobotDataset.
 
 Note: The last frame of the episode doesnt always correspond to a final state.

poetry.lock

@@ -4,7 +4,7 @@
 name = "absl-py"
 version = "2.1.0"
 description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py."
-optional = false
+optional = true
 python-versions = ">=3.7"
 files = [
     {file = "absl-py-2.1.0.tar.gz", hash = "sha256:7820790efbb316739cde8b4e19357243fc3608a152024288513dd968d7d959ff"},
@@ -845,16 +845,6 @@ files = [
 [package.dependencies]
 six = ">=1.4.0"
 
-[[package]]
-name = "egl-probe"
-version = "1.0.2"
-description = ""
-optional = false
-python-versions = "*"
-files = [
-    {file = "egl_probe-1.0.2.tar.gz", hash = "sha256:29bdca7b08da1e060cfb42cd46af8300a7ac4f3b1b2eeb16e545ea16d9a5ac93"},
-]
-
 [[package]]
 name = "einops"
 version = "0.8.0"
@@ -1180,64 +1170,6 @@ files = [
 [package.extras]
 preview = ["glfw-preview"]
 
-[[package]]
-name = "grpcio"
-version = "1.63.0"
-description = "HTTP/2-based RPC framework"
-optional = false
-python-versions = ">=3.8"
-files = [
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-]
-
-[package.extras]
-protobuf = ["grpcio-tools (>=1.63.0)"]
-
 [[package]]
 name = "gym-aloha"
 version = "0.1.1"
@@ -1996,21 +1928,6 @@ html5 = ["html5lib"]
 htmlsoup = ["BeautifulSoup4"]
 source = ["Cython (>=3.0.10)"]
 
-[[package]]
-name = "markdown"
-version = "3.6"
-description = "Python implementation of John Gruber's Markdown."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "Markdown-3.6-py3-none-any.whl", hash = "sha256:48f276f4d8cfb8ce6527c8f79e2ee29708508bf4d40aa410fbc3b4ee832c850f"},
-    {file = "Markdown-3.6.tar.gz", hash = "sha256:ed4f41f6daecbeeb96e576ce414c41d2d876daa9a16cb35fa8ed8c2ddfad0224"},
-]
-
-[package.extras]
-docs = ["mdx-gh-links (>=0.2)", "mkdocs (>=1.5)", "mkdocs-gen-files", "mkdocs-literate-nav", "mkdocs-nature (>=0.6)", "mkdocs-section-index", "mkdocstrings[python]"]
-testing = ["coverage", "pyyaml"]
-
 [[package]]
 name = "markupsafe"
 version = "2.1.5"
@@ -3547,30 +3464,6 @@ typing-extensions = ">=4.5"
 [package.extras]
 tests = ["pytest (==7.1.2)"]
 
-[[package]]
-name = "robomimic"
-version = "0.2.0"
-description = "robomimic: A Modular Framework for Robot Learning from Demonstration"
-optional = false
-python-versions = ">=3"
-files = [
-    {file = "robomimic-0.2.0.tar.gz", hash = "sha256:ee3bb5cf9c3e1feead6b57b43c5db738fd0a8e0c015fdf6419808af8fffdc463"},
-]
-
-[package.dependencies]
-egl_probe = ">=1.0.1"
-h5py = "*"
-imageio = "*"
-imageio-ffmpeg = "*"
-numpy = ">=1.13.3"
-psutil = "*"
-tensorboard = "*"
-tensorboardX = "*"
-termcolor = "*"
-torch = "*"
-torchvision = "*"
-tqdm = "*"
-
 [[package]]
 name = "safetensors"
 version = "0.4.3"
@@ -4106,55 +3999,6 @@ files = [
     {file = "tbb-2021.12.0-py3-none-win_amd64.whl", hash = "sha256:fc2772d850229f2f3df85f1109c4844c495a2db7433d38200959ee9265b34789"},
 ]
 
-[[package]]
-name = "tensorboard"
-version = "2.16.2"
-description = "TensorBoard lets you watch Tensors Flow"
-optional = false
-python-versions = ">=3.9"
-files = [
-    {file = "tensorboard-2.16.2-py3-none-any.whl", hash = "sha256:9f2b4e7dad86667615c0e5cd072f1ea8403fc032a299f0072d6f74855775cc45"},
-]
-
-[package.dependencies]
-absl-py = ">=0.4"
-grpcio = ">=1.48.2"
-markdown = ">=2.6.8"
-numpy = ">=1.12.0"
-protobuf = ">=3.19.6,<4.24.0 || >4.24.0"
-setuptools = ">=41.0.0"
-six = ">1.9"
-tensorboard-data-server = ">=0.7.0,<0.8.0"
-werkzeug = ">=1.0.1"
-
-[[package]]
-name = "tensorboard-data-server"
-version = "0.7.2"
-description = "Fast data loading for TensorBoard"
-optional = false
-python-versions = ">=3.7"
-files = [
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-]
-
-[[package]]
-name = "tensorboardx"
-version = "2.6.2.2"
-description = "TensorBoardX lets you watch Tensors Flow without Tensorflow"
-optional = false
-python-versions = "*"
-files = [
-    {file = "tensorboardX-2.6.2.2-py2.py3-none-any.whl", hash = "sha256:160025acbf759ede23fd3526ae9d9bfbfd8b68eb16c38a010ebe326dc6395db8"},
-    {file = "tensorboardX-2.6.2.2.tar.gz", hash = "sha256:c6476d7cd0d529b0b72f4acadb1269f9ed8b22f441e87a84f2a3b940bb87b666"},
-]
-
-[package.dependencies]
-numpy = "*"
-packaging = "*"
-protobuf = ">=3.20"
-
 [[package]]
 name = "termcolor"
 version = "2.4.0"
@@ -4432,23 +4276,6 @@ perf = ["orjson"]
 reports = ["pydantic (>=2.0.0)"]
 sweeps = ["sweeps (>=0.2.0)"]
 
-[[package]]
-name = "werkzeug"
-version = "3.0.3"
-description = "The comprehensive WSGI web application library."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "werkzeug-3.0.3-py3-none-any.whl", hash = "sha256:fc9645dc43e03e4d630d23143a04a7f947a9a3b5727cd535fdfe155a17cc48c8"},
-    {file = "werkzeug-3.0.3.tar.gz", hash = "sha256:097e5bfda9f0aba8da6b8545146def481d06aa7d3266e7448e2cccf67dd8bd18"},
-]
-
-[package.dependencies]
-MarkupSafe = ">=2.1.1"
-
-[package.extras]
-watchdog = ["watchdog (>=2.3)"]
-
 [[package]]
 name = "xxhash"
 version = "3.4.1"
@@ -4716,4 +4543,4 @@ xarm = ["gym-xarm"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "2467aeb96d0957c057e577eb59d090a3f002980e6ecaccd5828032c0912d84e7"
+content-hash = "32e3ed24bc4cdc6fc2880a62de2bbc8d334912cb86200ba32a8ed2cec31d2feb"

pyproject.toml

@@ -44,7 +44,6 @@ diffusers = "^0.27.2"
 torchvision = ">=0.18.0"
 h5py = ">=3.10.0"
 huggingface-hub = ">=0.21.4"
-robomimic = "0.2.0"
 gymnasium = ">=0.29.1"
 cmake = ">=3.29.0.1"
 gym-pusht = { version = ">=0.1.3", optional = true}

tests/conftest.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 from .utils import DEVICE
 
 

tests/scripts/save_dataset_to_safetensors.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 """
 This script provides a utility for saving a dataset as safetensors files for the purpose of testing backward compatibility
 when updating the data format. It uses the `PushtDataset` to create a DataLoader and saves selected frame from the

tests/scripts/save_policy_to_safetensor.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 import shutil
 from pathlib import Path
 

tests/test_available.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 import importlib
 
 import gymnasium as gym

tests/test_datasets.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 import json
 import logging
 from copy import deepcopy

tests/test_envs.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 import importlib
 
 import gymnasium as gym

tests/test_examples.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 # TODO(aliberts): Mute logging for these tests
 import subprocess
 import sys

tests/test_policies.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 import inspect
 from pathlib import Path
 
@@ -49,6 +64,14 @@ def test_get_policy_and_config_classes(policy_name: str):
             "act",
             ["env.task=AlohaTransferCube-v0", "dataset_repo_id=lerobot/aloha_sim_transfer_cube_scripted"],
         ),
+        # Note: these parameters also need custom logic in the test function for overriding the Hydra config.
+        (
+            "aloha",
+            "diffusion",
+            ["env.task=AlohaInsertion-v0", "dataset_repo_id=lerobot/aloha_sim_insertion_human"],
+        ),
+        # Note: these parameters also need custom logic in the test function for overriding the Hydra config.
+        ("pusht", "act", ["env.task=PushT-v0", "dataset_repo_id=lerobot/pusht"]),
     ],
 )
 @require_env
@@ -72,6 +95,31 @@ def test_policy(env_name, policy_name, extra_overrides):
         + extra_overrides,
     )
 
+    # Additional config override logic.
+    if env_name == "aloha" and policy_name == "diffusion":
+        for keys in [
+            ("training", "delta_timestamps"),
+            ("policy", "input_shapes"),
+            ("policy", "input_normalization_modes"),
+        ]:
+            dct = dict(cfg[keys[0]][keys[1]])
+            dct["observation.images.top"] = dct["observation.image"]
+            del dct["observation.image"]
+            cfg[keys[0]][keys[1]] = dct
+        cfg.override_dataset_stats = None
+
+    # Additional config override logic.
+    if env_name == "pusht" and policy_name == "act":
+        for keys in [
+            ("policy", "input_shapes"),
+            ("policy", "input_normalization_modes"),
+        ]:
+            dct = dict(cfg[keys[0]][keys[1]])
+            dct["observation.image"] = dct["observation.images.top"]
+            del dct["observation.images.top"]
+            cfg[keys[0]][keys[1]] = dct
+        cfg.override_dataset_stats = None
+
     # Check that we can make the policy object.
     dataset = make_dataset(cfg)
     policy = make_policy(hydra_cfg=cfg, dataset_stats=dataset.stats)

tests/test_visualize_dataset.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 import pytest
 
 from lerobot.scripts.visualize_dataset import visualize_dataset

tests/utils.py

@@ -1,3 +1,18 @@
+#!/usr/bin/env python
+
+# 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.
 import platform
 from functools import wraps