Merge remote-tracking branch 'upstream/main'

lerobot/common/policies/act/configuration_act.py

@@ -66,8 +66,12 @@ class ACTConfig:
             documentation in the policy class).
         latent_dim: The VAE's latent dimension.
         n_vae_encoder_layers: The number of transformer layers to use for the VAE's encoder.
-        use_temporal_aggregation: Whether to blend the actions of multiple policy invocations for any given
+        temporal_ensemble_momentum: Exponential moving average (EMA) momentum parameter (α) for ensembling
-            environment step.
+            actions for a given time step over multiple policy invocations. Updates are calculated as:
+            x⁻ₙ = αx⁻ₙ₋₁ + (1-α)xₙ. Note that the ACT paper and original ACT code describes a different
+            parameter here: they refer to a weighting scheme wᵢ = exp(-m⋅i) and set m = 0.01. With our
+            formulation, this is equivalent to α = exp(-0.01) ≈ 0.99. When this parameter is provided, we
+            require `n_action_steps == 1` (since we need to query the policy every step anyway).
         dropout: Dropout to use in the transformer layers (see code for details).
         kl_weight: The weight to use for the KL-divergence component of the loss if the variational objective
             is enabled. Loss is then calculated as: `reconstruction_loss + kl_weight * kld_loss`.
@@ -115,6 +119,9 @@ class ACTConfig:
     dim_feedforward: int = 3200
     feedforward_activation: str = "relu"
     n_encoder_layers: int = 4
+    # Note: Although the original ACT implementation has 7 for `n_decoder_layers`, there is a bug in the code
+    # that means only the first layer is used. Here we match the original implementation by setting this to 1.
+    # See this issue https://github.com/tonyzhaozh/act/issues/25#issue-2258740521.
     n_decoder_layers: int = 1
     # VAE.
     use_vae: bool = True
@@ -122,7 +129,7 @@ class ACTConfig:
     n_vae_encoder_layers: int = 4
 
     # Inference.
-    use_temporal_aggregation: bool = False
+    temporal_ensemble_momentum: float | None = None
 
     # Training and loss computation.
     dropout: float = 0.1
@@ -134,8 +141,11 @@ class ACTConfig:
             raise ValueError(
                 f"`vision_backbone` must be one of the ResNet variants. Got {self.vision_backbone}."
             )
-        if self.use_temporal_aggregation:
+        if self.temporal_ensemble_momentum is not None and self.n_action_steps > 1:
-            raise NotImplementedError("Temporal aggregation is not yet implemented.")
+            raise NotImplementedError(
+                "`n_action_steps` must be 1 when using temporal ensembling. This is "
+                "because the policy needs to be queried every step to compute the ensembled action."
+            )
         if self.n_action_steps > self.chunk_size:
             raise ValueError(
                 f"The chunk size is the upper bound for the number of action steps per model invocation. Got "

lerobot/common/policies/act/modeling_act.py

@@ -61,7 +61,7 @@ class ACTPolicy(nn.Module, PyTorchModelHubMixin):
         super().__init__()
         if config is None:
             config = ACTConfig()
-        self.config = config
+        self.config: ACTConfig = config
 
         self.normalize_inputs = Normalize(
             config.input_shapes, config.input_normalization_modes, dataset_stats
@@ -81,7 +81,9 @@ class ACTPolicy(nn.Module, PyTorchModelHubMixin):
 
     def reset(self):
         """This should be called whenever the environment is reset."""
-        if self.config.n_action_steps is not None:
+        if self.config.temporal_ensemble_momentum is not None:
+            self._ensembled_actions = None
+        else:
             self._action_queue = deque([], maxlen=self.config.n_action_steps)
 
     @torch.no_grad
@@ -97,14 +99,36 @@ class ACTPolicy(nn.Module, PyTorchModelHubMixin):
         batch = self.normalize_inputs(batch)
         batch["observation.images"] = torch.stack([batch[k] for k in self.expected_image_keys], dim=-4)
 
+        # If we are doing temporal ensembling, keep track of the exponential moving average (EMA), and return
+        # the first action.
+        if self.config.temporal_ensemble_momentum is not None:
+            actions = self.model(batch)[0]  # (batch_size, chunk_size, action_dim)
+            actions = self.unnormalize_outputs({"action": actions})["action"]
+            if self._ensembled_actions is None:
+                # Initializes `self._ensembled_action` to the sequence of actions predicted during the first
+                # time step of the episode.
+                self._ensembled_actions = actions.clone()
+            else:
+                # self._ensembled_actions will have shape (batch_size, chunk_size - 1, action_dim). Compute
+                # the EMA update for those entries.
+                alpha = self.config.temporal_ensemble_momentum
+                self._ensembled_actions = alpha * self._ensembled_actions + (1 - alpha) * actions[:, :-1]
+                # The last action, which has no prior moving average, needs to get concatenated onto the end.
+                self._ensembled_actions = torch.cat([self._ensembled_actions, actions[:, -1:]], dim=1)
+            # "Consume" the first action.
+            action, self._ensembled_actions = self._ensembled_actions[:, 0], self._ensembled_actions[:, 1:]
+            return action
+
+        # Action queue logic for n_action_steps > 1. When the action_queue is depleted, populate it by
+        # querying the policy.
         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()
 

lerobot/common/policies/diffusion/modeling_diffusion.py

@@ -17,7 +17,6 @@
 """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.
 """
@@ -27,13 +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
@@ -312,6 +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.
 

lerobot/configs/policy/act.yaml

@@ -66,6 +66,9 @@ policy:
   dim_feedforward: 3200
   feedforward_activation: relu
   n_encoder_layers: 4
+  # Note: Although the original ACT implementation has 7 for `n_decoder_layers`, there is a bug in the code
+  # that means only the first layer is used. Here we match the original implementation by setting this to 1.
+  # See this issue https://github.com/tonyzhaozh/act/issues/25#issue-2258740521.
   n_decoder_layers: 1
   # VAE.
   use_vae: true
@@ -73,7 +76,7 @@ policy:
   n_vae_encoder_layers: 4
 
   # Inference.
-  use_temporal_aggregation: false
+  temporal_ensemble_momentum: null
 
   # Training and loss computation.
   dropout: 0.1

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"},
@@ -767,16 +767,6 @@ 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"
@@ -1037,64 +1027,6 @@ 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"
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-]
-
-[package.extras]
-protobuf = ["grpcio-tools (>=1.63.0)"]
-
 [[package]]
 name = "gym-aloha"
 version = "0.1.1"
@@ -1668,7 +1600,6 @@ files = [
     {file = "lxml-5.2.1-cp37-cp37m-musllinux_1_2_x86_64.whl", hash = "sha256:9e2addd2d1866fe112bc6f80117bcc6bc25191c5ed1bfbcf9f1386a884252ae8"},
     {file = "lxml-5.2.1-cp37-cp37m-win32.whl", hash = "sha256:f51969bac61441fd31f028d7b3b45962f3ecebf691a510495e5d2cd8c8092dbd"},
     {file = "lxml-5.2.1-cp37-cp37m-win_amd64.whl", hash = "sha256:b0b58fbfa1bf7367dde8a557994e3b1637294be6cf2169810375caf8571a085c"},
-    {file = "lxml-5.2.1-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:3e183c6e3298a2ed5af9d7a356ea823bccaab4ec2349dc9ed83999fd289d14d5"},
     {file = "lxml-5.2.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:804f74efe22b6a227306dd890eecc4f8c59ff25ca35f1f14e7482bbce96ef10b"},
     {file = "lxml-5.2.1-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:08802f0c56ed150cc6885ae0788a321b73505d2263ee56dad84d200cab11c07a"},
     {file = "lxml-5.2.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0f8c09ed18ecb4ebf23e02b8e7a22a05d6411911e6fabef3a36e4f371f4f2585"},
@@ -1740,21 +1671,6 @@ 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"
@@ -3056,6 +2972,7 @@ files = [
     {file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"},
     {file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"},
     {file = "PyYAML-6.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40df9b996c2b73138957fe23a16a4f0ba614f4c0efce1e9406a184b6d07fa3a9"},
+    {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a08c6f0fe150303c1c6b71ebcd7213c2858041a7e01975da3a99aed1e7a378ef"},
     {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c22bec3fbe2524cde73d7ada88f6566758a8f7227bfbf93a408a9d86bcc12a0"},
     {file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"},
     {file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"},
@@ -3224,30 +3141,6 @@ 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"
@@ -3738,55 +3631,6 @@ 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 = [
-    {file = "tensorboard_data_server-0.7.2-py3-none-any.whl", hash = "sha256:7e0610d205889588983836ec05dc098e80f97b7e7bbff7e994ebb78f578d0ddb"},
-    {file = "tensorboard_data_server-0.7.2-py3-none-macosx_10_9_x86_64.whl", hash = "sha256:9fe5d24221b29625dbc7328b0436ca7fc1c23de4acf4d272f1180856e32f9f60"},
-    {file = "tensorboard_data_server-0.7.2-py3-none-manylinux_2_31_x86_64.whl", hash = "sha256:ef687163c24185ae9754ed5650eb5bc4d84ff257aabdc33f0cc6f74d8ba54530"},
-]
-
-[[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"
@@ -4064,23 +3908,6 @@ 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"
@@ -4348,4 +4175,4 @@ xarm = ["gym-xarm"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "2f0d2cbf4a2dec546e25b29b9b108ff1f97b4c278b718360b3f7f6a2bf9dcef8"
+content-hash = "e3e3c306a5519e4f716a1ac086ad9b734efedcac077a0ec71e5bc16349a1e559"

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}