run precommit and fix issues

Merge branch 'main' into feature/basic-peft-support
feat: support wallx model (#2593 )
2026-05-12 07:09:43 +00:00 · 2025-12-22 14:50:58 +01:00 · 2025-12-22 14:29:51 +01:00 · 2025-12-22 10:12:39 +01:00 · 2025-12-22 10:04:33 +01:00 · 2025-12-20 19:20:12 +01:00
113 changed files with 15243 additions and 692 deletions
@@ -12,57 +12,83 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

-name: "\U0001F41B Bug Report"
-description: Submit a bug report to help us improve LeRobot
+name: "🚀 Issue / Bug / Request"
+description: Report a bug, suggest an improvement, or ask a technical question.
 body:
  - type: markdown
    attributes:
      value: |
-        Thanks for taking the time to submit a bug report! 🐛
-        If this is not a bug related to the LeRobot library directly, but instead a general question about your code or the library specifically please use our [discord](https://discord.gg/s3KuuzsPFb).
+        ### Thanks for contributing to LeRobot! 🙌
+        Please choose the most relevant sections below. If this is a general "how-to" question, consider our [Discord](https://discord.gg/s3KuuzsPFb) for faster community support.
+
+  - type: dropdown
+    id: issue-type
+    attributes:
+      label: Ticket Type
+      description: What kind of ticket are you opening?
+      options:
+        - "🐛 Bug Report (Something isn't working)"
+        - "💡 Feature Request / Improvement"
+        - "❓ Technical Question"
+        - "🧹 Maintenance / Documentation"
+    validations:
+      required: true

  - type: textarea
    id: system-info
    attributes:
-      label: System Info
-      description: Please share your LeRobot configuration by running `lerobot-info` (if installed) or `python -m lerobot.scripts.display_sys_info` (if not installed) and pasting the output below.
+      label: Environment & System Info
+      description: |
+        For bugs or technical questions, please run `lerobot-info` and paste the output.
+        (Optional for feature requests).
      render: Shell
-      placeholder: lerobot version, OS, python version, numpy version, torch version, and lerobot's configuration
+      placeholder: lerobot version, OS, python version, etc.
+
+  - type: textarea
+    id: description
    validations:
      required: true
+    attributes:
+      label: Description
+      description: |
+        Provide a clear summary of the issue or your proposal.
+        - **Bugs:** What is happening?
+        - **Features:** What is the goal/use case?
+        - **Questions:** What are you trying to achieve?
+      placeholder: |
+        A clear and concise description of the issue or suggestion.
+
+  - type: textarea
+    id: context-repro
+    attributes:
+      label: Context & Reproduction
+      description: |
+        Provide a code snippet, steps to reproduce a bug, or technical details about your proposal.
+        Please use code blocks for scripts and CLI commands.
+      placeholder: |
+        Steps to reproduce / Usage example:
+        1.
+        2.
+        3.
+
+  - type: textarea
+    id: logs
+    attributes:
+      label: Relevant logs or stack trace
+      description: If applicable, paste relevant error logs here.
+      render: Shell

  - type: checkboxes
-    id: information-scripts-examples
+    id: extras
    attributes:
-      label: Information
-      description: 'The problem arises when using:'
+      label: Checklist
      options:
-        - label: "One of the scripts in the examples/ folder of LeRobot"
-        - label: "My own task or dataset (give details below)"
+        - label: I have searched existing tickets to ensure this isn't a duplicate.
+        - label: I am using the latest version of the `main` branch.
+        - label: I have verified this is not an environment-specific problem.

  - type: textarea
-    id: reproduction
-    validations:
-      required: true
+    id: workaround
    attributes:
-      label: Reproduction
-      description: |
-        If needed, provide a simple code sample that reproduces the problem you ran into. It can be a Colab link or just a code snippet.
-        Sharing error messages or stack traces could be useful as well!
-        Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
-        Try to avoid screenshots, as they are hard to read and don't allow copy-and-pasting.
-
-      placeholder: |
-        Steps to reproduce the behavior:
-
-          1.
-          2.
-          3.
-
-  - type: textarea
-    id: expected-behavior
-    validations:
-      required: true
-    attributes:
-      label: Expected behavior
-      description: "A clear and concise description of what you would expect to happen."
+      label: Additional Info / Workarounds
+      description: Anything else we should know? If you have a workaround, please share it!
@@ -1,41 +1,54 @@
-## What this does
+## Title

-Explain what this PR does. Feel free to tag your PR with the appropriate label(s).
+Short, imperative summary (e.g., "fix(robots): handle None in sensor parser"). See [CONTRIBUTING.md](../CONTRIBUTING.md) for PR conventions.

-Examples:
-| Title | Label |
-|----------------------|-----------------|
-| Fixes #[issue] | (🐛 Bug) |
-| Adds new dataset | (🗃️ Dataset) |
-| Optimizes something | (⚡️ Performance) |
+## Type / Scope

-## How it was tested
+- **Type**: (Bug | Feature | Docs | Performance | Test | CI | Chore)
+- **Scope**: (optional — name of module or package affected)

-Explain/show how you tested your changes.
+## Summary / Motivation

-Examples:
+- One-paragraph description of what changes and why.
+- Why this change is needed and any trade-offs or design notes.

- Added `test_something` in `tests/test_stuff.py`.
- Added `new_feature` and checked that training converges with policy X on dataset/environment Y.
- Optimized `some_function`, it now runs X times faster than previously.
+## Related issues

-## How to checkout & try? (for the reviewer)
+- Fixes / Closes: # (if any)
+- Related: # (if any)

-Provide a simple way for the reviewer to try out your changes.
+## What changed

-Examples:
+- Short, concrete bullets of the modifications (files/behaviour).
+- Short note if this introduces breaking changes and migration steps.

-```bash
-pytest -sx tests/test_stuff.py::test_something
-```
+## How was this tested

-```bash
-lerobot-train --some.option=true
-```
+- Tests added: list new tests or test files.
+- Manual checks / dataset runs performed.

-## SECTION TO REMOVE BEFORE SUBMITTING YOUR PR
+## How to run locally (reviewer)

-**Note**: Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
-members/contributors who may be interested in your PR. Try to avoid tagging more than 3 people.
+- Run the relevant tests:

-**Note**: Before submitting this PR, please read the [contributor guideline](https://github.com/huggingface/lerobot/blob/main/CONTRIBUTING.md#submitting-a-pull-request-pr).
+  ```bash
+  pytest -q tests/ -k <keyword>
+  ```
+
+- Run a quick example or CLI (if applicable):
+
+  ```bash
+  lerobot-train --some.option=true
+  ```
+
+## Checklist (required before merge)
+
+- [ ] Linting/formatting run (`pre-commit run -a`)
+- [ ] All tests pass locally (`pytest`)
+- [ ] Documentation updated
+- [ ] CI is green
+
+## Reviewer notes
+
+- Anything the reviewer should focus on (performance, edge-cases, specific files) or general notes.
+- Anyone in the community is free to review the PR.
@@ -0,0 +1,69 @@
+# 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.
+
+CI:
+  - changed-files:
+      - any-glob-to-any-file:
+        - '.github/**'
+        - 'docker/**'
+
+github_actions:
+  - changed-files:
+      - any-glob-to-any-file: '.github/**'
+
+documentation:
+  - changed-files:
+      - any-glob-to-any-file:
+          - '**/*.md'
+          - '**/*.mdx'
+          - 'docs/**'
+
+examples:
+  - changed-files:
+      - any-glob-to-any-file: 'examples/**'
+
+tests:
+  - changed-files:
+      - any-glob-to-any-file: 'tests/**'
+
+sensors:
+  - changed-files:
+      - any-glob-to-any-file: 'src/lerobot/cameras/**'
+
+configuration:
+  - changed-files:
+      - any-glob-to-any-file: 'src/lerobot/configs/**'
+
+dataset:
+  - changed-files:
+      - any-glob-to-any-file: 'src/lerobot/datasets/**'
+
+evaluation:
+  - changed-files:
+      - any-glob-to-any-file: 'src/lerobot/envs/**'
+
+robots:
+  - changed-files:
+      - any-glob-to-any-file:
+          - 'src/lerobot/teleoperators/**'
+          - 'src/lerobot/robots/**'
+          - 'src/lerobot/motors/**'
+
+policies:
+  - changed-files:
+      - any-glob-to-any-file: 'src/lerobot/policies/**'
+
+processor:
+  - changed-files:
+      - any-glob-to-any-file: 'src/lerobot/processor/**'
@@ -31,7 +31,8 @@ jobs:
    name: Upload Preview and Comment
    if: >
      github.event.workflow_run.event == 'pull_request' &&
-      github.event.workflow_run.conclusion == 'success'
+      github.event.workflow_run.conclusion == 'success' &&
+      github.repository == 'huggingface/lerobot'
    uses: huggingface/doc-builder/.github/workflows/upload_pr_documentation.yml@main
    with:
      package_name: lerobot
@@ -42,7 +42,9 @@ jobs:
  # This job builds and deploys the official documentation.
  build_main_docs:
    name: Build Main Docs
-    if: github.event_name == 'push' || github.event_name == 'workflow_dispatch'
+    if: >
+      (github.event_name == 'push' || github.event_name == 'workflow_dispatch') &&
+      github.repository == 'huggingface/lerobot'
    permissions:
      contents: read
    uses: huggingface/doc-builder/.github/workflows/build_main_documentation.yml@main
@@ -58,7 +60,7 @@ jobs:
  # The result of this job triggers the 'Upload PR Documentation' workflow.
  build_pr_docs:
    name: Build PR Docs
-    if: github.event_name == 'pull_request'
+    if: github.event_name == 'pull_request' && github.repository == 'huggingface/lerobot'
    permissions:
      contents: read
      pull-requests: write
@@ -45,7 +45,6 @@ permissions:
 env:
  UV_VERSION: "0.8.0"
  PYTHON_VERSION: "3.10"
-  DOCKER_IMAGE_NAME: huggingface/lerobot-gpu

 # Ensures that only the latest commit for a PR or branch is built, canceling older runs.
 concurrency:
@@ -85,7 +85,7 @@ jobs:
          python-version: ${{ env.PYTHON_VERSION }}

      - name: Install lerobot with all extras
-        run: uv sync --all-extras --no-extra groot # TODO(Steven): Make flash-attn optional
+        run: uv sync --all-extras --no-extra groot --no-extra wallx # TODO(Steven): Make flash-attn optional

      - name: Run pytest (all extras)
        run: uv run pytest tests -vv --maxfail=10
@@ -0,0 +1,89 @@
+# 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.
+
+# This workflow automatically labels issues based on their content.
+name: Issue Labeler
+on:
+  # Trigger on new issues and edits to existing issues
+  issues:
+    types: [opened, edited]
+
+permissions:
+  contents: read
+  issues: write
+
+jobs:
+  label-issue:
+    name: Auto Label Issue
+    runs-on: ubuntu-latest
+    if: github.repository == 'huggingface/lerobot'
+    steps:
+      - uses: actions/github-script@v8
+        with:
+          script: |
+            // Setup Input Text
+            const body = (context.payload.issue.body || '');
+            const title = (context.payload.issue.title || '');
+            const cleanBody = body.replace(/```[\s\S]*?```/g, '');
+            const text = `${title}\n${cleanBody}`.toLowerCase();
+            const labelsToAdd = new Set();
+            const matches = (re) => re.test(text);
+
+            // Keyword Heuristics
+
+            // Domain Specific
+            if (matches(/\b(bug|error|issue|fault|crash|exception)\b/i)) labelsToAdd.add('bug');
+            if (matches(/\b(feature|enhancement|improvement|support|implement|proposal)\b/i)) labelsToAdd.add('enhancement');
+            if (matches(/\b(question|help|how to||clarify|explain|unclear)\b/i)) labelsToAdd.add('question');
+            if (matches(/\b(maintenance|documentation|docs|readme|tutorial|guide|wiki)\b/i)) labelsToAdd.add('documentation');
+            if (matches(/\b(example|script|sample|demo|notebook)s?\b/i)) labelsToAdd.add('examples');
+            if (matches(/\b(datasets?|data loader|data augmentation|data preprocessing)\b/i)) labelsToAdd.add('dataset');
+            if (matches(/\b(mujoco|isaac|simulation|sim)\b/i)) labelsToAdd.add('simulation');
+            if (matches(/\b(train|training|loss|optimizer|backward|gradient|wandb|sac)\b/i)) labelsToAdd.add('training');
+            if (matches(/\b(rerun|plot|video|render|visualiz|gif)/i)) labelsToAdd.add('visualization');
+            if (matches(/\b(camera|realsense|lidar|depth|sensor|imu|microphone|rgbd)\b/i)) labelsToAdd.add('sensors');
+            if (matches(/\b(aloha|koch|so-100|so100|mobile|teleop|manipulator|robots?)\b/i)) labelsToAdd.add('robots');
+            if (matches(/\b(teleop|teleoperator|controller|leader|follower|joystick|gamepad)\b/i)) labelsToAdd.add('teleoperators');
+            if (matches(/\b(policy|policies|p0licy)\b/i)) labelsToAdd.add('policies');
+            if (matches(/\b(processors?|pipeline)\b/i)) labelsToAdd.add('processor');
+            if (matches(/\b(eval|evaluate|evaluation|metrics?|score|benchmark)\b/i)) labelsToAdd.add('evaluation');
+
+            // Infrastructure & Code Quality
+            if (matches(/\b(tests?|pytest|unittest|failing test)\b/i)) labelsToAdd.add('tests');
+            if (matches(/\b(ci|github actions|workflow|gha|actions?|pipeline)\b/i)) {
+              labelsToAdd.add('CI');
+              labelsToAdd.add('github_actions');
+            }
+            if (matches(/\b(perf|latency|throughput|fps|speed|performance)\b/i)) labelsToAdd.add('performance');
+            if (matches(/\b(dependency|requirements|pip|conda|install error|importerror|package not found)\b/i)) labelsToAdd.add('dependencies');
+            if (matches(/\b(python|pyproject|requirements(\.txt)?|pip install|typing error)\b/i)) labelsToAdd.add('python');
+
+            // Documentation & Meta
+            if (matches(/\b(doc|documentation|docs|readme|typo|how to)\b/i)) labelsToAdd.add('documentation');
+            if (matches(/\b(refactor|cleanup|restructure|rename|modernize code)\b/i)) labelsToAdd.add('refactor');
+            if (matches(/\b(release|changelog|version bump|cut a release|tag v)\b/i)) labelsToAdd.add('release');
+            if (matches(/\b(breaking change|major change)\b/i)) labelsToAdd.add('breaking change');
+
+            // Apply Labels
+            const labels = Array.from(labelsToAdd).filter(Boolean);
+
+            if (labels.length > 0) {
+              console.log(`Adding labels: ${labels.join(', ')}`);
+              await github.rest.issues.addLabels({
+                owner: context.repo.owner,
+                repo: context.repo.repo,
+                issue_number: context.issue.number,
+                labels,
+              });
+            }
@@ -43,6 +43,7 @@ jobs:
    name: Build CPU Docker for Nightly
    runs-on:
      group: aws-general-8-plus
+    if: github.repository == 'huggingface/lerobot'
    outputs:
      image_tag: ${{ env.DOCKER_IMAGE_NAME_CPU }}
    steps:
@@ -77,6 +78,7 @@ jobs:
    name: Build GPU Docker for Nightly
    runs-on:
      group: aws-general-8-plus
+    if: github.repository == 'huggingface/lerobot'
    outputs:
      image_tag: ${{ env.DOCKER_IMAGE_NAME_GPU }}
    steps:
@@ -0,0 +1,39 @@
+# 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.
+
+# This workflow labels pull requests based on the files that were changed.
+name: Pull Request Labeler
+
+on:
+  # Allows labeling pull requests when they are opened or updated
+  # zizmor: ignore[dangerous-triggers] Needed to label PRs from forks
+  pull_request_target:
+    branches:
+      - main
+    types: [opened, synchronize, reopened, ready_for_review]
+
+permissions:
+  contents: read
+  pull-requests: write
+
+jobs:
+  triage:
+    name: Label PR
+    runs-on: ubuntu-latest
+    if: github.repository == 'huggingface/lerobot' && !github.event.pull_request.draft
+    steps:
+      - uses: actions/labeler@v6
+        with:
+          repo-token: ${{ secrets.GITHUB_TOKEN }}
+          sync-labels: true # Removes labels if files are removed from the PR
@@ -29,6 +29,7 @@ jobs:
  build-and-publish:
    name: Build and publish Python distributions
    runs-on: ubuntu-latest
+    if: github.repository == 'huggingface/lerobot'
    outputs:
      version: ${{ steps.extract_info.outputs.tag_version }}
    permissions:
@@ -45,6 +45,7 @@ jobs:
  stale:
    name: Close Stale Issues and PRs
    runs-on: ubuntu-latest
+    if: github.repository == 'huggingface/lerobot'
    permissions:
      actions: write
      contents: write # only for delete-branch option
@@ -43,6 +43,7 @@ jobs:
  full-tests:
    name: Full Unbound Tests
    runs-on: ubuntu-latest
+    if: github.repository == 'huggingface/lerobot'
    env:
      MUJOCO_GL: egl
      HF_HOME: /mnt/cache/.cache/huggingface
@@ -77,7 +78,7 @@ jobs:
          echo "Dependencies unbound:" && cat pyproject.toml

      - name: Install lerobot with all extras
-        run: uv sync --all-extras --no-extra groot # TODO(Steven): Make flash-attn optional
+        run: uv sync --all-extras --no-extra groot --no-extra wallx # TODO(Steven): Make flash-attn optional

      - name: Run pytest (all extras)
        run: uv run pytest tests -vv
@@ -87,7 +87,7 @@ repos:
  # TODO(Steven): Uncomment when ready to use
  ##### Static Analysis & Typing #####
  - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.18.2
+    rev: v1.19.1
    hooks:
      - id: mypy
        args: [--config-file=pyproject.toml]
@@ -52,7 +52,7 @@ decisions when appropriate.

 This Code of Conduct applies within all community spaces, and also applies when
 an individual is officially representing the community in public spaces.
-Examples of representing our community include using an official email address,
+Examples of representing our community include using an official e-mail address,
 posting via an official social media account, or acting as an appointed
 representative at an online or offline event.

@@ -60,7 +60,7 @@ representative at an online or offline event.

 Instances of abusive, harassing, or otherwise unacceptable behavior may be
 reported to the community leaders responsible for enforcement at
-[feedback@huggingface.co](mailto:feedback@huggingface.co).
+feedback@huggingface.co.
 All complaints will be reviewed and investigated promptly and fairly.

 All community leaders are obligated to respect the privacy and security of the
@@ -1,7 +1,5 @@
 <p align="center">
-  <img alt="LeRobot, Hugging Face Robotics Library" src="https://raw.githubusercontent.com/huggingface/lerobot/main/media/lerobot-logo-thumbnail.png" width="100%">
-  <br/>
-  <br/>
+  <img alt="LeRobot, Hugging Face Robotics Library" src="./media/readme/lerobot-logo-thumbnail.png" width="100%">
 </p>

 <div align="center">
@@ -12,323 +10,130 @@
 [![Status](https://img.shields.io/pypi/status/lerobot)](https://pypi.org/project/lerobot/)
 [![Version](https://img.shields.io/pypi/v/lerobot)](https://pypi.org/project/lerobot/)
 [![Contributor Covenant](https://img.shields.io/badge/Contributor%20Covenant-v2.1-ff69b4.svg)](https://github.com/huggingface/lerobot/blob/main/CODE_OF_CONDUCT.md)
-[![Discord](https://dcbadge.vercel.app/api/server/C5P34WJ68S?style=flat)](https://discord.gg/s3KuuzsPFb)
-
-<!-- [![Coverage](https://codecov.io/gh/huggingface/lerobot/branch/main/graph/badge.svg?token=TODO)](https://codecov.io/gh/huggingface/lerobot) -->

 </div>

-<h2 align="center">
-    <p><a href="https://huggingface.co/docs/lerobot/hope_jr">
-        Build Your Own HopeJR Robot!</a></p>
-</h2>
+**LeRobot** aims to provide models, datasets, and tools for real-world robotics in PyTorch. The goal is to lower the barrier to entry so that everyone can contribute to and benefit from shared datasets and pretrained models.

-<div align="center">
-  <img
-    src="https://raw.githubusercontent.com/huggingface/lerobot/main/media/hope_jr/hopejr.png"
-    alt="HopeJR robot"
-    title="HopeJR robot"
-    width="60%"
-  />
+🤗 A hardware-agnostic, Python-native interface that standardizes control across diverse platforms, from low-cost arms (SO-100) to humanoids.

-  <p><strong>Meet HopeJR – A humanoid robot arm and hand for dexterous manipulation!</strong></p>
-  <p>Control it with exoskeletons and gloves for precise hand movements.</p>
-  <p>Perfect for advanced manipulation tasks! 🤖</p>
+🤗 A standardized, scalable LeRobotDataset format (Parquet + MP4 or images) hosted on the Hugging Face Hub, enabling efficient storage, streaming and visualization of massive robotic datasets.

-  <p><a href="https://huggingface.co/docs/lerobot/hope_jr">
-      See the full HopeJR tutorial here.</a></p>
-</div>
+🤗 State-of-the-art policies that have been shown to transfer to the real-world ready for training and deployment.

-<br/>
+🤗 Comprehensive support for the open-source ecosystem to democratize physical AI.

-<h2 align="center">
-    <p><a href="https://huggingface.co/docs/lerobot/so101">
-        Build Your Own SO-101 Robot!</a></p>
-</h2>
+## Quick Start

-<div align="center">
-  <table>
-    <tr>
-      <td align="center"><img src="https://raw.githubusercontent.com/huggingface/lerobot/main/media/so101/so101.webp" alt="SO-101 follower arm" title="SO-101 follower arm" width="90%"/></td>
-      <td align="center"><img src="https://raw.githubusercontent.com/huggingface/lerobot/main/media/so101/so101-leader.webp" alt="SO-101 leader arm" title="SO-101 leader arm" width="90%"/></td>
-    </tr>
-  </table>
-
-  <p><strong>Meet the updated SO100, the SO-101 – Just €114 per arm!</strong></p>
-  <p>Train it in minutes with a few simple moves on your laptop.</p>
-  <p>Then sit back and watch your creation act autonomously! 🤯</p>
-
-  <p><a href="https://huggingface.co/docs/lerobot/so101">
-      See the full SO-101 tutorial here.</a></p>
-
-  <p>Want to take it to the next level? Make your SO-101 mobile by building LeKiwi!</p>
-  <p>Check out the <a href="https://huggingface.co/docs/lerobot/lekiwi">LeKiwi tutorial</a> and bring your robot to life on wheels.</p>
-
-  <img src="https://raw.githubusercontent.com/huggingface/lerobot/main/media/lekiwi/kiwi.webp" alt="LeKiwi mobile robot" title="LeKiwi mobile robot" width="50%">
-</div>
-
-<br/>
-
-<h3 align="center">
-    <p>LeRobot: State-of-the-art AI for real-world robotics</p>
-</h3>
-
---
-
-🤗 LeRobot aims to provide models, datasets, and tools for real-world robotics in PyTorch. The goal is to lower the barrier to entry to robotics so that everyone can contribute and benefit from sharing datasets and pretrained models.
-
-🤗 LeRobot contains state-of-the-art approaches that have been shown to transfer to the real-world with a focus on imitation learning and reinforcement learning.
-
-🤗 LeRobot already provides a set of pretrained models, datasets with human collected demonstrations, and simulation environments to get started without assembling a robot. In the coming weeks, the plan is to add more and more support for real-world robotics on the most affordable and capable robots out there.
-
-🤗 LeRobot hosts pretrained models and datasets on this Hugging Face community page: [huggingface.co/lerobot](https://huggingface.co/lerobot)
-
-#### Examples of pretrained models on simulation environments
-
-<table>
-  <tr>
-    <td><img src="https://raw.githubusercontent.com/huggingface/lerobot/main/media/gym/aloha_act.gif" width="100%" alt="ACT policy on ALOHA env"/></td>
-    <td><img src="https://raw.githubusercontent.com/huggingface/lerobot/main/media/gym/simxarm_tdmpc.gif" width="100%" alt="TDMPC policy on SimXArm env"/></td>
-    <td><img src="https://raw.githubusercontent.com/huggingface/lerobot/main/media/gym/pusht_diffusion.gif" width="100%" alt="Diffusion policy on PushT env"/></td>
-  </tr>
-  <tr>
-    <td align="center">ACT policy on ALOHA env</td>
-    <td align="center">TDMPC policy on SimXArm env</td>
-    <td align="center">Diffusion policy on PushT env</td>
-  </tr>
-</table>
-
-## Installation
-
-LeRobot works with Python 3.10+ and PyTorch 2.2+.
-
-### Environment Setup
-
-Create a virtual environment with Python 3.10 and activate it, e.g. with [`miniforge`](https://conda-forge.org/download/):
-
-```bash
-conda create -y -n lerobot python=3.10
-conda activate lerobot
-```
-
-When using `conda`, install `ffmpeg` in your environment:
-
-```bash
-conda install ffmpeg -c conda-forge
-```
-
-> **NOTE:** This usually installs `ffmpeg 7.X` for your platform compiled with the `libsvtav1` encoder. If `libsvtav1` is not supported (check supported encoders with `ffmpeg -encoders`), you can:
->
-> - _[On any platform]_ Explicitly install `ffmpeg 7.X` using:
->
-> ```bash
-> conda install ffmpeg=7.1.1 -c conda-forge
-> ```
->
-> - _[On Linux only]_ Install [ffmpeg build dependencies](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#GettheDependencies) and [compile ffmpeg from source with libsvtav1](https://trac.ffmpeg.org/wiki/CompilationGuide/Ubuntu#libsvtav1), and make sure you use the corresponding ffmpeg binary to your install with `which ffmpeg`.
-
-### Install LeRobot 🤗
-
-#### From Source
-
-First, clone the repository and navigate into the directory:
-
-```bash
-git clone https://github.com/huggingface/lerobot.git
-cd lerobot
-```
-
-Then, install the library in editable mode. This is useful if you plan to contribute to the code.
-
-```bash
-pip install -e .
-```
-
-> **NOTE:** If you encounter build errors, you may need to install additional dependencies (`cmake`, `build-essential`, and `ffmpeg libs`). On Linux, run:
-> `sudo apt-get install cmake build-essential python3-dev pkg-config libavformat-dev libavcodec-dev libavdevice-dev libavutil-dev libswscale-dev libswresample-dev libavfilter-dev`. For other systems, see: [Compiling PyAV](https://pyav.org/docs/develop/overview/installation.html#bring-your-own-ffmpeg)
-
-For simulations, 🤗 LeRobot comes with gymnasium environments that can be installed as extras:
-
- [aloha](https://github.com/huggingface/gym-aloha)
- [xarm](https://github.com/huggingface/gym-xarm)
- [pusht](https://github.com/huggingface/gym-pusht)
-
-For instance, to install 🤗 LeRobot with aloha and pusht, use:
-
-```bash
-pip install -e ".[aloha, pusht]"
-```
-
-### Installation from PyPI
-
-**Core Library:**
-Install the base package with:
+LeRobot can be installed directly from PyPI.

 ```bash
 pip install lerobot
+lerobot-info
 ```

-_This installs only the default dependencies._
+> [!IMPORTANT]
+> For detailed installation guide, please see the [Installation Documentation](https://huggingface.co/docs/lerobot/installation).

-**Extra Features:**
-To install additional functionality, use one of the following:
+## Robots & Control
+
+<div align="center">
+  <img src="./media/readme/robots_control_video.webp" width="640px" alt="Reachy 2 Demo">
+</div>
+
+LeRobot provides a unified `Robot` class interface that decouples control logic from hardware specifics. It supports a wide range of robots and teleoperation devices.
+
+```python
+from lerobot.robots.myrobot import MyRobot
+
+# Connect to a robot
+robot = MyRobot(config=...)
+robot.connect()
+
+# Read observation and send action
+obs = robot.get_observation()
+action = model.select_action(obs)
+robot.send_action(action)
+```
+
+**Supported Hardware:** SO100, LeKiwi, Koch, HopeJR, OMX, EarthRover, Reachy2, Gamepads, Keyboards, Phones, OpenARM, Unitree G1.
+
+While these devices are natively integrated into the LeRobot codebase, the library is designed to be extensible. You can easily implement the Robot interface to utilize LeRobot's data collection, training, and visualization tools for your own custom robot.
+
+For detailed hardware setup guides, see the [Hardware Documentation](https://huggingface.co/docs/lerobot/integrate_hardware).
+
+## LeRobot Dataset
+
+To solve the data fragmentation problem in robotics, we utilize the **LeRobotDataset** format.
+
+- **Structure:** Synchronized MP4 videos (or images) for vision and Parquet files for state/action data.
+- **HF Hub Integration:** Explore thousands of robotics datasets on the [Hugging Face Hub](https://huggingface.co/lerobot).
+- **Tools:** Seamlessly delete episodes, split by indices/fractions, add/remove features, and merge multiple datasets.
+
+```python
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+
+# Load a dataset from the Hub
+dataset = LeRobotDataset("lerobot/aloha_mobile_cabinet")
+
+# Access data (automatically handles video decoding)
+episode_index=0
+print(f"{dataset[episode_index]['action'].shape=}\n")
+```
+
+Learn more about it in the [LeRobotDataset Documentation](https://huggingface.co/docs/lerobot/lerobot-dataset-v3)
+
+## SoTA Models
+
+LeRobot implements state-of-the-art policies in pure PyTorch, covering Imitation Learning, Reinforcement Learning, and Vision-Language-Action (VLA) models, with more coming soon. It also provides you with the tools to instrument and inspect your training process.
+
+<p align="center">
+  <img alt="Gr00t Architecture" src="./media/readme/VLA_architecture.jpg" width="640px">
+</p>
+
+Training a policy is as simple as running a script configuration:

 ```bash
-pip install 'lerobot[all]'          # All available features
-pip install 'lerobot[aloha,pusht]'  # Specific features (Aloha & Pusht)
-pip install 'lerobot[feetech]'      # Feetech motor support
+lerobot-train \
+  --policy=act \
+  --dataset.repo_id=lerobot/aloha_mobile_cabinet
 ```

-_Replace `[...]` with your desired features._
+| Category                   | Models                                                                                                                                                                 |
+| -------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **Imitation Learning**     | [ACT](./docs/source/policy_act_README.md), [Diffusion](./docs/source/policy_diffusion_README.md), [VQ-BeT](./docs/source/policy_vqbet_README.md)                       |
+| **Reinforcement Learning** | [HIL-SERL](./docs/source/hilserl.mdx), [TDMPC](./docs/source/policy_tdmpc_README.md) & QC-FQL (coming soon)                                                            |
+| **VLAs Models**            | [Pi0.5](./docs/source/pi05.mdx), [GR00T N1.5](./docs/source/policy_groot_README.md), [SmolVLA](./docs/source/policy_smolvla_README.md), [XVLA](./docs/source/xvla.mdx) |

-**Available Tags:**
-For a full list of optional dependencies, see:
-https://pypi.org/project/lerobot/
+Similarly to the hardware, you can easily implement your own policy & leverage LeRobot's data collection, training, and visualization tools, and share your model to the HF Hub

-> [!NOTE]
-> For lerobot 0.4.0, if you want to install pi tags, you will have to do: `pip install "lerobot[pi]@git+https://github.com/huggingface/lerobot.git"`.
->
-> This will be solved in the next patch release
+For detailed policy setup guides, see the [Policy Documentation](https://huggingface.co/docs/lerobot/bring_your_own_policies).

-### Weights & Biases
+## Inference & Evaluation

-To use [Weights and Biases](https://docs.wandb.ai/quickstart) for experiment tracking, log in with
+Evaluate your policies in simulation or on real hardware using the unified evaluation script. LeRobot supports standard benchmarks like **LIBERO**, **MetaWorld** and more to come.

 ```bash
-wandb login
+# Evaluate a policy on the LIBERO benchmark
+lerobot-eval \
+  --policy.path=lerobot/pi0_libero_finetuned \
+  --env.type=libero \
+  --env.task=libero_object \
+  --eval.n_episodes=10
 ```

-(note: you will also need to enable WandB in the configuration. See below.)
+Learn how to implement your own simulation environment or benchmark and distribute it from the HF Hub by following the [EnvHub Documentation](https://huggingface.co/docs/lerobot/envhub)

-### Visualize datasets
+## Resources

-Check out [example 1](https://github.com/huggingface/lerobot/blob/main/examples/dataset/load_lerobot_dataset.py) that illustrates how to use our dataset class which automatically downloads data from the Hugging Face hub.
-
-You can also locally visualize episodes from a dataset on the hub by executing our script from the command line:
-
-```bash
-lerobot-dataset-viz \
-    --repo-id lerobot/pusht \
-    --episode-index 0
-```
-
-or from a dataset in a local folder with the `root` option and the `--mode local` (in the following case the dataset will be searched for in `./my_local_data_dir/lerobot/pusht`)
-
-```bash
-lerobot-dataset-viz \
-    --repo-id lerobot/pusht \
-    --root ./my_local_data_dir \
-    --mode local \
-    --episode-index 0
-```
-
-It will open `rerun.io` and display the camera streams, robot states and actions, like this:
-
-https://github-production-user-asset-6210df.s3.amazonaws.com/4681518/328035972-fd46b787-b532-47e2-bb6f-fd536a55a7ed.mov?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAVCODYLSA53PQK4ZA%2F20240505%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20240505T172924Z&X-Amz-Expires=300&X-Amz-Signature=d680b26c532eeaf80740f08af3320d22ad0b8a4e4da1bcc4f33142c15b509eda&X-Amz-SignedHeaders=host&actor_id=24889239&key_id=0&repo_id=748713144
-
-Our script can also visualize datasets stored on a distant server. See `lerobot-dataset-viz --help` for more instructions.
-
-### The `LeRobotDataset` format
-
-A dataset in `LeRobotDataset` format is very simple to use. It can be loaded from a repository on the Hugging Face hub or a local folder simply with e.g. `dataset = LeRobotDataset("lerobot/aloha_static_coffee")` and can be indexed into like any Hugging Face and PyTorch dataset. For instance `dataset[0]` will retrieve a single temporal frame from the dataset containing observation(s) and an action as PyTorch tensors ready to be fed to a model.
-
-A specificity of `LeRobotDataset` is that, rather than retrieving a single frame by its index, we can retrieve several frames based on their temporal relationship with the indexed frame, by setting `delta_timestamps` to a list of relative times with respect to the indexed frame. For example, with `delta_timestamps = {"observation.image": [-1, -0.5, -0.2, 0]}` one can retrieve, for a given index, 4 frames: 3 "previous" frames 1 second, 0.5 seconds, and 0.2 seconds before the indexed frame, and the indexed frame itself (corresponding to the 0 entry). See example [1_load_lerobot_dataset.py](https://github.com/huggingface/lerobot/blob/main/examples/dataset/load_lerobot_dataset.py) for more details on `delta_timestamps`.
-
-Under the hood, the `LeRobotDataset` format makes use of several ways to serialize data which can be useful to understand if you plan to work more closely with this format. We tried to make a flexible yet simple dataset format that would cover most type of features and specificities present in reinforcement learning and robotics, in simulation and in real-world, with a focus on cameras and robot states but easily extended to other types of sensory inputs as long as they can be represented by a tensor.
-
-Here are the important details and internal structure organization of a typical `LeRobotDataset` instantiated with `dataset = LeRobotDataset("lerobot/aloha_static_coffee")`. The exact features will change from dataset to dataset but not the main aspects:
-
-```
-dataset attributes:
-  ├ hf_dataset: a Hugging Face dataset (backed by Arrow/parquet). Typical features example:
-  │  ├ observation.images.cam_high (VideoFrame):
-  │  │   VideoFrame = {'path': path to a mp4 video, 'timestamp' (float32): timestamp in the video}
-  │  ├ observation.state (list of float32): position of an arm joints (for instance)
-  │  ... (more observations)
-  │  ├ action (list of float32): goal position of an arm joints (for instance)
-  │  ├ episode_index (int64): index of the episode for this sample
-  │  ├ frame_index (int64): index of the frame for this sample in the episode ; starts at 0 for each episode
-  │  ├ timestamp (float32): timestamp in the episode
-  │  ├ next.done (bool): indicates the end of an episode ; True for the last frame in each episode
-  │  └ index (int64): general index in the whole dataset
-  ├ meta: a LeRobotDatasetMetadata object containing:
-  │  ├ info: a dictionary of metadata on the dataset
-  │  │  ├ codebase_version (str): this is to keep track of the codebase version the dataset was created with
-  │  │  ├ fps (int): frame per second the dataset is recorded/synchronized to
-  │  │  ├ features (dict): all features contained in the dataset with their shapes and types
-  │  │  ├ total_episodes (int): total number of episodes in the dataset
-  │  │  ├ total_frames (int): total number of frames in the dataset
-  │  │  ├ robot_type (str): robot type used for recording
-  │  │  ├ data_path (str): formattable string for the parquet files
-  │  │  └ video_path (str): formattable string for the video files (if using videos)
-  │  ├ episodes: a DataFrame containing episode metadata with columns:
-  │  │  ├ episode_index (int): index of the episode
-  │  │  ├ tasks (list): list of tasks for this episode
-  │  │  ├ length (int): number of frames in this episode
-  │  │  ├ dataset_from_index (int): start index of this episode in the dataset
-  │  │  └ dataset_to_index (int): end index of this episode in the dataset
-  │  ├ stats: a dictionary of statistics (max, mean, min, std) for each feature in the dataset, for instance
-  │  │  ├ observation.images.front_cam: {'max': tensor with same number of dimensions (e.g. `(c, 1, 1)` for images, `(c,)` for states), etc.}
-  │  │  └ ...
-  │  └ tasks: a DataFrame containing task information with task names as index and task_index as values
-  ├ root (Path): local directory where the dataset is stored
-  ├ image_transforms (Callable): optional image transformations to apply to visual modalities
-  └ delta_timestamps (dict): optional delta timestamps for temporal queries
-```
-
-A `LeRobotDataset` is serialised using several widespread file formats for each of its parts, namely:
-
- hf_dataset stored using Hugging Face datasets library serialization to parquet
- videos are stored in mp4 format to save space
- metadata are stored in plain json/jsonl files
-
-Dataset can be uploaded/downloaded from the HuggingFace hub seamlessly. To work on a local dataset, you can specify its location with the `root` argument if it's not in the default `~/.cache/huggingface/lerobot` location.
-
-#### Reproduce state-of-the-art (SOTA)
-
-We provide some pretrained policies on our [hub page](https://huggingface.co/lerobot) that can achieve state-of-the-art performances.
-You can reproduce their training by loading the config from their run. Simply running:
-
-```bash
-lerobot-train --config_path=lerobot/diffusion_pusht
-```
-
-reproduces SOTA results for Diffusion Policy on the PushT task.
-
-## Contribute
-
-If you would like to contribute to 🤗 LeRobot, please check out our [contribution guide](https://github.com/huggingface/lerobot/blob/main/CONTRIBUTING.md).
-
-### Add a pretrained policy
-
-Once you have trained a policy you may upload it to the Hugging Face hub using a hub id that looks like `${hf_user}/${repo_name}` (e.g. [lerobot/diffusion_pusht](https://huggingface.co/lerobot/diffusion_pusht)).
-
-You first need to find the checkpoint folder located inside your experiment directory (e.g. `outputs/train/2024-05-05/20-21-12_aloha_act_default/checkpoints/002500`). Within that there is a `pretrained_model` directory which should contain:
-
- `config.json`: A serialized version of the policy configuration (following the policy's dataclass config).
- `model.safetensors`: A set of `torch.nn.Module` parameters, saved in [Hugging Face Safetensors](https://huggingface.co/docs/safetensors/index) format.
- `train_config.json`: A consolidated configuration containing all parameters used for training. The policy configuration should match `config.json` exactly. This is useful for anyone who wants to evaluate your policy or for reproducibility.
-
-To upload these to the hub, run the following:
-
-```bash
-huggingface-cli upload ${hf_user}/${repo_name} path/to/pretrained_model
-```
-
-See [lerobot_eval.py](https://github.com/huggingface/lerobot/blob/main/src/lerobot/scripts/lerobot_eval.py) for an example of how other people may use your policy.
-
-### Acknowledgment
-
- The LeRobot team 🤗 for building SmolVLA [Paper](https://arxiv.org/abs/2506.01844), [Blog](https://huggingface.co/blog/smolvla).
- Thanks to Tony Zhao, Zipeng Fu and colleagues for open sourcing ACT policy, ALOHA environments and datasets. Ours are adapted from [ALOHA](https://tonyzhaozh.github.io/aloha) and [Mobile ALOHA](https://mobile-aloha.github.io).
- Thanks to Cheng Chi, Zhenjia Xu and colleagues for open sourcing Diffusion policy, Pusht environment and datasets, as well as UMI datasets. Ours are adapted from [Diffusion Policy](https://diffusion-policy.cs.columbia.edu) and [UMI Gripper](https://umi-gripper.github.io).
- Thanks to Nicklas Hansen, Yunhai Feng and colleagues for open sourcing TDMPC policy, Simxarm environments and datasets. Ours are adapted from [TDMPC](https://github.com/nicklashansen/tdmpc) and [FOWM](https://www.yunhaifeng.com/FOWM).
- Thanks to Antonio Loquercio and Ashish Kumar for their early support.
- Thanks to [Seungjae (Jay) Lee](https://sjlee.cc/), [Mahi Shafiullah](https://mahis.life/) and colleagues for open sourcing [VQ-BeT](https://sjlee.cc/vq-bet/) policy and helping us adapt the codebase to our repository. The policy is adapted from [VQ-BeT repo](https://github.com/jayLEE0301/vq_bet_official).
+- **[Documentation](https://huggingface.co/docs/lerobot/index):** The complete guide to tutorials & API.
+- **[Discord](https://discord.gg/3gxM6Avj):** Join the `LeRobot` server to discuss with the community.
+- **[X](https://x.com/LeRobotHF):** Follow us on X to stay up-to-date with the latest developments.
+- **[Robot Learning Tutorial](https://huggingface.co/spaces/lerobot/robot-learning-tutorial):** A free, hands-on course to learn robot learning using LeRobot.

 ## Citation

-If you want, you can cite this work with:
+If you use LeRobot in your research, please cite:

 ```bibtex
@misc{cadene2024lerobot,
@@ -339,6 +144,14 @@ If you want, you can cite this work with:
 }
 ```

-## Star History
+## Contribute

-[![Star History Chart](https://api.star-history.com/svg?repos=huggingface/lerobot&type=Timeline)](https://star-history.com/#huggingface/lerobot&Timeline)
+We welcome contributions from everyone in the community! To get started, please read our [CONTRIBUTING.md](./CONTRIBUTING.md) guide. Whether you're adding a new feature, improving documentation, or fixing a bug, your help and feedback are invaluable. We're incredibly excited about the future of open-source robotics and can't wait to work with you on what's next—thank you for your support!
+
+<p align="center">
+  <img alt="SO101 Video" src="./media/readme/so100_video.webp" width="640px">
+</p>
+
+<div align="center">
+<sub>Built by the <a href="https://huggingface.co/lerobot">LeRobot</a> team at <a href="https://huggingface.co">Hugging Face</a> with ❤️</sub>
+</div>
@@ -19,6 +19,8 @@
    title: Train RL in Simulation
  - local: multi_gpu_training
    title: Multi GPU training
+  - local: peft_training
+    title: Training with PEFT (e.g., LoRA)
  title: "Tutorials"
 - sections:
  - local: lerobot-dataset-v3
@@ -42,6 +44,10 @@
  - local: xvla
    title: X-VLA
  title: "Policies"
+- sections:
+  - local: sarm
+    title: SARM
+  title: "Reward Models"
 - sections:
  - local: async
    title: Use Async Inference
@@ -201,7 +201,8 @@ from lerobot.teleoperators.so100_leader.so100_leader import SO100Leader
 from lerobot.utils.control_utils import init_keyboard_listener
 from lerobot.utils.utils import log_say
 from lerobot.utils.visualization_utils import init_rerun
-from lerobot.record import record_loop
+from lerobot.scripts.lerobot_record import record_loop
+from lerobot.processor import make_default_processors

 NUM_EPISODES = 5
 FPS = 30
@@ -209,12 +210,19 @@ EPISODE_TIME_SEC = 60
 RESET_TIME_SEC = 10
 TASK_DESCRIPTION = "My task description"

-# Create the robot and teleoperator configurations
-camera_config = {"front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS)}
+# Create robot configuration
 robot_config = SO100FollowerConfig(
-    port="/dev/tty.usbmodem58760434471", id="my_awesome_follower_arm", cameras=camera_config
+    id="my_awesome_follower_arm",
+    cameras={
+        "front": OpenCVCameraConfig(index_or_path=0, width=640, height=480, fps=FPS) # Optional: fourcc="MJPG" for troubleshooting OpenCV async error.
+    },
+    port="/dev/tty.usbmodem58760434471",
+)
+
+teleop_config = SO100LeaderConfig(
+    id="my_awesome_leader_arm",
+    port="/dev/tty.usbmodem585A0077581",
 )
-teleop_config = SO100LeaderConfig(port="/dev/tty.usbmodem585A0077581", id="my_awesome_leader_arm")

 # Initialize the robot and teleoperator
 robot = SO100Follower(robot_config)
@@ -243,6 +251,9 @@ init_rerun(session_name="recording")
 robot.connect()
 teleop.connect()

+# Create the required processors
+teleop_action_processor, robot_action_processor, robot_observation_processor = make_default_processors()
+
 episode_idx = 0
 while episode_idx < NUM_EPISODES and not events["stop_recording"]:
    log_say(f"Recording episode {episode_idx + 1} of {NUM_EPISODES}")
@@ -251,6 +262,9 @@ while episode_idx < NUM_EPISODES and not events["stop_recording"]:
        robot=robot,
        events=events,
        fps=FPS,
+        teleop_action_processor=teleop_action_processor,
+        robot_action_processor=robot_action_processor,
+        robot_observation_processor=robot_observation_processor,
        teleop=teleop,
        dataset=dataset,
        control_time_s=EPISODE_TIME_SEC,
@@ -265,6 +279,9 @@ while episode_idx < NUM_EPISODES and not events["stop_recording"]:
            robot=robot,
            events=events,
            fps=FPS,
+            teleop_action_processor=teleop_action_processor,
+            robot_action_processor=robot_action_processor,
+            robot_observation_processor=robot_observation_processor,
            teleop=teleop,
            control_time_s=RESET_TIME_SEC,
            single_task=TASK_DESCRIPTION,
@@ -0,0 +1,62 @@
+# Parameter efficient fine-tuning with 🤗 PEFT
+
+[🤗 PEFT](https://github.com/huggingface/peft) (Parameter-Efficient Fine-Tuning) is a library for efficiently adapting
+large pretrained models such as pre-trained policies (e.g., SmolVLA, π₀, ...) to new tasks without training all
+of the model's parameters while yielding comparable performance.
+
+Install the `lerobot[peft]` optional package to enable PEFT support.
+
+To read about all the possible methods of adaption, please refer to the [🤗 PEFT docs](https://huggingface.co/docs/peft/index).
+
+## Training SmolVLA
+
+In this section we'll show you how to train a pre-trained SmolVLA policy with PEFT on the libero dataset.
+For brevity we're only training on the `libero_spatial` subset. We will use `lerobot/smolvla_base` as the model
+to parameter efficiently fine-tune:
+
+```
+lerobot-train \
+ --policy.path=lerobot/smolvla_base \
+ --policy.repo_id=your_hub_name/my_libero_smolvla \
+ --dataset.repo_id=HuggingFaceVLA/libero \
+ --policy.output_features=null \
+ --policy.input_features=null \
+ --policy.optimizer_lr=1e-3 \
+ --policy.scheduler_decay_lr=1e-4 \
+ --env.type=libero \
+ --env.task=libero_spatial \
+ --steps=100000 \
+ --batch_size=32 \
+ --peft.method_type=LORA \
+ --peft.r=64
+```
+
+Note the `--peft.method_type` parameter that let's you select which PEFT method to use. Here we use
+[LoRA](https://huggingface.co/docs/peft/main/en/package_reference/lora) (Low-Rank Adapter) which is probably the most
+popular fine-tuning method to date. Low-rank adaption means that we only fine-tune a matrix with comparably low rank
+instead of the full weight matrix. This rank can be specified using the `--peft.r` parameter. The higher the rank
+the closer you get to full fine-tuning
+
+There are more complex methods that have more parameters. These are not yet supported, feel free to raise an issue
+if you want to see a specific PEFT method supported.
+
+By default, PEFT will target the `q_proj` and `v_proj` layers of the LM expert in SmolVLA. It will also target the
+state and action projection matrices as they are most likely task-dependent. If you need to target different layers
+you can use `--peft.target_modules` to specify which layers to target. You can refer to the respective PEFT method's
+documentation to see what inputs are supported, (e.g., [LoRA's target_modules documentation](https://huggingface.co/docs/peft/main/en/package_reference/lora#peft.LoraConfig.target_modules)).
+Usually a list of suffixes or a regex are supported. For example, to target the MLPs of the `lm_expert` instead of
+the `q` and `v` projections, use:
+
+```
+--peft.target_modules='(model\.vlm_with_expert\.lm_expert\..*\.(down|gate|up)_proj|.*\.(state_proj|action_in_proj|action_out_proj|action_time_mlp_in|action_time_mlp_out))'
+```
+
+In case you need to fully fine-tune a layer instead of just adapting it, you can supply a list of layer suffixes
+to the `--peft.full_training_modules` parameter:
+
+```
+--peft.full_training_modules=["state_proj"]
+```
+
+The learning rate and the scheduled target learning rate can usually be scaled by a factor of 10 compared to the
+learning rate used for full fine-tuning (e.g., 1e-4 normal, so 1e-3 using LoRA).
@@ -0,0 +1,586 @@
+# SARM: Stage-Aware Reward Modeling
+
+SARM (Stage-Aware Reward Modeling) is a video-based reward modeling framework for long-horizon robot manipulation tasks. This guide covers how to train SARM reward models and optionally use them with Reward-Aligned Behavior Cloning (RA-BC).
+
+**Paper**: [SARM: Stage-Aware Reward Modeling for Long Horizon Robot Manipulation](https://arxiv.org/abs/2509.25358)
+
+## Why Reward Models?
+
+Standard behavior cloning treats all demonstration frames equally, but real-world robot datasets are messy. They contain hesitations, corrections, and variable-quality trajectories. Reward models solve this by learning a generalizable notion of **task progress** from demonstrations: given video frames and a task description, they predict how close the robot is to completing the task (0→1). This learned "progress signal" can be used in multiple ways, two promising applications are: (1) **weighted imitation learning** (RA-BC), where high-progress frames receive more weight during policy training, and (2) **reinforcement learning**, where the reward model provides dense rewards for online or offline policy improvement.
+
+## Overview
+
+SARM has following features:
+
+1. **Stage-aware architecture**: Jointly predicts the high-level task stage and fine-grained progress within each stage
+2. **Subtask annotations**: Uses natural language subtask annotations to derive consistent progress labels
+3. **Temporal proportions**: Computes dataset-level priors (α̅\_k) for each subtask to normalize progress across variable-length demonstrations
+
+SARM trains on a compact **stage+tau** target for each frame:
+
+- **stage**: integer stage index `k ∈ {0, ..., K-1}`
+- **τ (tau)**: within-stage progress `τ ∈ [0, 1]`
+- **target encoding**: `y = k + τ` (this is what the dataset processor produces)
+
+At inference time (and in downstream RA-BC), SARM converts the raw `k + τ` value into a **normalized progress** in `[0, 1]` using dataset-level **temporal proportions** `α̅_k` (stored in `meta/temporal_proportions_*.json`).
+
+This matches **Formula (2)** from the paper:
+
+```
+progress_t = P_{k-1} + α̅_k × τ_t
+```
+
+Where:
+
+- `τ_t = (t - s_k) / (e_k - s_k)` is within-subtask normalized time
+- `P_{k-1}` is cumulative prior (sum of previous subtask proportions)
+- `α̅_k` is the temporal proportion for subtask k
+
+This ensures identical task states map to consistent progress values, even across demonstrations of different lengths.
+
+## Inputs and Targets (What the new code expects)
+
+SARM is trained through its processor (`src/lerobot/policies/sarm/processor_sarm.py`), which:
+
+- **Encodes** images and task text with CLIP (ViT-B/32) into `video_features` and `text_features`
+- **Pads/truncates** robot state into `state_features` (up to `max_state_dim`)
+- **Builds targets** as `sparse_targets` (and `dense_targets` in `dense_only`/`dual`) using the stage+tau encoding `y = k + τ`
+- **Masks rewind frames** using a per-sample `lengths` tensor (rewind is a training-time augmentation)
+
+At minimum, each training sample needs:
+
+- `task` (string): task description
+- `policy.image_key` images and `policy.state_key` states from the dataset
+
+---
+
+## Annotation Modes
+
+You can choose from **3 annotation modes** that determine how progress labels are computed:
+
+| Mode           | Annotations Required | Heads                        | Use Case                                                     |
+| -------------- | -------------------- | ---------------------------- | ------------------------------------------------------------ |
+| `single_stage` | None                 | Sparse only                  | Simple tasks, quick experiments, no VLM needed               |
+| `dense_only`   | Dense (VLM)          | Dual (sparse auto-generated) | Detailed subtask tracking without defining high-level stages |
+| `dual`         | Sparse + Dense (VLM) | Dual                         | Full SARM paper setup with both granularities                |
+
+### Mode Details
+
+<hfoptions id="mode_explanation">
+<hfoption id="single_stage">
+
+**No annotations required.** The entire episode is treated as a single stage called `"task"`, and progress is linear from 0 to 1 over the episode duration.
+
+- **Sparse head**: 1 stage ("task"), linear progress
+- **Dense head**: Not used
+- **Best for**: Simple tasks, quick experiments, or when VLM annotation is not available
+
+## Set Up Your Environment
+
+1. Install LeRobot by following our [Installation Guide](./installation).
+2. Install SARM dependencies by running:
+
+```bash
+pip install -e ".[sarm]"
+```
+
+Workflow:
+
+```
+1. Train SARM → 2. Visualize predictions → 3. (Optional) Train policy with RA-BC
+```
+
+</hfoption>
+<hfoption id="dense_only">
+
+**Only dense (fine-grained) annotations from a VLM.** The sparse head automatically uses a single `"task"` stage covering the full episode, while the dense head learns detailed subtask progression.
+
+- **Sparse head**: 1 stage ("task"), linear progress (auto-generated)
+- **Dense head**: Multiple fine-grained stages from VLM annotations
+- **Best for**: When you want detailed subtask tracking but don't need to define high-level stages
+
+Workflow:
+
+```
+1. Annotate (dense) → 2. Verify → 3. Train SARM → 4. Visualize → 5. (Optional) Train policy with RA-BC
+```
+
+</hfoption>
+<hfoption id="dual">
+
+**Both sparse and dense annotations from VLM.** Full dual-head mode as described in the SARM paper, with both high-level (sparse) and fine-grained (dense) stage predictions.
+
+- **Sparse head**: High-level stages from VLM annotations
+- **Dense head**: Fine-grained stages from VLM annotations
+- **Best for**: Complex multi-stage tasks where both granularities are useful
+
+Workflow:
+
+```
+1. Annotate (sparse+dense) → 2. Verify → 3. Train SARM → 4. Visualize → 5. (Optional) Train policy with RA-BC
+```
+
+</hfoption>
+</hfoptions>
+
+---
+
+## Step 1: Subtask Annotation
+
+<hfoptions id="annotation_mode">
+<hfoption id="single_stage">
+
+**No annotation required!** Skip this step entirely. The model will use the episode's task description and compute linear progress automatically.
+
+</hfoption>
+<hfoption id="dense_only">
+
+Generate **dense (fine-grained) annotations only** using a VLM. The sparse stage will be auto-generated.
+
+```bash
+python src/lerobot/data_processing/sarm_annotations/subtask_annotation.py \
+  --repo-id your-username/your-dataset \
+  --dense-only \
+  --dense-subtasks "Bring robot arms up from starting position,Grab near side and do 1st fold,Grab side and do 2nd fold,Grab side and do 3rd fold to finish folding" \
+  --video-key observation.images.base \
+  --num-workers 4 \
+  --push-to-hub
+```
+
+**What gets saved:**
+
+- `meta/temporal_proportions_sparse.json` - Auto-generated sparse proportions (`{"task": 1.0}`)
+- `meta/temporal_proportions_dense.json` - Dense temporal proportions
+- Per-episode columns in `episodes/*.parquet`:
+  - `dense_subtask_names`, `dense_subtask_start_frames`, `dense_subtask_end_frames`
+  - (also time-based columns: `dense_subtask_start_times`, `dense_subtask_end_times`)
+
+</hfoption>
+<hfoption id="dual">
+
+Generate **both sparse (high-level) and dense (fine-grained) annotations** using a VLM.
+
+```bash
+python src/lerobot/data_processing/sarm_annotations/subtask_annotation.py \
+  --repo-id your-username/your-dataset \
+  --sparse-subtasks "Bring arms up from starting position,Fold the towel (3 folds in total)" \
+  --dense-subtasks "Bring robot arms up from starting position,Grab near side and do 1st fold,Grab side and do 2nd fold,Grab side and do 3rd fold to finish folding" \
+  --video-key observation.images.base \
+  --num-workers 4 \
+  --push-to-hub
+```
+
+**What gets saved:**
+
+- `meta/temporal_proportions_sparse.json` - Sparse temporal proportions
+- `meta/temporal_proportions_dense.json` - Dense temporal proportions
+- Per-episode columns in `episodes/*.parquet`:
+  - `sparse_subtask_names`, `sparse_subtask_start_frames`, `sparse_subtask_end_frames`
+  - `dense_subtask_names`, `dense_subtask_start_frames`, `dense_subtask_end_frames`
+  - (also time-based columns: `*_subtask_start_times`, `*_subtask_end_times`)
+
+</hfoption>
+</hfoptions>
+
+### Annotation Arguments
+
+| Argument               | Description                                                                     |
+| ---------------------- | ------------------------------------------------------------------------------- |
+| `--repo-id`            | HuggingFace dataset repository ID                                               |
+| `--sparse-subtasks`    | Comma-separated list of high-level subtask names                                |
+| `--dense-subtasks`     | Comma-separated list of fine-grained subtask names                              |
+| `--dense-only`         | Generate only dense annotations (auto-creates sparse "task" stage)              |
+| `--video-key`          | Camera/video key to use (e.g., `observation.images.top`)                        |
+| `--num-workers`        | Number of parallel GPU workers (default: 1)                                     |
+| `--episodes`           | Specific episode indices to annotate (default: all)                             |
+| `--skip-existing`      | Skip episodes that already have annotations                                     |
+| `--model`              | VLM model (default: `Qwen/Qwen3-VL-30B-A3B-Instruct`)                           |
+| `--num-visualizations` | Number of episodes to visualize after annotation (default: 5, set to 0 to skip) |
+
+> **Note**: After annotation completes, 5 episodes are automatically visualized by default. Use `--num-visualizations 0` to skip this step.
+
+---
+
+## Step 2: Verify Annotations
+
+<hfoptions id="verify_mode">
+<hfoption id="single_stage">
+
+**No verification needed!** Skip this step.
+
+</hfoption>
+<hfoption id="dense_only">
+
+Visualize annotations using the `--visualize-only` flag:
+
+```bash
+python src/lerobot/data_processing/sarm_annotations/subtask_annotation.py \
+  --repo-id your-username/your-dataset \
+  --visualize-only \
+  --visualize-type dense \
+  --num-visualizations 5 \
+  --video-key observation.images.base \
+  --output-dir ./subtask_viz
+```
+
+</hfoption>
+<hfoption id="dual">
+
+Visualize annotations using the `--visualize-only` flag:
+
+```bash
+python src/lerobot/data_processing/sarm_annotations/subtask_annotation.py \
+  --repo-id your-username/your-dataset \
+  --visualize-only \
+  --visualize-type both \
+  --num-visualizations 5 \
+  --video-key observation.images.base \
+  --output-dir ./subtask_viz
+```
+
+</hfoption>
+</hfoptions>
+
+This generates visualizations showing video frames with subtask boundaries overlaid and timeline of subtasks.
+
+### Visualization Arguments
+
+| Argument               | Description                                                    |
+| ---------------------- | -------------------------------------------------------------- |
+| `--visualize-only`     | Only visualize existing annotations (no generation)            |
+| `--num-visualizations` | Number of episodes to visualize (default: 5)                   |
+| `--visualize-type`     | Type of annotations to visualize: `sparse`, `dense`, or `both` |
+
+**Tip**: If annotations are inaccurate, adjust your subtask descriptions to be more specific and re-run.
+
+---
+
+## Step 3: Train SARM
+
+<hfoptions id="train_mode">
+<hfoption id="single_stage">
+
+Train with **no annotations** - uses linear progress from 0 to 1:
+
+```bash
+python src/lerobot/scripts/lerobot_train.py \
+  --dataset.repo_id=your-username/your-dataset \
+  --policy.type=sarm \
+  --policy.annotation_mode=single_stage \
+  --policy.image_key=observation.images.base \
+  --output_dir=outputs/train/sarm_single \
+  --batch_size=32 \
+  --steps=5000 \
+  --wandb.enable=true \
+  --wandb.project=sarm \
+  --policy.repo_id=your-username/your-model-name
+```
+
+</hfoption>
+<hfoption id="dense_only">
+
+Train with **dense annotations only** (sparse auto-generated):
+
+```bash
+python src/lerobot/scripts/lerobot_train.py \
+  --dataset.repo_id=your-username/your-dataset \
+  --policy.type=sarm \
+  --policy.annotation_mode=dense_only \
+  --policy.image_key=observation.images.base \
+  --output_dir=outputs/train/sarm_dense \
+  --batch_size=32 \
+  --steps=5000 \
+  --wandb.enable=true \
+  --wandb.project=sarm \
+  --policy.repo_id=your-username/your-model-name
+```
+
+</hfoption>
+<hfoption id="dual">
+
+Train with **both sparse and dense annotations**:
+
+```bash
+python src/lerobot/scripts/lerobot_train.py \
+  --dataset.repo_id=your-username/your-dataset \
+  --policy.type=sarm \
+  --policy.annotation_mode=dual \
+  --policy.image_key=observation.images.base \
+  --output_dir=outputs/train/sarm_dual \
+  --batch_size=32 \
+  --steps=5000 \
+  --wandb.enable=true \
+  --wandb.project=sarm \
+  --policy.repo_id=your-username/your-model-name
+```
+
+</hfoption>
+</hfoptions>
+
+### Multi-GPU Training
+
+Add `accelerate launch --multi_gpu --num_processes=4` to use multiple GPUs for training.
+
+### Training Arguments
+
+| Argument                   | Description                                                       | Default                  |
+| -------------------------- | ----------------------------------------------------------------- | ------------------------ |
+| `--policy.annotation_mode` | `single_stage`, `dense_only`, or `dual`                           | `single_stage`           |
+| `--policy.image_key`       | Camera key for images                                             | `observation.images.top` |
+| `--policy.state_key`       | Key for joint states                                              | `observation.state`      |
+| `--policy.n_obs_steps`     | Observation history steps (total obs frames = `n_obs_steps + 1`)  | `8`                      |
+| `--policy.frame_gap`       | Gap (in frames) between sampled observations (at 30 fps: 30 ≈ 1s) | `30`                     |
+
+---
+
+## Step 4: Visualize Predictions
+
+Use `compute_rabc_weights.py` with `--visualize-only` to visualize model predictions (and, if available, annotation-derived targets) without writing a parquet file.
+
+<hfoptions id="viz_mode">
+<hfoption id="single_stage">
+
+```bash
+python src/lerobot/policies/sarm/compute_rabc_weights.py \
+  --dataset-repo-id your-username/your-dataset \
+  --reward-model-path your-username/sarm-model \
+  --visualize-only \
+  --num-visualizations 5 \
+  --head-mode sparse \
+  --output-dir ./sarm_viz
+```
+
+</hfoption>
+<hfoption id="dense_only">
+
+```bash
+python src/lerobot/policies/sarm/compute_rabc_weights.py \
+  --dataset-repo-id your-username/your-dataset \
+  --reward-model-path your-username/sarm-model \
+  --visualize-only \
+  --num-visualizations 5 \
+  --head-mode dense \
+  --output-dir ./sarm_viz
+```
+
+</hfoption>
+<hfoption id="dual">
+
+```bash
+python src/lerobot/policies/sarm/compute_rabc_weights.py \
+  --dataset-repo-id your-username/your-dataset \
+  --reward-model-path your-username/sarm-model \
+  --visualize-only \
+  --num-visualizations 5 \
+  --head-mode both \
+  --output-dir ./sarm_viz
+```
+
+</hfoption>
+</hfoptions>
+
+The visualization shows:
+
+- **Progress plot**: Predicted progress (and optional annotation-derived “GT” when available and `--stride 1`)
+- **Stage probabilities**: Stacked area plot of predicted stage probabilities
+- **Sample frames**: Key frames from the episode with progress/stage labels
+
+### Visualization Arguments
+
+| Argument               | Description                                               |
+| ---------------------- | --------------------------------------------------------- |
+| `--visualize-only`     | Only visualize predictions (no RABC computation)          |
+| `--num-visualizations` | Number of episodes to visualize (default: 5)              |
+| `--head-mode`          | SARM head to use: `sparse`, `dense`, or `both`            |
+| `--stride`             | Compute every N frames, interpolate the rest (default: 1) |
+
+---
+
+## Step 5 (Optional): Train Policy with RA-BC
+
+Reward-Aligned Behavior Cloning (RA-BC) uses the trained SARM model to weight training samples based on predicted progress improvement. This requires two steps:
+
+1. **Precompute progress values** for all frames using the trained SARM model
+2. **Train policy** with RA-BC weighting using the precomputed values
+
+### How RA-BC Works
+
+For each training sample, RA-BC computes the progress delta:
+
+```
+r_i = φ(o_{t+Δ}) - φ(o_t)
+```
+
+Where `φ` is the SARM progress prediction and `Δ` is the policy's `chunk_size`. Samples with positive progress (good demonstrations) get higher weights, while samples with negative or zero progress get down-weighted.
+
+The weighting follows **Equations 8-9** from the paper:
+
+- **Soft weight**: `w̃_i = clip((r_i − (μ − 2σ)) / (4σ + ε), 0, 1)`
+- **Final weight**: `w_i = 𝟙{r_i > κ} + 𝟙{0 ≤ r_i ≤ κ} × w̃_i`
+
+### Step 5a: Compute SARM Progress Values
+
+First, run the SARM model on all frames in your dataset to compute progress values:
+
+```bash
+python src/lerobot/policies/sarm/compute_rabc_weights.py \
+  --dataset-repo-id your-username/your-dataset \
+  --reward-model-path your-username/sarm-model \
+  --head-mode sparse \
+  --num-visualizations 5 \
+  --push-to-hub
+```
+
+This script:
+
+- Processes all frames and computes progress values
+- Saves progress values to a parquet file next to the dataset on disk (defaults to `<dataset_root>/sarm_progress.parquet`)
+- Generates visualizations of the first N episodes (default: 5)
+
+**Arguments:**
+
+| Argument               | Description                                                    | Default    |
+| ---------------------- | -------------------------------------------------------------- | ---------- |
+| `--reward-model-path`  | Path to trained SARM model                                     | (required) |
+| `--head-mode`          | SARM head to use: `sparse`, `dense`, or `both`                 | `sparse`   |
+| `--device`             | Device for inference                                           | `cuda`     |
+| `--visualize-only`     | Only visualize predictions (no RA-BC computation)              | `false`    |
+| `--num-visualizations` | Number of episodes to visualize (default: 5, set to 0 to skip) | `5`        |
+
+**Output format** (`sarm_progress.parquet`):
+
+| Column            | Description                                    |
+| ----------------- | ---------------------------------------------- |
+| `index`           | Global frame index in dataset                  |
+| `episode_index`   | Episode number                                 |
+| `frame_index`     | Local frame index within episode               |
+| `progress_sparse` | Sparse head progress value [0, 1]              |
+| `progress_dense`  | Dense head progress value [0, 1] (if computed) |
+
+### Step 5b: Train Policy with RA-BC
+
+Once you have the progress file, train your policy with RA-BC weighting. The progress file is auto-detected from the dataset path (`sarm_progress.parquet`). Currently PI0, PI0.5 and SmolVLA are supported with RA-BC:
+
+```bash
+python src/lerobot/scripts/lerobot_train.py \
+  --dataset.repo_id=your-username/your-dataset \
+  --policy.type=pi0 \
+  --use_rabc=true \
+  --rabc_head_mode=sparse \
+  --rabc_kappa=0.01 \
+  --output_dir=outputs/train/policy_rabc \
+  --batch_size=32 \
+  --steps=40000
+```
+
+The training script automatically:
+
+- Loads the precomputed progress values from the parquet file
+- Uses the policy's `chunk_size` to compute progress deltas (Δ)
+- Computes sample weights based on progress improvement
+- Applies weighted loss during training
+
+**RA-BC Arguments:**
+
+| Argument               | Description                                                | Default                            |
+| ---------------------- | ---------------------------------------------------------- | ---------------------------------- |
+| `--use_rabc`           | Enable RA-BC sample weighting                              | `false`                            |
+| `--rabc_progress_path` | Path to progress parquet file (auto-detected from dataset) | `sarm_progress.parquet` in dataset |
+| `--rabc_head_mode`     | Which SARM head's progress to use: `sparse` or `dense`     | `sparse`                           |
+| `--rabc_kappa`         | Threshold κ for high-quality samples                       | `0.01`                             |
+
+### Tuning RA-BC Kappa
+
+The `kappa` parameter is the threshold that determines which samples get full weight (w=1). Understanding how to tune it is critical for RA-BC to work effectively.
+
+**How the weighting works:**
+
+| Condition           | Weight                  |
+| ------------------- | ----------------------- |
+| `delta > kappa`     | 1.0 (hard threshold)    |
+| `0 ≤ delta ≤ kappa` | Soft weight from Eq. 8  |
+| `delta < 0`         | 0.0 (negative progress) |
+
+**Diagnosing kappa issues:**
+
+Monitor these WandB metrics during training:
+
+| Metric             | Healthy Range | Problem Indicator         |
+| ------------------ | ------------- | ------------------------- |
+| `rabc_mean_weight` | 0.3 - 0.8     | ≈ 1.0 means kappa too low |
+| `rabc_delta_mean`  | > 0           | Should be positive        |
+| `rabc_delta_std`   | > 0           | Variance in data quality  |
+
+**If `rabc_mean_weight ≈ 1.0`:** Your kappa is too low. Most samples have `delta > kappa` and bypass the soft-weighting entirely. RA-BC becomes equivalent to vanilla BC.
+
+**Setting kappa based on your data:**
+
+The default `kappa=0.01` was tuned for the paper's T-shirt folding task (~90s episodes at 30fps). For your dataset, check the logged `rabc_delta_mean` and `rabc_delta_std`:
+
+```
+# If delta_mean ≈ 0.03 and delta_std ≈ 0.02:
+# Most deltas fall in range [0.01, 0.05]
+
+# Option 1: Set kappa = delta_mean (medium selectivity)
+--rabc_kappa=0.03
+
+# Option 2: Set kappa = delta_mean + delta_std (high selectivity)
+--rabc_kappa=0.05
+
+# Option 3: Set kappa = delta_mean + 2*delta_std (very selective)
+--rabc_kappa=0.07
+```
+
+**When RA-BC may not help:**
+
+If your dataset is already high quality (consistent progress across all demonstrations), RA-BC won't provide much benefit since there's nothing to filter.
+
+### Multi-GPU Training with RA-BC
+
+```bash
+accelerate launch \
+  --multi_gpu \
+  --num_processes=4 \
+  src/lerobot/scripts/lerobot_train.py \
+  --dataset.repo_id=your-username/your-dataset \
+  --policy.type=pi0 \
+  --use_rabc=true \
+  --rabc_kappa=0.01 \
+  --output_dir=outputs/train/policy_rabc \
+  --batch_size=32 \
+  --steps=40000
+```
+
+---
+
+## Tips & Best Practices
+
+### Choosing a Mode
+
+- **Start with `single_stage`** for quick experiments - no annotation overhead
+- Use **`dense_only`** when you want detailed progress tracking but tasks don't have clear high-level stages
+- Use **`dual`** for complex tasks where both coarse and fine-grained progress is meaningful
+
+### Annotation Quality
+
+1. **Be specific with subtask names**: Instead of "fold", use "grab near side and fold toward center"
+2. **Verify with visualization**: Always check a few episodes before training
+3. **Consistent naming**: Use the same subtask names across all episodes
+
+### RA-BC
+
+1. **Train SARM first**: RA-BC quality depends entirely on SARM quality
+2. **Monitor `rabc_mean_weight`**: If it's ≈ 1.0, increase kappa (see [Tuning RA-BC Kappa](#tuning-ra-bc-kappa))
+
+---
+
+## Citation
+
+```bibtex
+@article{chen2025sarm,
+  title={SARM: Stage-Aware Reward Modeling for Long Horizon Robot Manipulation},
+  author={Chen, Qianzhong and Yu, Justin and Schwager, Mac and Abbeel, Pieter and Shentu, Yide and Wu, Philipp},
+  journal={arXiv preprint arXiv:2509.25358},
+  year={2025}
+}
+```
@@ -4,11 +4,12 @@ This guide covers the complete setup process for the Unitree G1 humanoid, from i

 ## About the Unitree G1

-We offer support for both 29 and 23 DOF G1. In this first PR we introduce:
+We offer support for both 29 and 23 DOF G1. We introduce:

 - **`unitree g1` robot class, handling low level communication with the humanoid**
 - **ZMQ socket bridge** for remote communication over WiFi, allowing one to deploy policies remotely instead of over ethernet or directly on the Orin
 - **GR00T locomotion policy** for bipedal walking and balance
+- **MuJoCo simulation mode** for testing policies without the physical robot

 ---

@@ -191,6 +192,10 @@ Press `Ctrl+C` to stop the policy.

 ---

+## Extra: Running in Simulation Mode (MuJoCo)
+
+You can now test and develop policies without a physical robot using MuJoCo. to do so set `is_simulation=True` in config.
+
 ## Additional Resources

 - [Unitree SDK Documentation](https://github.com/unitreerobotics/unitree_sdk2_python)
@@ -11,13 +11,14 @@ LeRobot provides several utilities for manipulating datasets:
 3. **Merge Datasets** - Combine multiple datasets into one. The datasets must have identical features, and episodes are concatenated in the order specified in `repo_ids`
 4. **Add Features** - Add new features to a dataset
 5. **Remove Features** - Remove features from a dataset
+6. **Convert to Video** - Convert image-based datasets to video format for efficient storage

 The core implementation is in `lerobot.datasets.dataset_tools`.
 An example script detailing how to use the tools API is available in `examples/dataset/use_dataset_tools.py`.

 ## Command-Line Tool: lerobot-edit-dataset

-`lerobot-edit-dataset` is a command-line script for editing datasets. It can be used to delete episodes, split datasets, merge datasets, add features, and remove features.
+`lerobot-edit-dataset` is a command-line script for editing datasets. It can be used to delete episodes, split datasets, merge datasets, add features, remove features, and convert image datasets to video format.

 Run `lerobot-edit-dataset --help` for more information on the configuration of each operation.

@@ -86,9 +87,71 @@ lerobot-edit-dataset \
    --operation.feature_names "['observation.images.top']"
 ```

+#### Convert to Video
+
+Convert an image-based dataset to video format, creating a new LeRobotDataset where images are stored as videos. This is useful for reducing storage requirements and improving data loading performance. The new dataset will have the exact same structure as the original, but with images encoded as MP4 videos in the proper LeRobot format.
+
+```bash
+# Local-only: Save to a custom output directory (no hub push)
+lerobot-edit-dataset \
+    --repo_id lerobot/pusht_image \
+    --operation.type convert_to_video \
+    --operation.output_dir /path/to/output/pusht_video
+
+# Save with new repo_id (local storage)
+lerobot-edit-dataset \
+    --repo_id lerobot/pusht_image \
+    --new_repo_id lerobot/pusht_video \
+    --operation.type convert_to_video
+
+# Convert and push to Hugging Face Hub
+lerobot-edit-dataset \
+    --repo_id lerobot/pusht_image \
+    --new_repo_id lerobot/pusht_video \
+    --operation.type convert_to_video \
+    --push_to_hub true
+
+# Convert with custom video codec and quality settings
+lerobot-edit-dataset \
+    --repo_id lerobot/pusht_image \
+    --operation.type convert_to_video \
+    --operation.output_dir outputs/pusht_video \
+    --operation.vcodec libsvtav1 \
+    --operation.pix_fmt yuv420p \
+    --operation.g 2 \
+    --operation.crf 30
+
+# Convert only specific episodes
+lerobot-edit-dataset \
+    --repo_id lerobot/pusht_image \
+    --operation.type convert_to_video \
+    --operation.output_dir outputs/pusht_video \
+    --operation.episode_indices "[0, 1, 2, 5, 10]"
+
+# Convert with multiple workers for parallel processing
+lerobot-edit-dataset \
+    --repo_id lerobot/pusht_image \
+    --operation.type convert_to_video \
+    --operation.output_dir outputs/pusht_video \
+    --operation.num_workers 8
+```
+
+**Parameters:**
+
+- `output_dir`: Custom output directory (optional - by default uses `new_repo_id` or `{repo_id}_video`)
+- `vcodec`: Video codec to use - options: `h264`, `hevc`, `libsvtav1` (default: `libsvtav1`)
+- `pix_fmt`: Pixel format - options: `yuv420p`, `yuv444p` (default: `yuv420p`)
+- `g`: Group of pictures (GOP) size - lower values give better quality but larger files (default: 2)
+- `crf`: Constant rate factor - lower values give better quality but larger files, 0 is lossless (default: 30)
+- `fast_decode`: Fast decode tuning option (default: 0)
+- `episode_indices`: List of specific episodes to convert (default: all episodes)
+- `num_workers`: Number of parallel workers for processing (default: 4)
+
+**Note:** The resulting dataset will be a proper LeRobotDataset with all cameras encoded as videos in the `videos/` directory, with parquet files containing only metadata (no raw image data). All episodes, stats, and tasks are preserved.
+
 ### Push to Hub

-Add the `--push_to_hub` flag to any command to automatically upload the resulting dataset to the Hugging Face Hub:
+Add the `--push_to_hub true` flag to any command to automatically upload the resulting dataset to the Hugging Face Hub:

 ```bash
 lerobot-edit-dataset \
@@ -96,7 +159,45 @@ lerobot-edit-dataset \
    --new_repo_id lerobot/pusht_after_deletion \
    --operation.type delete_episodes \
    --operation.episode_indices "[0, 2, 5]" \
-    --push_to_hub
+    --push_to_hub true
 ```

 There is also a tool for adding features to a dataset that is not yet covered in `lerobot-edit-dataset`.
+
+# Dataset Visualization
+
+## Online Visualization
+
+When you record a dataset using `lerobot`, it automatically uploads to the Hugging Face Hub unless you specify otherwise. To view the dataset online, use our **LeRobot Dataset Visualizer**, available at:
+https://huggingface.co/spaces/lerobot/visualize_dataset
+
+## Local Visualization
+
+You can also visualize episodes from a dataset locally using our command-line tool.
+
+**From the Hugging Face Hub:**
+
+```bash
+lerobot-dataset-viz \
+    --repo-id lerobot/pusht \
+    --episode-index 0
+```
+
+**From a local folder:**
+Add the `--root` option and set `--mode local`. For example, to search in `./my_local_data_dir/lerobot/pusht`:
+
+```bash
+lerobot-dataset-viz \
+    --repo-id lerobot/pusht \
+    --root ./my_local_data_dir \
+    --mode local \
+    --episode-index 0
+```
+
+Once executed, the tool opens `rerun.io` and displays the camera streams, robot states, and actions for the selected episode.
+
+For advanced usage—including visualizing datasets stored on a remote server—run:
+
+```bash
+lerobot-dataset-viz --help
+```
@@ -24,7 +24,7 @@ Built from pure Transformer encoders, X-VLA scales naturally with model size and
  <img
    src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/xvla-architecture2.png"
    alt="XVLA Architecture 2"
-    style="width: 32%; max-width: 450px; height: auto;"
+    style="width: 60%; height: auto;"
  />
 </p>

@@ -120,7 +120,7 @@ Adapted for Google Robot platforms.

 ### Recommended Training Configuration

-When fine-tuning X-VLA for a new embodiment or task, we recommend the following freezing strategy:
+When fine-tuning X-VLA for a new embodiment or task, we recommend not freezing the VLM, and also setting the `policy.dtype=bfloat16` to not hit OOM errors.

 ```bash
 lerobot-train \
@@ -129,25 +129,26 @@ lerobot-train \
  --job_name=xvla_training \
  --policy.path="lerobot/xvla-base" \
  --policy.repo_id="HF_USER/xvla-your-robot" \
-  --steps=3000 \
+  --policy.dtype=bfloat16 \
+  --policy.action_mode=auto \
+  --steps=20000 \
  --policy.device=cuda \
-  --policy.freeze_vision_encoder=True \
-  --policy.freeze_language_encoder=True \
-  --policy.train_policy_transformer=True \
-  --policy.train_soft_prompts=True \
-  --policy.action_mode=YOUR_ACTION_MODE
+  --policy.freeze_vision_encoder=false \
+  --policy.freeze_language_encoder=false \
+  --policy.train_policy_transformer=true \
+  --policy.train_soft_prompts=true \
 ```

 ### Training Parameters Explained

-| Parameter                  | Default | Description                              |
-| -------------------------- | ------- | ---------------------------------------- |
-| `freeze_vision_encoder`    | `True`  | Freeze the VLM vision encoder weights    |
-| `freeze_language_encoder`  | `True`  | Freeze the VLM language encoder weights  |
-| `train_policy_transformer` | `True`  | Allow policy transformer layers to train |
-| `train_soft_prompts`       | `True`  | Allow soft prompts to train              |
+| Parameter                  | Default | Description                                    |
+| -------------------------- | ------- | ---------------------------------------------- |
+| `freeze_vision_encoder`    | `false` | Do not freeze the VLM vision encoder weights   |
+| `freeze_language_encoder`  | `false` | Do not freeze the VLM language encoder weights |
+| `train_policy_transformer` | `true`  | Allow policy transformer layers to train       |
+| `train_soft_prompts`       | `true`  | Allow soft prompts to train                    |

-**💡 Best Practice**: For Phase II adaptation to new embodiments, freeze the VLM encoders and only train the policy transformer and soft prompts. This provides excellent sample efficiency with minimal compute.
+**💡 Best Practice**: For Phase II adaptation to new embodiments, do not freeze the VLM encoders and also train the policy transformer and soft prompts.

 ### Example: Training on Bimanual Robot

@@ -157,14 +158,15 @@ lerobot-train \
  --output_dir=./outputs/xvla_bimanual \
  --job_name=xvla_so101_training \
  --policy.path="lerobot/xvla-base" \
+  --policy.dtype=bfloat16 \
  --policy.repo_id="YOUR_USERNAME/xvla-biso101" \
  --steps=3000 \
  --policy.device=cuda \
  --policy.action_mode=so101_bimanual \
-  --policy.freeze_vision_encoder=True \
-  --policy.freeze_language_encoder=True \
-  --policy.train_policy_transformer=True \
-  --policy.train_soft_prompts=True
+  --policy.freeze_vision_encoder=false \
+  --policy.freeze_language_encoder=false \
+  --policy.train_policy_transformer=true \
+  --policy.train_soft_prompts=true
 ```

 💡 **Best Performance:** If you have sufficient computational resources and want to achieve best X-VLA finetuning performance, you should follow the official finetuning strategy:
@@ -172,71 +174,7 @@ lerobot-train \
 **🔥 Full-finetune all components with a custom learning-rate scheme**

 To ensure stable optimization, the Vision-Language Model (VLM) must be trained with only 1/10 of the base learning rate, while all other components use the full LR.
-This LR ratio is crucial for achieving strong and stable finetuning performance.
-To enable this behavior, you must:
-
-1. Implement a custom optimizer and register it in your training config
-
-```
-from dataclasses import dataclass, asdict
-from lerobot.optim.optimizers import OptimizerConfig
-import torch
-
-@OptimizerConfig.register_subclass("xvla-adamw")
-@dataclass
-class XVLAAdamW(OptimizerConfig):
-    lr: float = 1e-4
-    betas: tuple[float, float] = (0.9, 0.99)
-    eps: float = 1e-8
-    weight_decay: float = 0.0
-    grad_clip_norm: float = 10.0
-
-    def build(self, params: dict) -> torch.optim.Optimizer:
-        """
-        Expect `named_parameters()` as input.
-        Apply lr = lr / 10 for all VLM-related parameters.
-        """
-        assert isinstance(params, dict), \
-            "Custom LR optimizer requires `named_parameters()` as inputs."
-        kwargs = asdict(self)
-        kwargs.pop("grad_clip_norm")
-        vlm_group, other_group = [], []
-        for name, p in params.items():
-            if not p.requires_grad:
-                continue
-            if "vlm" in name.lower():
-                vlm_group.append(p)
-            else:
-                other_group.append(p)
-
-        param_groups = [
-            {"params": vlm_group, "lr": self.lr * 0.1, "weight_decay": self.weight_decay * 0.1},
-            {"params": other_group, "lr": self.lr, "weight_decay": self.weight_decay},
-        ]
-
-        return torch.optim.AdamW(param_groups, **kwargs)
-```
-
-2. Modify X-VLA’s get_optim_params to return named parameters
-
-Replace:
-
-```
-def get_optim_params(self) -> dict:
-    """Return only trainable parameters for optimization."""
-    return filter(lambda p: p.requires_grad, self.parameters())
-```
-
-with:
-
-```
-def get_optim_params(self):
-    """Return trainable named parameters."""
-    return filter(lambda kv: kv[1].requires_grad, self.named_parameters())
-```
-
-This ensures the optimizer receives a dict of named parameters, allowing it to correctly detect VLM modules and apply the 1/10 LR rule.
-
+This LR ratio is crucial for achieving strong and stable finetuning performance. This is already done for you by default.
 ❕Note

 Completely matching the official reported performance may require an additional warm-up LR schedule for soft-prompts, which can bring minor improvements.
@@ -326,6 +264,26 @@ domain_id = 3

 The domain_id is automatically added to observations by the `XVLAAddDomainIdProcessorStep` in the preprocessing pipeline.

+The `lerobot/xvla-base` model has been trained on the following domain IDs. It is recommended to choose one that most resembles your robot/configuration:
+
+#### Fine-tuning Datasets
+
+| Dataset Name     | Domain ID |
+| ---------------- | --------- |
+| Bridge           | 0         |
+| RT1              | 1         |
+| Calvin           | 2         |
+| libero           | 3         |
+| widowx-air       | 4         |
+| AIR-AGILEX-HQ    | 5         |
+| robotwin2_abs_ee | 6         |
+| robotwin2_clean  | 6         |
+| robocasa-human   | 7         |
+| VLABench         | 8         |
+| AGIBOT-challenge | 9         |
+| AIR-AGILEX       | 10        |
+| AIRBOT           | 18        |
+
 ### 3. Processor Steps

 X-VLA requires specific preprocessing and postprocessing steps for proper operation.
@@ -455,7 +455,18 @@ def demo_cli(cfg: RTCDemoConfig):
    if cfg.policy.type == "pi05" or cfg.policy.type == "pi0":
        config.compile_model = cfg.use_torch_compile

-    policy = policy_class.from_pretrained(cfg.policy.pretrained_path, config=config)
+    if config.use_peft:
+        from peft import PeftConfig, PeftModel
+
+        peft_pretrained_path = cfg.policy.pretrained_path
+        peft_config = PeftConfig.from_pretrained(peft_pretrained_path)
+
+        policy = policy_class.from_pretrained(
+            pretrained_name_or_path=peft_config.base_model_name_or_path, config=config
+        )
+        policy = PeftModel.from_pretrained(policy, peft_pretrained_path, config=peft_config)
+    else:
+        policy = policy_class.from_pretrained(cfg.policy.pretrained_path, config=config)

    # Turn on RTC
    policy.config.rtc_config = cfg.rtc
@@ -96,7 +96,7 @@ dependencies = [
 # Common
 pygame-dep = ["pygame>=2.5.1,<2.7.0"]
 placo-dep = ["placo>=0.9.6,<0.10.0"]
-transformers-dep = ["transformers>=4.53.0,<5.0.0"]
+transformers-dep = ["transformers>=4.57.1,<5.0.0"]
 grpcio-dep = ["grpcio==1.73.1", "protobuf==6.31.0"] # TODO: Bumb dependency (compatible with wandb)

 # Motors
@@ -120,6 +120,13 @@ intelrealsense = [
 phone = ["hebi-py>=2.8.0,<2.12.0", "teleop>=0.1.0,<0.2.0", "fastapi<1.0"]

 # Policies
+wallx = [
+    "transformers==4.49.0",
+    "peft==0.17.1",
+    "scipy==1.15.3",
+    "torchdiffeq==0.2.5",
+    "qwen_vl_utils==0.0.11"
+]
 pi = ["transformers @ git+https://github.com/huggingface/transformers.git@fix/lerobot_openpi"]
 smolvla = ["lerobot[transformers-dep]", "num2words>=0.5.14,<0.6.0", "accelerate>=1.7.0,<2.0.0", "safetensors>=0.4.3,<1.0.0"]
 groot = [
@@ -133,14 +140,16 @@ groot = [
    "ninja>=1.11.1,<2.0.0",
    "flash-attn>=2.5.9,<3.0.0 ; sys_platform != 'darwin'"
 ]
+sarm = ["lerobot[transformers-dep]", "faker>=33.0.0,<35.0.0", "matplotlib>=3.10.3,<4.0.0", "qwen-vl-utils>=0.0.14"]
 xvla = ["lerobot[transformers-dep]"]
 hilserl = ["lerobot[transformers-dep]", "gym-hil>=0.1.13,<0.2.0", "lerobot[grpcio-dep]", "lerobot[placo-dep]"]

 # Features
 async = ["lerobot[grpcio-dep]", "matplotlib>=3.10.3,<4.0.0"]
+peft = ["lerobot[transformers-dep]", "peft>=0.18.0"]

 # Development
-dev = ["pre-commit>=3.7.0,<5.0.0", "debugpy>=1.8.1,<1.9.0", "lerobot[grpcio-dep]", "grpcio-tools==1.73.1"]
+dev = ["pre-commit>=3.7.0,<5.0.0", "debugpy>=1.8.1,<1.9.0", "lerobot[grpcio-dep]", "grpcio-tools==1.73.1", "mypy>=1.19.1"]
 test = ["pytest>=8.1.0,<9.0.0", "pytest-timeout>=2.4.0,<3.0.0", "pytest-cov>=5.0.0,<8.0.0", "mock-serial>=0.0.1,<0.1.0 ; sys_platform != 'win32'"]
 video_benchmark = ["scikit-image>=0.23.2,<0.26.0", "pandas>=2.2.2,<2.4.0"]

@@ -159,6 +168,7 @@ all = [
    "lerobot[reachy2]",
    "lerobot[kinematics]",
    "lerobot[intelrealsense]",
+    # "lerobot[wallx]",
    "lerobot[pi]",
    "lerobot[smolvla]",
    # "lerobot[groot]", TODO(Steven): Gr00t requires specific installation instructions for flash-attn
@@ -173,6 +183,8 @@ all = [
    "lerobot[phone]",
    "lerobot[libero]",
    "lerobot[metaworld]",
+    "lerobot[sarm]",
+    "lerobot[peft]",
 ]

 [project.scripts]
@@ -227,6 +239,7 @@ ignore = [

 [tool.ruff.lint.per-file-ignores]
 "__init__.py" = ["F401", "F403"]
+"src/lerobot/policies/wall_x/**" = ["N801", "N812", "SIM102", "SIM108", "SIM210", "SIM211", "B006", "B007", "SIM118"] # Supprese these as they are coming from original Qwen2_5_vl code TODO(pepijn): refactor original

 [tool.ruff.lint.isort]
 combine-as-imports = true
@@ -263,6 +276,7 @@ default.extend-ignore-identifiers-re = [
    "ein",
    "thw",
    "inpt",
+    "ROBOTIS",
 ]

 # TODO: Uncomment when ready to use
@@ -317,9 +331,9 @@ disallow_untyped_defs = true
 disallow_incomplete_defs = true
 check_untyped_defs = true

-# [[tool.mypy.overrides]]
-# module = "lerobot.optim.*"
-# ignore_errors = false
+[[tool.mypy.overrides]]
+module = "lerobot.optim.*"
+ignore_errors = false

 [[tool.mypy.overrides]]
 module = "lerobot.model.*"
@@ -369,3 +383,40 @@ ignore_errors = false
 # [[tool.mypy.overrides]]
 # module = "lerobot.scripts.*"
 # ignore_errors = false
+
+[tool.uv]
+# wallx requires transformers==4.49.0 which conflicts with other extras that need >=4.53.0
+conflicts = [
+    [
+        { extra = "wallx" },
+        { extra = "transformers-dep" },
+    ],
+    [
+        { extra = "wallx" },
+        { extra = "pi" },
+    ],
+    [
+        { extra = "wallx" },
+        { extra = "smolvla" },
+    ],
+    [
+        { extra = "wallx" },
+        { extra = "groot" },
+    ],
+    [
+        { extra = "wallx" },
+        { extra = "xvla" },
+    ],
+    [
+        { extra = "wallx" },
+        { extra = "hilserl" },
+    ],
+    [
+        { extra = "wallx" },
+        { extra = "libero" },
+    ],
+    [
+        { extra = "wallx" },
+        { extra = "all" },
+    ],
+]
@@ -26,4 +26,4 @@ DEFAULT_OBS_QUEUE_TIMEOUT = 2
 SUPPORTED_POLICIES = ["act", "smolvla", "diffusion", "tdmpc", "vqbet", "pi0", "pi05"]

 # TODO: Add all other robots
-SUPPORTED_ROBOTS = ["so100_follower", "so101_follower", "bi_so100_follower"]
+SUPPORTED_ROBOTS = ["so100_follower", "so101_follower", "bi_so100_follower", "omx_follower"]
@@ -54,6 +54,7 @@ from lerobot.robots import (  # noqa: F401
    bi_so100_follower,
    koch_follower,
    make_robot_from_config,
+    omx_follower,
    so100_follower,
    so101_follower,
 )
@@ -67,3 +67,31 @@ class EvalConfig:
                f"to increase the number of episodes to match the batch size (e.g. `eval.n_episodes={self.batch_size}`), "
                f"or lower the batch size (e.g. `eval.batch_size={self.n_episodes}`)."
            )
+
+
+@dataclass
+class PeftConfig:
+    # PEFT offers many fine-tuning methods, layer adapters being the most common and currently also the most
+    # effective methods so we'll focus on those in this high-level config interface.
+
+    # Either a string (module name suffix or 'all-linear'), a list of module name suffixes or a regular expression
+    # describing module names to target with the configured PEFT method. Some policies have a default value for this
+    # so that you don't *have* to choose which layers to adapt but it might still be worthwhile depending on your case.
+    target_modules: list[str] | str | None = None
+
+    # Names/suffixes of modules to fully fine-tune and store alongside adapter weights. Useful for layers that are
+    # not part of a pre-trained model (e.g., action state projections). Depending on the policy this defaults to layers
+    # that are newly created in pre-trained policies. If you're fine-tuning an already trained policy you might want
+    # to set this to `[]`. Corresponds to PEFT's `modules_to_save`.
+    full_training_modules: list[str] | None = None
+
+    # The PEFT (adapter) method to apply to the policy. Needs to be a valid PEFT type.
+    method_type: str = "LORA"
+
+    # Adapter initialization method. Look at the specific PEFT adapter documentation for defaults.
+    init_type: str | None = None
+
+    # We expect that all PEFT adapters are in some way doing rank-decomposition therefore this parameter specifies
+    # the rank used for the adapter. In general a higher rank means more trainable parameters and closer to full
+    # fine-tuning.
+    r: int = 16
@@ -55,14 +55,18 @@ class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):  # type: igno

    n_obs_steps: int = 1

-    input_features: dict[str, PolicyFeature] = field(default_factory=dict)
-    output_features: dict[str, PolicyFeature] = field(default_factory=dict)
+    # `input_features` can be set to None/null in order to infer those values from the dataset.
+    input_features: dict[str, PolicyFeature] | None = field(default_factory=dict)
+    output_features: dict[str, PolicyFeature] | None = field(default_factory=dict)

    device: str | None = None  # e.g. "cuda", "cuda:0", "cpu", or "mps"
    # `use_amp` determines whether to use Automatic Mixed Precision (AMP) for training and evaluation. With AMP,
    # automatic gradient scaling is used.
    use_amp: bool = False

+    # Whether the policy employed PEFT for training.
+    use_peft: bool = False
+
    push_to_hub: bool = True  # type: ignore[assignment] # TODO: use a different name to avoid override
    repo_id: str | None = None

@@ -125,6 +129,8 @@ class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):  # type: igno

    @property
    def robot_state_feature(self) -> PolicyFeature | None:
+        if self.input_features is None:
+            return None
        for ft_name, ft in self.input_features.items():
            if ft.type is FeatureType.STATE and ft_name == OBS_STATE:
                return ft
@@ -132,6 +138,8 @@ class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):  # type: igno

    @property
    def env_state_feature(self) -> PolicyFeature | None:
+        if self.input_features is None:
+            return None
        for _, ft in self.input_features.items():
            if ft.type is FeatureType.ENV:
                return ft
@@ -139,10 +147,14 @@ class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC):  # type: igno

    @property
    def image_features(self) -> dict[str, PolicyFeature]:
+        if self.input_features is None:
+            return {}
        return {key: ft for key, ft in self.input_features.items() if ft.type is FeatureType.VISUAL}

    @property
    def action_feature(self) -> PolicyFeature | None:
+        if self.output_features is None:
+            return None
        for ft_name, ft in self.output_features.items():
            if ft.type is FeatureType.ACTION and ft_name == ACTION:
                return ft
@@ -24,7 +24,7 @@ from huggingface_hub.errors import HfHubHTTPError

 from lerobot import envs
 from lerobot.configs import parser
-from lerobot.configs.default import DatasetConfig, EvalConfig, WandBConfig
+from lerobot.configs.default import DatasetConfig, EvalConfig, PeftConfig, WandBConfig
 from lerobot.configs.policies import PreTrainedConfig
 from lerobot.optim import OptimizerConfig
 from lerobot.optim.schedulers import LRSchedulerConfig
@@ -56,6 +56,7 @@ class TrainPipelineConfig(HubMixin):
    steps: int = 100_000
    eval_freq: int = 20_000
    log_freq: int = 200
+    tolerance_s: float = 1e-4
    save_checkpoint: bool = True
    # Checkpoint is saved every `save_freq` training iterations and after the last training step.
    save_freq: int = 20_000
@@ -64,9 +65,18 @@ class TrainPipelineConfig(HubMixin):
    scheduler: LRSchedulerConfig | None = None
    eval: EvalConfig = field(default_factory=EvalConfig)
    wandb: WandBConfig = field(default_factory=WandBConfig)
-    checkpoint_path: Path | None = field(init=False, default=None)
+    peft: PeftConfig | None = None
+
+    # RA-BC (Reward-Aligned Behavior Cloning) parameters
+    use_rabc: bool = False  # Enable reward-weighted training
+    rabc_progress_path: str | None = None  # Path to precomputed SARM progress parquet file
+    rabc_kappa: float = 0.01  # Hard threshold for high-quality samples
+    rabc_epsilon: float = 1e-6  # Small constant for numerical stability
+    rabc_head_mode: str | None = "sparse"  # For dual-head models: "sparse" or "dense"
+
    # Rename map for the observation to override the image and state keys
    rename_map: dict[str, str] = field(default_factory=dict)
+    checkpoint_path: Path | None = field(init=False, default=None)

    def validate(self) -> None:
        # HACK: We parse again the cli args here to get the pretrained paths if there was some.
@@ -130,6 +140,14 @@ class TrainPipelineConfig(HubMixin):
                "'policy.repo_id' argument missing. Please specify it to push the model to the hub."
            )

+        if self.use_rabc and not self.rabc_progress_path:
+            # Auto-detect from dataset path
+            repo_id = self.dataset.repo_id
+            if self.dataset.root:
+                self.rabc_progress_path = str(Path(self.dataset.root) / "sarm_progress.parquet")
+            else:
+                self.rabc_progress_path = f"hf://datasets/{repo_id}/sarm_progress.parquet"
+
    @classmethod
    def __get_path_fields__(cls) -> list[str]:
        """This enables the parser to load config from the policy using `--policy.path=local/dir`"""
@@ -0,0 +1,13 @@
+# 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.
@@ -0,0 +1,13 @@
+# 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.
@@ -98,6 +98,7 @@ def make_dataset(cfg: TrainPipelineConfig) -> LeRobotDataset | MultiLeRobotDatas
                image_transforms=image_transforms,
                revision=cfg.dataset.revision,
                video_backend=cfg.dataset.video_backend,
+                tolerance_s=cfg.tolerance_s,
            )
        else:
            dataset = StreamingLeRobotDataset(
@@ -108,6 +109,7 @@ def make_dataset(cfg: TrainPipelineConfig) -> LeRobotDataset | MultiLeRobotDatas
                image_transforms=image_transforms,
                revision=cfg.dataset.revision,
                max_num_shards=cfg.num_workers,
+                tolerance_s=cfg.tolerance_s,
            )
    else:
        raise NotImplementedError("The MultiLeRobotDataset isn't supported for now.")
@@ -35,6 +35,8 @@ def make_optimizer_and_scheduler(
        tuple[Optimizer, LRScheduler | None]: The couple (Optimizer, Scheduler). Scheduler can be `None`.
    """
    params = policy.get_optim_params() if cfg.use_policy_training_preset else policy.parameters()
+    if cfg.optimizer is None:
+        raise ValueError("Optimizer config is required but not provided in TrainPipelineConfig")
    optimizer = cfg.optimizer.build(params)
    lr_scheduler = cfg.scheduler.build(optimizer, cfg.steps) if cfg.scheduler is not None else None
    return optimizer, lr_scheduler
@@ -14,6 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import abc
+from collections.abc import Iterable
 from dataclasses import asdict, dataclass, field
 from pathlib import Path
 from typing import Any
@@ -29,6 +30,17 @@ from lerobot.utils.constants import (
 )
 from lerobot.utils.io_utils import deserialize_json_into_object

+# Type alias for parameters accepted by optimizer build() methods.
+# This matches PyTorch's optimizer signature while also supporting:
+# - dict[str, Parameter]: Named parameters for differential LR by name (e.g., XVLA)
+# - dict[str, Iterable]: Multiple parameter groups for multi-optimizer configs (e.g., SAC)
+OptimizerParams = (
+    Iterable[torch.nn.Parameter]  # From model.parameters()
+    | Iterable[dict[str, Any]]  # List of param groups with lr/weight_decay overrides
+    | dict[str, torch.nn.Parameter]  # From dict(model.named_parameters()) for name-based LR
+    | dict[str, Any]  # For multi-optimizer configs (SAC) with multiple param groups
+)
+

@dataclass
 class OptimizerConfig(draccus.ChoiceRegistry, abc.ABC):
@@ -45,13 +57,24 @@ class OptimizerConfig(draccus.ChoiceRegistry, abc.ABC):
        return "adam"

    @abc.abstractmethod
-    def build(self) -> torch.optim.Optimizer | dict[str, torch.optim.Optimizer]:
+    def build(self, params: OptimizerParams) -> torch.optim.Optimizer | dict[str, torch.optim.Optimizer]:
        """
        Build the optimizer. It can be a single optimizer or a dictionary of optimizers.
+
        NOTE: Multiple optimizers are useful when you have different models to optimize.
        For example, you can have one optimizer for the policy and another one for the value function
        in reinforcement learning settings.

+        Args:
+            params: Parameters to optimize. Accepts multiple formats depending on the optimizer:
+                - Iterable[Parameter]: From model.parameters() - standard PyTorch usage
+                - Iterable[dict]: List of param groups with 'params' key and optional
+                  'lr', 'weight_decay' overrides (e.g., ACT, VQBeT policies)
+                - dict[str, Parameter]: From dict(model.named_parameters()) for optimizers
+                  that apply differential learning rates by parameter name (e.g., XVLA)
+                - dict[str, Iterable]: For multi-optimizer configs where each key maps to
+                  a separate optimizer's parameters (e.g., SAC with actor/critic/temperature)
+
        Returns:
            The optimizer or a dictionary of optimizers.
        """
@@ -67,7 +90,7 @@ class AdamConfig(OptimizerConfig):
    weight_decay: float = 0.0
    grad_clip_norm: float = 10.0

-    def build(self, params: dict) -> torch.optim.Optimizer:
+    def build(self, params: OptimizerParams) -> torch.optim.Optimizer:
        kwargs = asdict(self)
        kwargs.pop("grad_clip_norm")
        return torch.optim.Adam(params, **kwargs)
@@ -82,7 +105,7 @@ class AdamWConfig(OptimizerConfig):
    weight_decay: float = 1e-2
    grad_clip_norm: float = 10.0

-    def build(self, params: dict) -> torch.optim.Optimizer:
+    def build(self, params: OptimizerParams) -> torch.optim.Optimizer:
        kwargs = asdict(self)
        kwargs.pop("grad_clip_norm")
        return torch.optim.AdamW(params, **kwargs)
@@ -98,7 +121,7 @@ class SGDConfig(OptimizerConfig):
    weight_decay: float = 0.0
    grad_clip_norm: float = 10.0

-    def build(self, params: dict) -> torch.optim.Optimizer:
+    def build(self, params: OptimizerParams) -> torch.optim.Optimizer:
        kwargs = asdict(self)
        kwargs.pop("grad_clip_norm")
        return torch.optim.SGD(params, **kwargs)
@@ -139,21 +162,19 @@ class XVLAAdamWConfig(OptimizerConfig):
    soft_prompt_lr_scale: float = 1.0  # Scale factor for soft-prompt LR (1.0 = same as base LR)
    soft_prompt_warmup_lr_scale: float | None = None  # If set, start soft-prompts at this scale (e.g., 0.01)

-    def build(self, params: dict) -> torch.optim.Optimizer:
+    def build(self, params: OptimizerParams) -> torch.optim.Optimizer:
        """
        Build AdamW optimizer with differential learning rates.

-        Expects `named_parameters()` as input (dict of name -> param).
-        Applies:
-        - lr * 0.1 for all VLM-related parameters
-        - lr * soft_prompt_lr_scale for soft-prompt parameters (with optional warmup)
-        - full lr for all other parameters
-
        Args:
-            params: Dictionary of parameter names to parameters (from named_parameters())
+            params: Must be a dict[str, Parameter] from dict(model.named_parameters())
+                or equivalent.

        Returns:
            AdamW optimizer with parameter groups for VLM, soft-prompts, and other components
+
+        Raises:
+            AssertionError: If params is not a dict (e.g., from model.parameters())
        """
        assert isinstance(params, dict), "Custom LR optimizer requires `named_parameters()` as inputs."

@@ -174,7 +195,7 @@ class XVLAAdamWConfig(OptimizerConfig):
            # Start at warmup scale, scheduler will warm up to soft_prompt_lr
            soft_prompt_lr = self.lr * self.soft_prompt_warmup_lr_scale

-        param_groups = [
+        param_groups: list[dict[str, Any]] = [
            {
                "params": vlm_group,
                "lr": self.lr * 0.1,
@@ -224,19 +245,25 @@ class MultiAdamConfig(OptimizerConfig):
    grad_clip_norm: float = 10.0
    optimizer_groups: dict[str, dict[str, Any]] = field(default_factory=dict)

-    def build(self, params_dict: dict[str, list]) -> dict[str, torch.optim.Optimizer]:
+    def build(self, params: OptimizerParams) -> dict[str, torch.optim.Optimizer]:
        """Build multiple Adam optimizers.

        Args:
-            params_dict: Dictionary mapping parameter group names to lists of parameters
-                         The keys should match the keys in optimizer_groups
+            params: Must be a dict[str, Iterable[Parameter]] mapping parameter group names
+                to iterables of parameters. The keys should match the keys in optimizer_groups.
+                Typically from policies that need separate optimizers (e.g., SAC with
+                actor/critic/temperature).

        Returns:
            Dictionary mapping parameter group names to their optimizers
+
+        Raises:
+            AssertionError: If params is not a dict
        """
+        assert isinstance(params, dict), "MultiAdamConfig requires a dict of parameter groups as inputs."
        optimizers = {}

-        for name, params in params_dict.items():
+        for name, group_params in params.items():
            # Get group-specific hyperparameters or use defaults
            group_config = self.optimizer_groups.get(name, {})

@@ -248,7 +275,7 @@ class MultiAdamConfig(OptimizerConfig):
                "weight_decay": group_config.get("weight_decay", self.weight_decay),
            }

-            optimizers[name] = torch.optim.Adam(params, **optimizer_kwargs)
+            optimizers[name] = torch.optim.Adam(group_params, **optimizer_kwargs)

        return optimizers

@@ -30,7 +30,7 @@ from lerobot.utils.io_utils import deserialize_json_into_object

@dataclass
 class LRSchedulerConfig(draccus.ChoiceRegistry, abc.ABC):
-    num_warmup_steps: int
+    num_warmup_steps: int | None

    @property
    def type(self) -> str:
@@ -21,6 +21,7 @@ from .smolvla.configuration_smolvla import SmolVLAConfig as SmolVLAConfig
 from .smolvla.processor_smolvla import SmolVLANewLineProcessor
 from .tdmpc.configuration_tdmpc import TDMPCConfig as TDMPCConfig
 from .vqbet.configuration_vqbet import VQBeTConfig as VQBeTConfig
+from .wall_x.configuration_wall_x import WallXConfig as WallXConfig
 from .xvla.configuration_xvla import XVLAConfig as XVLAConfig

 __all__ = [
@@ -29,8 +30,10 @@ __all__ = [
    "PI0Config",
    "PI05Config",
    "SmolVLAConfig",
+    "SARMConfig",
    "TDMPCConfig",
    "VQBeTConfig",
    "GrootConfig",
    "XVLAConfig",
+    "WallXConfig",
 ]
@@ -50,6 +50,7 @@ class ACTPolicy(PreTrainedPolicy):
    def __init__(
        self,
        config: ACTConfig,
+        **kwargs,
    ):
        """
        Args:
@@ -56,6 +56,7 @@ class DiffusionPolicy(PreTrainedPolicy):
    def __init__(
        self,
        config: DiffusionConfig,
+        **kwargs,
    ):
        """
        Args:
@@ -37,10 +37,12 @@ from lerobot.policies.pi05.configuration_pi05 import PI05Config
 from lerobot.policies.pretrained import PreTrainedPolicy
 from lerobot.policies.sac.configuration_sac import SACConfig
 from lerobot.policies.sac.reward_model.configuration_classifier import RewardClassifierConfig
+from lerobot.policies.sarm.configuration_sarm import SARMConfig
 from lerobot.policies.smolvla.configuration_smolvla import SmolVLAConfig
 from lerobot.policies.tdmpc.configuration_tdmpc import TDMPCConfig
 from lerobot.policies.utils import validate_visual_features_consistency
 from lerobot.policies.vqbet.configuration_vqbet import VQBeTConfig
+from lerobot.policies.wall_x.configuration_wall_x import WallXConfig
 from lerobot.policies.xvla.configuration_xvla import XVLAConfig
 from lerobot.processor import PolicyAction, PolicyProcessorPipeline
 from lerobot.processor.converters import (
@@ -61,7 +63,7 @@ def get_policy_class(name: str) -> type[PreTrainedPolicy]:

    Args:
        name: The name of the policy. Supported names are "tdmpc", "diffusion", "act",
-              "vqbet", "pi0", "pi05", "sac", "reward_classifier", "smolvla".
+              "vqbet", "pi0", "pi05", "sac", "reward_classifier", "smolvla", "wall_x".

    Returns:
        The policy class corresponding to the given name.
@@ -105,6 +107,10 @@ def get_policy_class(name: str) -> type[PreTrainedPolicy]:
        from lerobot.policies.smolvla.modeling_smolvla import SmolVLAPolicy

        return SmolVLAPolicy
+    elif name == "sarm":
+        from lerobot.policies.sarm.modeling_sarm import SARMRewardModel
+
+        return SARMRewardModel
    elif name == "groot":
        from lerobot.policies.groot.modeling_groot import GrootPolicy

@@ -113,6 +119,10 @@ def get_policy_class(name: str) -> type[PreTrainedPolicy]:
        from lerobot.policies.xvla.modeling_xvla import XVLAPolicy

        return XVLAPolicy
+    elif name == "wall_x":
+        from lerobot.policies.wall_x.modeling_wall_x import WallXPolicy
+
+        return WallXPolicy
    else:
        try:
            return _get_policy_cls_from_policy_name(name=name)
@@ -130,7 +140,7 @@ 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", "pi05", "sac", "smolvla",
-                     "reward_classifier".
+                     "reward_classifier", "wall_x".
        **kwargs: Keyword arguments to be passed to the configuration class constructor.

    Returns:
@@ -161,6 +171,8 @@ def make_policy_config(policy_type: str, **kwargs) -> PreTrainedConfig:
        return GrootConfig(**kwargs)
    elif policy_type == "xvla":
        return XVLAConfig(**kwargs)
+    elif policy_type == "wall_x":
+        return WallXConfig(**kwargs)
    else:
        try:
            config_cls = PreTrainedConfig.get_choice_class(policy_type)
@@ -337,6 +349,14 @@ def make_pre_post_processors(
            dataset_stats=kwargs.get("dataset_stats"),
        )

+    elif isinstance(policy_cfg, SARMConfig):
+        from lerobot.policies.sarm.processor_sarm import make_sarm_pre_post_processors
+
+        processors = make_sarm_pre_post_processors(
+            config=policy_cfg,
+            dataset_stats=kwargs.get("dataset_stats"),
+            dataset_meta=kwargs.get("dataset_meta"),
+        )
    elif isinstance(policy_cfg, GrootConfig):
        from lerobot.policies.groot.processor_groot import make_groot_pre_post_processors

@@ -344,6 +364,7 @@ def make_pre_post_processors(
            config=policy_cfg,
            dataset_stats=kwargs.get("dataset_stats"),
        )
+
    elif isinstance(policy_cfg, XVLAConfig):
        from lerobot.policies.xvla.processor_xvla import (
            make_xvla_pre_post_processors,
@@ -354,6 +375,14 @@ def make_pre_post_processors(
            dataset_stats=kwargs.get("dataset_stats"),
        )

+    elif isinstance(policy_cfg, WallXConfig):
+        from lerobot.policies.wall_x.processor_wall_x import make_wall_x_pre_post_processors
+
+        processors = make_wall_x_pre_post_processors(
+            config=policy_cfg,
+            dataset_stats=kwargs.get("dataset_stats"),
+        )
+
    else:
        try:
            processors = _make_processors_from_policy_config(
@@ -435,11 +464,47 @@ def make_policy(
        cfg.input_features = {key: ft for key, ft in features.items() if key not in cfg.output_features}
    kwargs["config"] = cfg

-    if cfg.pretrained_path:
+    # Pass dataset_stats to the policy if available (needed for some policies like SARM)
+    if ds_meta is not None and hasattr(ds_meta, "stats"):
+        kwargs["dataset_stats"] = ds_meta.stats
+
+    if ds_meta is not None:
+        kwargs["dataset_meta"] = ds_meta
+
+    if not cfg.pretrained_path and cfg.use_peft:
+        raise ValueError(
+            "Instantiating a policy with `use_peft=True` without a checkpoint is not supported since that requires "
+            "the PEFT config parameters to be set. For training with PEFT, see `lerobot_train.py` on how to do that."
+        )
+
+    if cfg.pretrained_path and not cfg.use_peft:
        # Load a pretrained policy and override the config if needed (for example, if there are inference-time
        # hyperparameters that we want to vary).
        kwargs["pretrained_name_or_path"] = cfg.pretrained_path
        policy = policy_cls.from_pretrained(**kwargs)
+    elif cfg.pretrained_path and cfg.use_peft:
+        # Load a pretrained PEFT model on top of the policy. The pretrained path points to the folder/repo
+        # of the adapter and the adapter's config contains the path to the base policy. So we need the
+        # adapter config first, then load the correct policy and then apply PEFT.
+        from peft import PeftConfig, PeftModel
+
+        logging.info("Loading policy's PEFT adapter.")
+
+        peft_pretrained_path = cfg.pretrained_path
+        peft_config = PeftConfig.from_pretrained(peft_pretrained_path)
+
+        kwargs["pretrained_name_or_path"] = peft_config.base_model_name_or_path
+        if not kwargs["pretrained_name_or_path"]:
+            # This means that there's a bug or we trained a policy from scratch using PEFT.
+            # It is more likely that this is a bug so we'll raise an error.
+            raise ValueError(
+                "No pretrained model name found in adapter config. Can't instantiate the pre-trained policy on which "
+                "the adapter was trained."
+            )
+
+        policy = policy_cls.from_pretrained(**kwargs)
+        policy = PeftModel.from_pretrained(policy, peft_pretrained_path, config=peft_config)
+
    else:
        # Make a fresh policy.
        policy = policy_cls(**kwargs)
@@ -49,7 +49,7 @@ class GrootPolicy(PreTrainedPolicy):
    name = "groot"
    config_class = GrootConfig

-    def __init__(self, config: GrootConfig):
+    def __init__(self, config: GrootConfig, **kwargs):
        """Initialize Groot policy wrapper."""
        super().__init__(config)
        config.validate_features()
@@ -23,6 +23,8 @@ from lerobot.optim.schedulers import CosineDecayWithWarmupSchedulerConfig
 from lerobot.policies.rtc.configuration_rtc import RTCConfig
 from lerobot.utils.constants import OBS_IMAGES

+DEFAULT_IMAGE_SIZE = 224
+

@PreTrainedConfig.register_subclass("pi0")
@dataclass
@@ -51,7 +53,10 @@ class PI0Config(PreTrainedConfig):
    # Real-Time Chunking (RTC) configuration
    rtc_config: RTCConfig | None = None

-    image_resolution: tuple[int, int] = (224, 224)  # see openpi `preprocessing_pytorch.py`
+    image_resolution: tuple[int, int] = (
+        DEFAULT_IMAGE_SIZE,
+        DEFAULT_IMAGE_SIZE,
+    )  # see openpi `preprocessing_pytorch.py`

    # Add empty images. Used to add empty cameras when no image features are present.
    empty_cameras: int = 0
@@ -41,7 +41,7 @@ else:
    PaliGemmaForConditionalGeneration = None

 from lerobot.configs.policies import PreTrainedConfig
-from lerobot.policies.pi0.configuration_pi0 import PI0Config
+from lerobot.policies.pi0.configuration_pi0 import DEFAULT_IMAGE_SIZE, PI0Config
 from lerobot.policies.pretrained import PreTrainedPolicy, T
 from lerobot.policies.rtc.modeling_rtc import RTCProcessor
 from lerobot.utils.constants import (
@@ -337,6 +337,7 @@ class PaliGemmaWithExpertModel(
        action_expert_config,
        use_adarms=None,
        precision: Literal["bfloat16", "float32"] = "bfloat16",
+        image_size: int = DEFAULT_IMAGE_SIZE,
    ):
        if use_adarms is None:
            use_adarms = [False, False]
@@ -356,6 +357,7 @@ class PaliGemmaWithExpertModel(
        vlm_config_hf.text_config.vocab_size = 257152
        vlm_config_hf.text_config.use_adarms = use_adarms[0]
        vlm_config_hf.text_config.adarms_cond_dim = vlm_config.width if use_adarms[0] else None
+        vlm_config_hf.vision_config.image_size = image_size
        vlm_config_hf.vision_config.intermediate_size = 4304
        vlm_config_hf.vision_config.projection_dim = 2048
        vlm_config_hf.vision_config.projector_hidden_act = "gelu_fast"
@@ -519,11 +521,17 @@ class PI0Pytorch(nn.Module):  # see openpi `PI0Pytorch`
        paligemma_config = get_gemma_config(config.paligemma_variant)
        action_expert_config = get_gemma_config(config.action_expert_variant)

+        if config.image_resolution[0] != config.image_resolution[1]:
+            raise ValueError(
+                f"PaliGemma expects square image resolution, invalid resolution: {config.image_resolution}"
+            )
+
        self.paligemma_with_expert = PaliGemmaWithExpertModel(
            paligemma_config,
            action_expert_config,
            use_adarms=[False, False],
            precision=config.dtype,
+            image_size=config.image_resolution[0],
        )

        self.action_in_proj = nn.Linear(config.max_action_dim, action_expert_config.width)
@@ -812,16 +820,13 @@ class PI0Pytorch(nn.Module):  # see openpi `PI0Pytorch`
        )

        dt = -1.0 / num_steps
-        dt = torch.tensor(dt, dtype=torch.float32, device=device)

        x_t = noise
-        time = torch.tensor(1.0, dtype=torch.float32, device=device)
-        while time >= -dt / 2:
-            expanded_time = time.expand(bsize)
+        for step in range(num_steps):
+            time = 1.0 + step * dt
+            time_tensor = torch.tensor(time, dtype=torch.float32, device=device).expand(bsize)

-            # Define a closure function to properly capture expanded_time
-            # This avoids the lambda expression (E731) and loop variable binding (B023) issues
-            def denoise_step_partial_call(input_x_t, current_timestep=expanded_time):
+            def denoise_step_partial_call(input_x_t, current_timestep=time_tensor):
                return self.denoise_step(
                    state=state,
                    prefix_pad_masks=prefix_pad_masks,
@@ -846,15 +851,11 @@ class PI0Pytorch(nn.Module):  # see openpi `PI0Pytorch`
            else:
                v_t = denoise_step_partial_call(x_t)

-            # Euler step
-            x_t += dt * v_t
+            x_t = x_t + dt * v_t

-            # Record x_t and v_t after Euler step
            if self.rtc_processor is not None and self.rtc_processor.is_debug_enabled():
                self.rtc_processor.track(time=time, x_t=x_t, v_t=v_t)

-            time += dt
-
        return x_t

    def denoise_step(
@@ -906,6 +907,7 @@ class PI0Policy(PreTrainedPolicy):
    def __init__(
        self,
        config: PI0Config,
+        **kwargs,
    ):
        """
        Args:
@@ -1234,9 +1236,15 @@ class PI0Policy(PreTrainedPolicy):

        return actions

-    def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict]:
-        """Run the batch through the model and compute the loss for training."""
+    def forward(self, batch: dict[str, Tensor], reduction: str = "mean") -> tuple[Tensor, dict]:
+        """Run the batch through the model and compute the loss for training.

+        Args:
+            batch: Training batch containing observations and actions.
+            reduction: How to reduce the loss. Options:
+                - "mean": Return scalar mean loss (default, backward compatible)
+                - "none": Return per-sample losses of shape (batch_size,) for RA-BC weighting
+        """
        # Prepare inputs
        images, img_masks = self._preprocess_images(batch)
        lang_tokens, lang_masks = batch[f"{OBS_LANGUAGE_TOKENS}"], batch[f"{OBS_LANGUAGE_ATTENTION_MASK}"]
@@ -1250,11 +1258,17 @@ class PI0Policy(PreTrainedPolicy):
        original_action_dim = self.config.output_features[ACTION].shape[0]
        losses = losses[:, :, :original_action_dim]

-        loss = losses.mean()
-
        loss_dict = {
-            "loss": loss.item(),
            "loss_per_dim": losses.mean(dim=[0, 1]).detach().cpu().numpy().tolist(),
        }

-        return loss, loss_dict
+        if reduction == "none":
+            # Return per-sample losses (B,) by averaging over time and action dims
+            per_sample_loss = losses.mean(dim=(1, 2))
+            loss_dict["loss"] = per_sample_loss.mean().item()
+            return per_sample_loss, loss_dict
+        else:
+            # Default: return scalar mean loss
+            loss = losses.mean()
+            loss_dict["loss"] = loss.item()
+            return loss, loss_dict
@@ -22,6 +22,8 @@ from lerobot.optim.optimizers import AdamWConfig
 from lerobot.optim.schedulers import CosineDecayWithWarmupSchedulerConfig
 from lerobot.policies.rtc.configuration_rtc import RTCConfig

+DEFAULT_IMAGE_SIZE = 224
+

@PreTrainedConfig.register_subclass("pi05")
@dataclass
@@ -50,7 +52,10 @@ class PI05Config(PreTrainedConfig):
    # Real-Time Chunking (RTC) configuration
    rtc_config: RTCConfig | None = None

-    image_resolution: tuple[int, int] = (224, 224)  # see openpi `preprocessing_pytorch.py`
+    image_resolution: tuple[int, int] = (
+        DEFAULT_IMAGE_SIZE,
+        DEFAULT_IMAGE_SIZE,
+    )  # see openpi `preprocessing_pytorch.py`

    # Add empty images. Used to add empty cameras when no image features are present.
    empty_cameras: int = 0
@@ -41,7 +41,7 @@ else:
    PaliGemmaForConditionalGeneration = None

 from lerobot.configs.policies import PreTrainedConfig
-from lerobot.policies.pi05.configuration_pi05 import PI05Config
+from lerobot.policies.pi05.configuration_pi05 import DEFAULT_IMAGE_SIZE, PI05Config
 from lerobot.policies.pretrained import PreTrainedPolicy, T
 from lerobot.policies.rtc.modeling_rtc import RTCProcessor
 from lerobot.utils.constants import (
@@ -336,6 +336,7 @@ class PaliGemmaWithExpertModel(
        action_expert_config,
        use_adarms=None,
        precision: Literal["bfloat16", "float32"] = "bfloat16",
+        image_size: int = DEFAULT_IMAGE_SIZE,
    ):
        if use_adarms is None:
            use_adarms = [False, False]
@@ -355,6 +356,7 @@ class PaliGemmaWithExpertModel(
        vlm_config_hf.text_config.vocab_size = 257152
        vlm_config_hf.text_config.use_adarms = use_adarms[0]
        vlm_config_hf.text_config.adarms_cond_dim = vlm_config.width if use_adarms[0] else None
+        vlm_config_hf.vision_config.image_size = image_size
        vlm_config_hf.vision_config.intermediate_size = 4304
        vlm_config_hf.vision_config.projection_dim = 2048
        vlm_config_hf.vision_config.projector_hidden_act = "gelu_fast"
@@ -518,11 +520,17 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
        paligemma_config = get_gemma_config(config.paligemma_variant)
        action_expert_config = get_gemma_config(config.action_expert_variant)

+        if config.image_resolution[0] != config.image_resolution[1]:
+            raise ValueError(
+                f"PaliGemma expects square image resolution, invalid resolution: {config.image_resolution}"
+            )
+
        self.paligemma_with_expert = PaliGemmaWithExpertModel(
            paligemma_config,
            action_expert_config,
            use_adarms=[False, True],
            precision=config.dtype,
+            image_size=config.image_resolution[0],
        )

        self.action_in_proj = nn.Linear(config.max_action_dim, action_expert_config.width)
@@ -787,16 +795,13 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
        )

        dt = -1.0 / num_steps
-        dt = torch.tensor(dt, dtype=torch.float32, device=device)

        x_t = noise
-        time = torch.tensor(1.0, dtype=torch.float32, device=device)
-        while time >= -dt / 2:
-            expanded_time = time.expand(bsize)
+        for step in range(num_steps):
+            time = 1.0 + step * dt
+            time_tensor = torch.tensor(time, dtype=torch.float32, device=device).expand(bsize)

-            # Define a closure function to properly capture expanded_time
-            # This avoids the lambda expression (E731) and loop variable binding (B023) issues
-            def denoise_step_partial_call(input_x_t, current_timestep=expanded_time):
+            def denoise_step_partial_call(input_x_t, current_timestep=time_tensor):
                return self.denoise_step(
                    prefix_pad_masks=prefix_pad_masks,
                    past_key_values=past_key_values,
@@ -820,15 +825,11 @@ class PI05Pytorch(nn.Module):  # see openpi `PI0Pytorch`
            else:
                v_t = denoise_step_partial_call(x_t)

-            # Euler step
-            x_t += dt * v_t
+            x_t = x_t + dt * v_t

-            # Record x_t and v_t after Euler step
            if self.rtc_processor is not None and self.rtc_processor.is_debug_enabled():
                self.rtc_processor.track(time=time, x_t=x_t, v_t=v_t)

-            time += dt
-
        return x_t

    def denoise_step(
@@ -879,6 +880,7 @@ class PI05Policy(PreTrainedPolicy):
    def __init__(
        self,
        config: PI05Config,
+        **kwargs,
    ):
        """
        Args:
@@ -1208,9 +1210,15 @@ class PI05Policy(PreTrainedPolicy):

        return actions

-    def forward(self, batch: dict[str, Tensor]) -> tuple[Tensor, dict]:
-        """Run the batch through the model and compute the loss for training."""
+    def forward(self, batch: dict[str, Tensor], reduction: str = "mean") -> tuple[Tensor, dict]:
+        """Run the batch through the model and compute the loss for training.

+        Args:
+            batch: Training batch containing observations and actions.
+            reduction: How to reduce the loss. Options:
+                - "mean": Return scalar mean loss (default, backward compatible)
+                - "none": Return per-sample losses of shape (batch_size,) for RA-BC weighting
+        """
        # Prepare inputs
        images, img_masks = self._preprocess_images(batch)
        tokens, masks = batch[f"{OBS_LANGUAGE_TOKENS}"], batch[f"{OBS_LANGUAGE_ATTENTION_MASK}"]
@@ -1224,11 +1232,17 @@ class PI05Policy(PreTrainedPolicy):
        original_action_dim = self.config.output_features[ACTION].shape[0]
        losses = losses[:, :, :original_action_dim]

-        loss = losses.mean()
-
        loss_dict = {
-            "loss": loss.item(),
            "loss_per_dim": losses.mean(dim=[0, 1]).detach().cpu().numpy().tolist(),
        }

-        return loss, loss_dict
+        if reduction == "none":
+            # Return per-sample losses (B,) by averaging over time and action dims
+            per_sample_loss = losses.mean(dim=(1, 2))
+            loss_dict["loss"] = per_sample_loss.mean().item()
+            return per_sample_loss, loss_dict
+        else:
+            # Default: return scalar mean loss
+            loss = losses.mean()
+            loss_dict["loss"] = loss.item()
+            return loss, loss_dict
@@ -206,6 +206,7 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
    def push_model_to_hub(
        self,
        cfg: TrainPipelineConfig,
+        peft_model=None,
    ):
        api = HfApi()
        repo_id = api.create_repo(
@@ -216,7 +217,14 @@ class PreTrainedPolicy(nn.Module, HubMixin, abc.ABC):
        with TemporaryDirectory(ignore_cleanup_errors=True) as tmp:
            saved_path = Path(tmp) / repo_id

-            self.save_pretrained(saved_path)  # Calls _save_pretrained and stores model tensors
+            if peft_model is not None:
+                # Since PEFT just forwards calls to `push_model_to_hub`, `self` is not the PeftModel wrapper
+                # but the actual policy which is why we need the PEFT model passed to us to save the adapter.
+                # That also means that we need to store the policy config ourselves since PEFT can't.
+                peft_model.save_pretrained(saved_path)
+                self.config.save_pretrained(saved_path)
+            else:
+                self.save_pretrained(saved_path)  # Calls _save_pretrained and stores model tensors

            card = self.generate_model_card(
                cfg.dataset.repo_id, self.config.type, self.config.license, self.config.tags
@@ -0,0 +1,14 @@
+## Paper
+
+https://arxiv.org/abs/2509.25358
+
+## Citation
+
+```bibtex
+@article{chen2025sarm,
+  title={SARM: Stage-Aware Reward Modeling for Long Horizon Robot Manipulation},
+  author={Chen, Qianzhong and Yu, Justin and Schwager, Mac and Abbeel, Pieter and Shentu, Yide and Wu, Philipp},
+  journal={arXiv preprint arXiv:2509.25358},
+  year={2025}
+}
+```
@@ -0,0 +1,870 @@
+#!/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.
+
+"""
+Compute SARM progress values for RA-BC (Reward-Aware Behavior Cloning) weighting.
+
+This script processes all frames in a dataset with SARM to compute progress values [0, 1].
+The results are saved as a parquet file that can be loaded during training for RA-BC weighting.
+
+Uses multi-output extraction: each SARM query returns progress for 9 frames, so we only
+need ~num_frames/30 queries instead of one per frame (~30x speedup).
+
+Usage:
+    # Full RA-BC computation with visualizations
+    python src/lerobot/policies/sarm/compute_rabc_weights.py \\
+        --dataset-repo-id lerobot/aloha_sim_insertion_human \\
+        --reward-model-path pepijn223/sarm_single_uni4
+
+    # Faster computation with stride (compute every 5 frames, interpolate the rest)
+    python src/lerobot/policies/sarm/compute_rabc_weights.py \\
+        --dataset-repo-id lerobot/aloha_sim_insertion_human \\
+        --reward-model-path pepijn223/sarm_single_uni4 \\
+        --stride 5
+
+    # Visualize predictions only (no RA-BC computation)
+    python src/lerobot/policies/sarm/compute_rabc_weights.py \\
+        --dataset-repo-id lerobot/aloha_sim_insertion_human \\
+        --reward-model-path pepijn223/sarm_single_uni4 \\
+        --visualize-only \\
+        --num-visualizations 5
+
+The output is saved to the dataset's local cache directory as 'sarm_progress.parquet'.
+"""
+
+import argparse
+import logging
+from pathlib import Path
+
+import matplotlib.gridspec as gridspec
+import matplotlib.pyplot as plt
+import numpy as np
+import pyarrow as pa
+import pyarrow.parquet as pq
+import torch
+from tqdm import tqdm
+
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.policies.sarm.modeling_sarm import SARMRewardModel
+from lerobot.policies.sarm.processor_sarm import make_sarm_pre_post_processors
+from lerobot.policies.sarm.sarm_utils import normalize_stage_tau
+
+
+def get_reward_model_path_from_parquet(parquet_path: Path) -> str | None:
+    """Read reward_model_path from parquet metadata if available."""
+    if not parquet_path.exists():
+        return None
+    try:
+        metadata = pq.read_metadata(parquet_path).schema.to_arrow_schema().metadata
+        if metadata and b"reward_model_path" in metadata:
+            return metadata[b"reward_model_path"].decode()
+    except Exception:  # nosec B110
+        return None
+    return None
+
+
+def load_sarm_resources(
+    dataset_repo_id: str,
+    reward_model_path: str,
+    device: str = "cuda",
+) -> tuple[LeRobotDataset, SARMRewardModel, any]:
+    """
+    Load SARM model, dataset, and preprocessor.
+
+    Returns:
+        Tuple of (dataset, reward_model, preprocessor)
+    """
+    logging.info(f"Loading model: {reward_model_path}")
+    reward_model = SARMRewardModel.from_pretrained(reward_model_path)
+    reward_model.config.device = device
+    reward_model.to(device).eval()
+
+    image_key = reward_model.config.image_key
+    state_key = reward_model.config.state_key
+    delta_indices = reward_model.config.observation_delta_indices
+
+    logging.info(f"Loading dataset: {dataset_repo_id}")
+    temp_dataset = LeRobotDataset(dataset_repo_id, download_videos=True)
+    fps = temp_dataset.fps
+
+    delta_timestamps = {
+        image_key: [idx / fps for idx in delta_indices],
+        state_key: [idx / fps for idx in delta_indices],
+    }
+    dataset = LeRobotDataset(dataset_repo_id, delta_timestamps=delta_timestamps)
+    logging.info(f"Dataset: {dataset.num_episodes} episodes, {dataset.num_frames} frames")
+
+    preprocess, _ = make_sarm_pre_post_processors(
+        config=reward_model.config,
+        dataset_stats=dataset.meta.stats,
+        dataset_meta=dataset.meta,
+    )
+
+    return dataset, reward_model, preprocess
+
+
+def to_numpy_image(img) -> np.ndarray:
+    """Convert image tensor to numpy uint8 (H, W, C)."""
+    if isinstance(img, torch.Tensor):
+        img = img.cpu().numpy()
+    if img.ndim == 4:
+        # Take center frame for bidirectional sampling
+        img = img[img.shape[0] // 2]
+    if img.shape[0] in [1, 3]:
+        img = np.transpose(img, (1, 2, 0))
+    if img.dtype != np.uint8:
+        # Handle normalized images (may have negative values or values > 1)
+        img = img.astype(np.float32)
+        img = (img - img.min()) / (img.max() - img.min() + 1e-8)  # Normalize to [0, 1]
+        img = (img * 255).astype(np.uint8)
+    return img
+
+
+def visualize_episode(
+    frames, progress_preds, stage_preds, title, output_path, stage_labels, gt_progress=None, gt_stages=None
+):
+    """Create visualization with progress plot, stage probabilities, and sample frames.
+
+    Same as sarm_inference_visualization.py
+    """
+    num_stages = stage_preds.shape[1]
+    colors = plt.cm.tab10(np.linspace(0, 1, num_stages))
+    frame_indices = np.arange(len(progress_preds))
+
+    fig = plt.figure(figsize=(14, 12))
+    gs = gridspec.GridSpec(3, 1, height_ratios=[2, 1, 1], hspace=0.3)
+    ax_progress, ax_stages, ax_frames = fig.add_subplot(gs[0]), fig.add_subplot(gs[1]), fig.add_subplot(gs[2])
+
+    # Progress plot
+    ax_progress.plot(frame_indices, progress_preds, linewidth=2, color="#2E86AB", label="Predicted")
+    ax_progress.fill_between(frame_indices, 0, progress_preds, alpha=0.3, color="#2E86AB")
+    if gt_progress is not None:
+        ax_progress.plot(
+            frame_indices, gt_progress, linewidth=2, color="#28A745", linestyle="--", label="Ground Truth"
+        )
+    ax_progress.axhline(y=1.0, color="gray", linestyle="--", alpha=0.5)
+    ax_progress.set_ylabel("Progress")
+    ax_progress.set_title(f'Task: "{title}"', fontweight="bold")
+    ax_progress.set_ylim(-0.05, 1.1)
+    ax_progress.legend(loc="upper left")
+    ax_progress.grid(True, alpha=0.3)
+
+    # Stage predictions
+    ax_stages.stackplot(
+        frame_indices,
+        *[stage_preds[:, i] for i in range(num_stages)],
+        colors=colors,
+        alpha=0.8,
+        labels=stage_labels,
+    )
+    if gt_stages is not None:
+        for change_idx in np.where(np.diff(gt_stages) != 0)[0] + 1:
+            ax_stages.axvline(x=change_idx, color="black", linestyle="-", alpha=0.7, linewidth=1.5)
+    ax_stages.set_xlabel("Frame")
+    ax_stages.set_ylabel("Stage Probability")
+    ax_stages.set_ylim(0, 1)
+    ax_stages.legend(loc="upper left", ncol=min(num_stages, 5), fontsize=8)
+    ax_stages.grid(True, alpha=0.3)
+
+    # Sample frames
+    ax_frames.axis("off")
+    num_sample = 8
+    sample_indices = np.linspace(0, len(frames) - 1, num_sample, dtype=int)
+    h, w = frames[0].shape[:2]
+    combined = np.zeros((h, w * num_sample, 3), dtype=np.uint8)
+    for i, idx in enumerate(sample_indices):
+        frame = frames[idx]
+        if frame.shape[-1] == 1:
+            frame = np.repeat(frame, 3, axis=-1)
+        combined[:, i * w : (i + 1) * w] = frame
+        stage_name = stage_labels[np.argmax(stage_preds[idx])][:12]
+        ax_frames.text(
+            i * w + w / 2,
+            -10,
+            f"Frame {idx}\n{progress_preds[idx]:.2f}\n{stage_name}",
+            ha="center",
+            va="top",
+            fontsize=7,
+        )
+    ax_frames.imshow(combined)
+    ax_frames.set_title("Sample Frames", pad=20)
+
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    plt.savefig(output_path, dpi=150, bbox_inches="tight")
+    plt.close()
+    print(f"Saved: {output_path}")
+
+
+def visualize_sarm_predictions(
+    dataset: LeRobotDataset,
+    reward_model: SARMRewardModel,
+    preprocess,
+    episode_indices: list[int],
+    head_mode: str,
+    output_dir: Path,
+    num_display_frames: int = 5,
+    stride: int = 1,
+):
+    """
+    Visualize SARM predictions for multiple episodes.
+
+    Computes predictions for every frame by default. With stride > 1, computes predictions
+    every N frames and interpolates (progress + stage probabilities) for visualization.
+
+    Args:
+        dataset: LeRobotDataset with delta_timestamps configured
+        reward_model: Loaded SARM model
+        preprocess: Preprocessor from make_sarm_pre_post_processors
+        episode_indices: List of episode indices to visualize
+        head_mode: "sparse", "dense", or "both"
+        output_dir: Directory to save visualizations
+        num_display_frames: Number of frames to display in thumbnail strip (default: 5)
+        stride: Compute predictions every N frames, interpolate the rest (default: 1)
+    """
+    output_dir = Path(output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    image_key = reward_model.config.image_key
+    state_key = reward_model.config.state_key
+    dual_mode = reward_model.config.uses_dual_heads
+    device = reward_model.device
+
+    # Center frame index for bidirectional sampling
+    target_idx = reward_model.config.n_obs_steps // 2
+
+    # Determine which heads to visualize
+    schemes_to_viz = []
+    if head_mode in ("sparse", "both") or not dual_mode:
+        schemes_to_viz.append("sparse")
+    if head_mode in ("dense", "both") and dual_mode:
+        schemes_to_viz.append("dense")
+
+    # Set preprocessor to eval mode to disable augmentations
+    if hasattr(preprocess, "eval"):
+        preprocess.eval()
+    for step in preprocess.steps:
+        if hasattr(step, "eval"):
+            step.eval()
+
+    for episode_idx in episode_indices:
+        ep = dataset.meta.episodes[episode_idx]
+        ep_start = ep["dataset_from_index"]
+        ep_end = ep["dataset_to_index"]
+        task = dataset[ep_start].get("task", "perform the task")
+        num_frames = ep_end - ep_start
+
+        # Select frames for display thumbnails (evenly sampled from begin to end)
+        display_indices = set(
+            [
+                ep_start + int(i * (num_frames - 1) / (num_display_frames - 1))
+                for i in range(num_display_frames)
+            ]
+            if num_frames >= num_display_frames
+            else list(range(ep_start, ep_end))
+        )
+        viz_frames = {}
+
+        # Load display frames up-front (stride mode might skip them otherwise).
+        for frame_idx in display_indices:
+            sample = dataset[frame_idx]
+            viz_frames[frame_idx] = to_numpy_image(sample[image_key])
+
+        # Initialize storage for each scheme
+        scheme_data = {}
+        for scheme in schemes_to_viz:
+            num_stages = getattr(reward_model.config, f"num_{scheme}_stages")
+            scheme_data[scheme] = {
+                "viz_progress": np.full(num_frames, np.nan),
+                "viz_stages": np.full((num_frames, num_stages), np.nan),
+                "viz_gt_progress": np.full(num_frames, np.nan),
+                "viz_gt_stages": np.full(num_frames, np.nan),
+                "target_key": f"{scheme}_targets",
+                "num_stages": num_stages,
+                "temporal_props": getattr(reward_model.config, f"{scheme}_temporal_proportions"),
+                "subtask_names": getattr(reward_model.config, f"{scheme}_subtask_names"),
+            }
+
+        if stride > 1:
+            logging.info(f"Visualization stride={stride}: inferring every {stride} frames and interpolating")
+
+        # Process frames one at a time to avoid memory buildup
+        frame_indices = list(range(ep_start, ep_end, stride))
+        if (ep_end - 1) not in frame_indices:
+            frame_indices.append(ep_end - 1)
+        frame_indices = sorted(set(frame_indices))
+
+        for frame_idx in tqdm(frame_indices, desc=f"Episode {episode_idx}", leave=False):
+            local_idx = frame_idx - ep_start
+            sample = dataset[frame_idx]
+
+            batch = {
+                image_key: sample[image_key],
+                "task": task,
+                "index": frame_idx,
+                "episode_index": episode_idx,
+            }
+            if state_key in sample:
+                batch[state_key] = sample[state_key]
+
+            with torch.no_grad():
+                processed = preprocess(batch)
+                video_features = processed["video_features"].to(device)
+                text_features = processed["text_features"].to(device)
+                state_features = processed.get("state_features")
+                if state_features is not None:
+                    state_features = state_features.to(device)
+                lengths = processed.get("lengths")
+
+                for scheme in schemes_to_viz:
+                    sd = scheme_data[scheme]
+
+                    # Ground truth
+                    # In stride visualization mode, ground-truth plots can be misleading
+                    # (only sparse points are available), so we skip GT.
+                    if stride == 1 and sd["target_key"] in processed:
+                        gt_target = processed[sd["target_key"]][0, target_idx].cpu().item()
+                        sd["viz_gt_stages"][local_idx] = int(gt_target)
+                        sd["viz_gt_progress"][local_idx] = normalize_stage_tau(
+                            gt_target,
+                            num_stages=sd["num_stages"],
+                            temporal_proportions=sd["temporal_props"],
+                            subtask_names=sd["subtask_names"],
+                        )
+
+                    # Predictions
+                    reward, stage_probs = reward_model.calculate_rewards(
+                        text_embeddings=text_features,
+                        video_embeddings=video_features,
+                        state_features=state_features,
+                        lengths=lengths,
+                        return_all_frames=True,
+                        return_stages=True,
+                        head_mode=scheme,
+                    )
+
+                    # Handle both tensor and numpy outputs
+                    if isinstance(reward, torch.Tensor):
+                        reward = reward.cpu().numpy()
+                        stage_probs = stage_probs.cpu().numpy()
+
+                    if reward.ndim == 2:
+                        sd["viz_progress"][local_idx] = reward[0, target_idx]
+                        sd["viz_stages"][local_idx] = stage_probs[0, target_idx, :]
+                    else:
+                        sd["viz_progress"][local_idx] = reward[target_idx]
+                        sd["viz_stages"][local_idx] = stage_probs[target_idx, :]
+
+                # Clear GPU memory after each frame
+                del processed, video_features, text_features
+                if state_features is not None:
+                    del state_features
+
+            torch.cuda.empty_cache()
+
+        # Interpolate predictions back to per-frame arrays for smooth visualization.
+        if stride > 1:
+            all_local = np.arange(num_frames)
+            for scheme in schemes_to_viz:
+                sd = scheme_data[scheme]
+
+                valid = np.isfinite(sd["viz_progress"])
+                valid_idx = np.where(valid)[0]
+                if valid_idx.size >= 1:
+                    sd["viz_progress"] = interpolate_progress(
+                        valid_idx, sd["viz_progress"][valid_idx], all_local
+                    )
+
+                    stage_interp = np.zeros_like(sd["viz_stages"], dtype=np.float32)
+                    for s in range(sd["num_stages"]):
+                        stage_interp[:, s] = interpolate_progress(
+                            valid_idx, sd["viz_stages"][valid_idx, s], all_local
+                        )
+
+                    stage_interp = np.clip(stage_interp, 0.0, 1.0)
+                    row_sums = stage_interp.sum(axis=1, keepdims=True)
+                    nz = row_sums.squeeze(-1) > 0
+                    stage_interp[nz] = stage_interp[nz] / row_sums[nz]
+                    sd["viz_stages"] = stage_interp
+                else:
+                    # No valid points: keep NaNs/zeros; visualization will be empty.
+                    sd["viz_stages"] = np.nan_to_num(sd["viz_stages"], nan=0.0)
+
+        # Generate visualization for each head
+        ordered_viz_frames = [viz_frames[idx] for idx in sorted(display_indices)]
+        for scheme in schemes_to_viz:
+            sd = scheme_data[scheme]
+            stage_labels = sd["subtask_names"] or [f"Stage {i + 1}" for i in range(sd["num_stages"])]
+            viz_path = output_dir / f"sarm_prediction_ep{episode_idx}_{scheme}.png"
+
+            visualize_episode(
+                frames=np.array(ordered_viz_frames),
+                progress_preds=sd["viz_progress"],
+                stage_preds=sd["viz_stages"],
+                title=f"{task} (Episode {episode_idx})",
+                output_path=viz_path,
+                stage_labels=stage_labels,
+                gt_progress=sd["viz_gt_progress"] if not np.all(np.isnan(sd["viz_gt_progress"])) else None,
+                gt_stages=sd["viz_gt_stages"] if not np.all(np.isnan(sd["viz_gt_stages"])) else None,
+            )
+
+        # Clear memory between episodes
+        torch.cuda.empty_cache()
+
+    logging.info(f"Visualizations saved to: {output_dir.absolute()}")
+
+
+def generate_all_frame_indices(ep_start: int, ep_end: int, frame_gap: int = 30) -> list[int]:
+    """Generate all frame indices, ordered by offset for cache-friendly access.
+
+    Orders frames as: [0, 30, 60...], [1, 31, 61...], ..., [29, 59, 89...]
+    This groups frames that share similar temporal windows together.
+    """
+    num_frames = ep_end - ep_start
+    indices = []
+    for offset in range(frame_gap):
+        for frame_rel in range(offset, num_frames, frame_gap):
+            indices.append(ep_start + frame_rel)
+    return indices
+
+
+def interpolate_progress(
+    computed_indices: np.ndarray,
+    computed_values: np.ndarray,
+    all_indices: np.ndarray,
+) -> np.ndarray:
+    """Linearly interpolate values to fill in gaps (robust to NaNs / edge cases)."""
+    computed_indices = np.asarray(computed_indices)
+    computed_values = np.asarray(computed_values)
+    all_indices = np.asarray(all_indices)
+
+    mask = np.isfinite(computed_values)
+    if mask.sum() == 0:
+        return np.full(all_indices.shape, np.nan, dtype=np.float32)
+    if mask.sum() == 1:
+        return np.full(all_indices.shape, float(computed_values[mask][0]), dtype=np.float32)
+
+    out = np.interp(all_indices, computed_indices[mask], computed_values[mask])
+    return out.astype(np.float32)
+
+
+def compute_sarm_progress(
+    dataset_repo_id: str,
+    reward_model_path: str,
+    output_path: str | None = None,
+    head_mode: str = "sparse",
+    device: str = "cuda",
+    num_visualizations: int = 5,
+    output_dir: str = "./sarm_viz",
+    stride: int = 1,
+):
+    """
+    Compute SARM progress predictions for all frames in a dataset.
+
+    Args:
+        dataset_repo_id: HuggingFace dataset repo ID or local path
+        reward_model_path: Path to pretrained SARM model
+        output_path: Path to save results. If None, saves to dataset's cache directory
+        head_mode: SARM head to use ("sparse", "dense", or "both")
+        device: Device to use for inference
+        num_visualizations: Number of episodes to visualize (0 to skip)
+        output_dir: Directory to save visualizations
+        stride: Compute progress every N frames, interpolate the rest (default: 1 = every frame)
+    """
+    dataset, reward_model, preprocess = load_sarm_resources(dataset_repo_id, reward_model_path, device)
+
+    # Set preprocessor to eval mode to disable augmentations
+    if hasattr(preprocess, "eval"):
+        preprocess.eval()
+    for step in preprocess.steps:
+        if hasattr(step, "eval"):
+            step.eval()
+
+    image_key = reward_model.config.image_key
+    state_key = reward_model.config.state_key
+    frame_gap = reward_model.config.frame_gap
+    num_episodes = dataset.num_episodes
+    total_frames = dataset.num_frames
+    logging.info(f"Processing {total_frames} frames across {num_episodes} episodes")
+
+    # Determine which heads to compute
+    dual_mode = reward_model.config.uses_dual_heads
+    compute_sparse = head_mode in ("sparse", "both") or not dual_mode
+    compute_dense = head_mode in ("dense", "both") and dual_mode
+
+    # Storage arrays
+    all_indices = []
+    all_episode_indices = []
+    all_frame_indices = []
+    all_progress_sparse = [] if compute_sparse else None
+    all_progress_dense = [] if compute_dense else None
+
+    if stride > 1:
+        logging.info(f"Using stride={stride}: computing every {stride} frames, interpolating the rest")
+
+    # Process all episodes
+    for episode_idx in tqdm(range(num_episodes), desc="Episodes"):
+        ep = dataset.meta.episodes[episode_idx]
+        ep_start = ep["dataset_from_index"]
+        ep_end = ep["dataset_to_index"]
+
+        # Get task description
+        task = dataset[ep_start].get("task", "perform the task")
+
+        # Generate frames to compute (with stride applied)
+        all_ep_indices = generate_all_frame_indices(ep_start, ep_end, frame_gap)
+        if stride > 1:
+            # Only compute every stride-th frame (relative to episode start)
+            compute_indices = [idx for idx in all_ep_indices if (idx - ep_start) % stride == 0]
+            # Always include last frame for better interpolation at episode end
+            last_frame = ep_end - 1
+            if last_frame not in compute_indices:
+                compute_indices.append(last_frame)
+            compute_indices = sorted(set(compute_indices))
+        else:
+            compute_indices = all_ep_indices
+
+        center_idx = reward_model.config.n_obs_steps // 2  # Center of bidirectional window
+
+        # Dictionary to collect results
+        frame_results = {}
+
+        for query_idx in tqdm(compute_indices, desc=f"  Ep {episode_idx}", leave=False):
+            try:
+                sample = dataset[query_idx]
+
+                batch = {
+                    image_key: sample[image_key],
+                    "task": task,
+                    "index": query_idx,
+                    "episode_index": episode_idx,
+                }
+                if state_key in sample:
+                    batch[state_key] = sample[state_key]
+
+                with torch.no_grad():
+                    processed = preprocess(batch)
+                    video_features = processed["video_features"].to(device)
+                    text_features = processed["text_features"].to(device)
+                    state_features = processed.get("state_features")
+                    if state_features is not None:
+                        state_features = state_features.to(device)
+                    lengths = processed.get("lengths")
+
+                    sparse_val = np.nan
+                    dense_val = np.nan
+
+                    # Compute sparse prediction for center frame
+                    if compute_sparse:
+                        sparse_progress = reward_model.calculate_rewards(
+                            text_embeddings=text_features,
+                            video_embeddings=video_features,
+                            state_features=state_features,
+                            lengths=lengths,
+                            return_all_frames=True,
+                            head_mode="sparse",
+                        )
+                        sparse_val = float(
+                            sparse_progress[0, center_idx]
+                            if sparse_progress.ndim == 2
+                            else sparse_progress[center_idx]
+                        )
+
+                    # Compute dense prediction for center frame
+                    if compute_dense:
+                        dense_progress = reward_model.calculate_rewards(
+                            text_embeddings=text_features,
+                            video_embeddings=video_features,
+                            state_features=state_features,
+                            lengths=lengths,
+                            return_all_frames=True,
+                            head_mode="dense",
+                        )
+                        dense_val = float(
+                            dense_progress[0, center_idx]
+                            if dense_progress.ndim == 2
+                            else dense_progress[center_idx]
+                        )
+
+                    frame_results[query_idx] = (sparse_val, dense_val)
+
+            except Exception as e:
+                logging.warning(f"Failed to process frame {query_idx}: {e}")
+
+        # Interpolate to get values for all frames
+        computed_indices = np.array(sorted(frame_results.keys()))
+        computed_sparse = (
+            np.array([frame_results[i][0] for i in computed_indices]) if compute_sparse else None
+        )
+        computed_dense = np.array([frame_results[i][1] for i in computed_indices]) if compute_dense else None
+
+        # All frame indices for this episode
+        all_frame_idx_array = np.arange(ep_start, ep_end)
+
+        if stride > 1 and len(computed_indices) > 1:
+            # Interpolate progress values
+            if compute_sparse:
+                interp_sparse = interpolate_progress(computed_indices, computed_sparse, all_frame_idx_array)
+            if compute_dense:
+                interp_dense = interpolate_progress(computed_indices, computed_dense, all_frame_idx_array)
+        else:
+            # No interpolation needed
+            interp_sparse = computed_sparse if compute_sparse else None
+            interp_dense = computed_dense if compute_dense else None
+
+        # Store results for all frames
+        for i, frame_idx in enumerate(all_frame_idx_array):
+            local_idx = frame_idx - ep_start
+            all_indices.append(frame_idx)
+            all_episode_indices.append(episode_idx)
+            all_frame_indices.append(local_idx)
+            if compute_sparse:
+                if stride > 1 and len(computed_indices) > 1:
+                    all_progress_sparse.append(float(interp_sparse[i]))
+                elif frame_idx in frame_results:
+                    all_progress_sparse.append(frame_results[frame_idx][0])
+                else:
+                    all_progress_sparse.append(np.nan)
+            if compute_dense:
+                if stride > 1 and len(computed_indices) > 1:
+                    all_progress_dense.append(float(interp_dense[i]))
+                elif frame_idx in frame_results:
+                    all_progress_dense.append(frame_results[frame_idx][1])
+                else:
+                    all_progress_dense.append(np.nan)
+
+    # Create output table
+    table_data = {
+        "index": np.array(all_indices, dtype=np.int64),
+        "episode_index": np.array(all_episode_indices, dtype=np.int64),
+        "frame_index": np.array(all_frame_indices, dtype=np.int64),
+    }
+    if compute_sparse:
+        table_data["progress_sparse"] = np.array(all_progress_sparse, dtype=np.float32)
+    if compute_dense:
+        table_data["progress_dense"] = np.array(all_progress_dense, dtype=np.float32)
+
+    # Sort by index
+    df = pa.table(table_data).to_pandas()
+    df = df.sort_values("index").reset_index(drop=True)
+    final_table = pa.Table.from_pandas(df, preserve_index=False)
+
+    # Add metadata with reward model path
+    metadata = {b"reward_model_path": reward_model_path.encode()}
+    final_table = final_table.replace_schema_metadata(metadata)
+
+    # Determine output path
+    output_path = Path(dataset.root) / "sarm_progress.parquet" if output_path is None else Path(output_path)
+
+    # Save
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    pq.write_table(final_table, output_path)
+    logging.info(f"Saved {len(final_table)} frame progress values to {output_path}")
+
+    # Print statistics
+    if "progress_sparse" in df.columns:
+        valid = df["progress_sparse"].dropna()
+        logging.info(
+            f"Sparse progress: mean={valid.mean():.4f}, std={valid.std():.4f}, "
+            f"min={valid.min():.4f}, max={valid.max():.4f}"
+        )
+
+    if "progress_dense" in df.columns:
+        valid = df["progress_dense"].dropna()
+        logging.info(
+            f"Dense progress: mean={valid.mean():.4f}, std={valid.std():.4f}, "
+            f"min={valid.min():.4f}, max={valid.max():.4f}"
+        )
+
+    # Visualize episodes after processing
+    if num_visualizations > 0:
+        viz_episodes = list(range(min(num_visualizations, num_episodes)))
+        logging.info(f"Generating {len(viz_episodes)} visualizations...")
+        visualize_sarm_predictions(
+            dataset=dataset,
+            reward_model=reward_model,
+            preprocess=preprocess,
+            episode_indices=viz_episodes,
+            head_mode=head_mode,
+            output_dir=Path(output_dir),
+            stride=stride,
+        )
+
+    return output_path
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Compute SARM progress values for RA-BC weighting or visualize SARM predictions",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Examples:
+    # Full RA-BC computation with visualizations
+    python src/lerobot/policies/sarm/compute_rabc_weights.py \\
+        --dataset-repo-id lerobot/aloha_sim_insertion_human \\
+        --reward-model-path pepijn223/sarm_single_uni4
+
+    # Visualize predictions only (no RA-BC computation)
+    python src/lerobot/policies/sarm/compute_rabc_weights.py \\
+        --dataset-repo-id lerobot/aloha_sim_insertion_human \\
+        --reward-model-path pepijn223/sarm_single_uni4 \\
+        --visualize-only \\
+        --num-visualizations 10
+        """,
+    )
+    parser.add_argument(
+        "--dataset-repo-id",
+        type=str,
+        required=True,
+        help="HuggingFace dataset repo ID or local path",
+    )
+    parser.add_argument(
+        "--reward-model-path",
+        type=str,
+        default=None,
+        help="Path to pretrained SARM model (reads from existing parquet metadata if not provided)",
+    )
+    parser.add_argument(
+        "--output-path",
+        type=str,
+        default=None,
+        help="Output path for parquet. If not set, saves to dataset's cache directory",
+    )
+    parser.add_argument(
+        "--head-mode",
+        type=str,
+        default="sparse",
+        choices=["sparse", "dense", "both"],
+        help="SARM head to use (default: sparse)",
+    )
+    parser.add_argument(
+        "--device",
+        type=str,
+        default="cuda",
+        help="Device to use (default: cuda)",
+    )
+    # Visualization options
+    parser.add_argument(
+        "--visualize-only",
+        action="store_true",
+        help="Only visualize SARM predictions (no RA-BC computation)",
+    )
+    parser.add_argument(
+        "--num-visualizations",
+        type=int,
+        default=5,
+        help="Number of episodes to visualize (default: 5, set to 0 to skip)",
+    )
+    parser.add_argument(
+        "--output-dir",
+        type=str,
+        default="./sarm_viz",
+        help="Output directory for visualizations (default: ./sarm_viz)",
+    )
+    parser.add_argument(
+        "--push-to-hub",
+        action="store_true",
+        help="Upload progress file to the dataset repo on HuggingFace Hub",
+        default=True,
+    )
+    parser.add_argument(
+        "--stride",
+        type=int,
+        default=1,
+        help="Compute progress every N frames, interpolate the rest (default: 1 = every frame)",
+    )
+
+    args = parser.parse_args()
+
+    logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
+
+    # Try to get reward_model_path from parquet metadata if not provided
+    reward_model_path = args.reward_model_path
+    if reward_model_path is None:
+        # Load dataset to find parquet path
+        temp_dataset = LeRobotDataset(args.dataset_repo_id, download_videos=False)
+        parquet_path = Path(temp_dataset.root) / "sarm_progress.parquet"
+        reward_model_path = get_reward_model_path_from_parquet(parquet_path)
+        if reward_model_path:
+            logging.info(f"Using reward model from parquet metadata: {reward_model_path}")
+        else:
+            raise ValueError(
+                "--reward-model-path is required (no existing parquet with model metadata found)"
+            )
+
+    # Handle visualize-only mode
+    if args.visualize_only:
+        dataset, reward_model, preprocess = load_sarm_resources(
+            args.dataset_repo_id, reward_model_path, args.device
+        )
+        logging.info(f"Visualization-only mode: visualizing {args.num_visualizations} episodes")
+        viz_episodes = list(range(min(args.num_visualizations, dataset.num_episodes)))
+        visualize_sarm_predictions(
+            dataset=dataset,
+            reward_model=reward_model,
+            preprocess=preprocess,
+            episode_indices=viz_episodes,
+            head_mode=args.head_mode,
+            output_dir=Path(args.output_dir),
+            stride=args.stride,
+        )
+        print(f"\nVisualizations saved to: {Path(args.output_dir).absolute()}")
+        return
+
+    # Full RABC computation (compute_sarm_progress loads model/dataset itself)
+    output_path = compute_sarm_progress(
+        dataset_repo_id=args.dataset_repo_id,
+        reward_model_path=reward_model_path,
+        output_path=args.output_path,
+        head_mode=args.head_mode,
+        device=args.device,
+        num_visualizations=args.num_visualizations,
+        output_dir=args.output_dir,
+        stride=args.stride,
+    )
+
+    print(f"\nSARM progress values saved to: {output_path}")
+
+    # Upload to Hub if requested
+    if args.push_to_hub:
+        from huggingface_hub import HfApi
+
+        api = HfApi()
+        hub_path = "sarm_progress.parquet"
+
+        print(f"\nUploading to Hub: {args.dataset_repo_id}/{hub_path}")
+        api.upload_file(
+            path_or_fileobj=str(output_path),
+            path_in_repo=hub_path,
+            repo_id=args.dataset_repo_id,
+            repo_type="dataset",
+        )
+        print(
+            f"Successfully uploaded to: https://huggingface.co/datasets/{args.dataset_repo_id}/blob/main/{hub_path}"
+        )
+
+        print("\nTo use in training, add to your config:")
+        print("  use_rabc: true")
+        print(f"  rabc_progress_path: hf://datasets/{args.dataset_repo_id}/{hub_path}")
+        print("  rabc_head_mode: sparse  # or dense")
+    else:
+        print("\nTo use in training, add to your config:")
+        print("  use_rabc: true")
+        print(f"  rabc_progress_path: {output_path}")
+        print("  rabc_head_mode: sparse  # or dense")
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,248 @@
+#!/usr/bin/env python
+
+# Copyright 2025 Qianzhong Chen, Justin Yu, Mac Schwager, Pieter Abbeel, Yide Shentu, Philipp Wu
+# 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.
+
+"""
+SARM: Stage-Aware Reward Modeling for Long Horizon Robot Manipulation.
+Paper: https://arxiv.org/abs/2509.25358
+"""
+
+from dataclasses import dataclass, field
+
+from lerobot.configs.policies import PreTrainedConfig
+from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
+from lerobot.optim.optimizers import AdamWConfig
+from lerobot.optim.schedulers import CosineDecayWithWarmupSchedulerConfig
+
+
+@PreTrainedConfig.register_subclass("sarm")
+@dataclass
+class SARMConfig(PreTrainedConfig):
+    """Configuration class for SARM (Stage-Aware Reward Modeling).
+
+    Supports three annotation modes:
+
+    1. single_stage (default): No annotations needed. Uses the episode's task description
+       as a single stage covering the entire episode.
+
+    2. dense_only: Uses dense (fine-grained) annotations from VLM, with an auto-generated
+       single sparse "task" stage covering the full episode. The dense head learns detailed
+       subtask progression while sparse provides overall task completion.
+
+    3. dual: Full dual-head mode with both sparse (high-level) and dense (fine-grained)
+       annotations from VLM. Both heads are trained on their respective annotations.
+
+    The annotation_mode determines how sparse_temporal_proportions and dense_temporal_proportions
+    are loaded/generated during model initialization.
+    """
+
+    annotation_mode: str = "single_stage"  # "single_stage", "dense_only", or "dual"
+    n_obs_steps: int = 8  # Number of observation history steps
+    frame_gap: int = 30  # Frame gap between frames (at 30 fps = 1 second)
+    max_rewind_steps: int = 4  # Maximum rewind steps for temporal augmentation
+
+    # Total frames = 1 + n_obs_steps + max_rewind_steps (computed in property)
+    # During training with rewind: [obs_frames] + [rewind_frames]
+    # During inference: [obs_frames] only
+
+    # Architecture params
+    image_dim: int = 512
+    text_dim: int = 512
+    hidden_dim: int = 768
+    num_heads: int = 12
+    num_layers: int = 8
+    max_state_dim: int = 32
+    drop_n_last_frames: int = 1
+    batch_size: int = 64
+    clip_batch_size: int = 64
+    dropout: float = 0.1
+    stage_loss_weight: float = 1.0  # Weight for stage classification loss when using subtask annotations
+
+    rewind_probability: float = 0.8
+    language_perturbation_probability: float = 0.2
+
+    # Sparse annotations (high-level stages)
+    num_sparse_stages: int = 1
+    sparse_subtask_names: list | None = None
+    sparse_temporal_proportions: list | None = None
+
+    # Dense annotations (fine-grained stages)
+    num_dense_stages: int | None = None
+    dense_subtask_names: list | None = None
+    dense_temporal_proportions: list | None = None
+
+    pretrained_model_path: str | None = None
+    device: str | None = None
+    image_key: str = "observation.images.top"  # Key for image used from the dataset
+    state_key: str = "observation.state"
+
+    # Populated by the processor (video_features, state_features, text_features)
+    input_features: dict = field(default_factory=lambda: {})
+
+    # Output features (updated in __post_init__)
+    output_features: dict = field(
+        default_factory=lambda: {
+            "stage": PolicyFeature(shape=(9, 5), type=FeatureType.REWARD),
+            "progress": PolicyFeature(shape=(9, 1), type=FeatureType.REWARD),
+        }
+    )
+
+    normalization_mapping: dict[str, NormalizationMode] = field(
+        default_factory=lambda: {
+            "VISUAL": NormalizationMode.IDENTITY,
+            "STATE": NormalizationMode.MEAN_STD,
+            "LANGUAGE": NormalizationMode.IDENTITY,
+            "REWARD": NormalizationMode.IDENTITY,
+        }
+    )
+
+    def __post_init__(self):
+        super().__post_init__()
+
+        if self.annotation_mode not in ["single_stage", "dense_only", "dual"]:
+            raise ValueError(
+                f"annotation_mode must be 'single_stage', 'dense_only', or 'dual', got {self.annotation_mode}"
+            )
+
+        if self.annotation_mode == "single_stage":
+            # Use task description as stage name, full episode as one stage
+            self.num_sparse_stages = 1
+            self.sparse_subtask_names = ["task"]
+            self.sparse_temporal_proportions = [1.0]
+            self.num_dense_stages = None
+            self.dense_subtask_names = None
+            self.dense_temporal_proportions = None
+
+        elif self.annotation_mode == "dense_only":
+            self.num_sparse_stages = 1
+            self.sparse_subtask_names = ["task"]
+            self.sparse_temporal_proportions = [1.0]
+
+        self.input_features = {}
+        self.output_features = {}
+
+        if self.image_key:
+            self.input_features[self.image_key] = PolicyFeature(shape=(480, 640, 3), type=FeatureType.VISUAL)
+
+        self.input_features[self.state_key] = PolicyFeature(
+            shape=(self.max_state_dim,),
+            type=FeatureType.STATE,
+        )
+
+        # Update output features based on annotation_mode
+        if self.annotation_mode in ["dense_only", "dual"]:
+            self.output_features["sparse_stage"] = PolicyFeature(
+                shape=(self.num_frames, self.num_sparse_stages), type=FeatureType.REWARD
+            )
+            self.output_features["sparse_progress"] = PolicyFeature(
+                shape=(self.num_frames, 1), type=FeatureType.REWARD
+            )
+            dense_stages = self.num_dense_stages or self.num_sparse_stages
+            self.output_features["dense_stage"] = PolicyFeature(
+                shape=(self.num_frames, dense_stages), type=FeatureType.REWARD
+            )
+            self.output_features["dense_progress"] = PolicyFeature(
+                shape=(self.num_frames, 1), type=FeatureType.REWARD
+            )
+        else:
+            self.output_features["sparse_stage"] = PolicyFeature(
+                shape=(self.num_frames, self.num_sparse_stages), type=FeatureType.REWARD
+            )
+            self.output_features["sparse_progress"] = PolicyFeature(
+                shape=(self.num_frames, 1), type=FeatureType.REWARD
+            )
+
+        if self.max_rewind_steps >= self.n_obs_steps:
+            raise ValueError(
+                f"max_rewind_steps ({self.max_rewind_steps}) must be less than n_obs_steps ({self.n_obs_steps})"
+            )
+        if self.num_sparse_stages < 1:
+            raise ValueError(f"num_sparse_stages must be at least 1, got {self.num_sparse_stages}")
+        if (
+            self.annotation_mode in ["dense_only", "dual"]
+            and self.num_dense_stages is not None
+            and self.num_dense_stages < 2
+        ):
+            raise ValueError(f"num_dense_stages must be at least 2, got {self.num_dense_stages}")
+
+    def get_optimizer_preset(self) -> AdamWConfig:
+        """Get default optimizer configuration for SARM training."""
+        return AdamWConfig(
+            lr=5e-5,
+            weight_decay=1e-3,
+            betas=(0.9, 0.999),
+            eps=1e-8,
+        )
+
+    def get_scheduler_preset(self) -> CosineDecayWithWarmupSchedulerConfig:
+        """Get default learning rate scheduler configuration."""
+        return CosineDecayWithWarmupSchedulerConfig(
+            peak_lr=5e-5,
+            decay_lr=5e-6,
+            num_warmup_steps=500,
+            num_decay_steps=50000,
+        )
+
+    def validate_features(self) -> None:
+        pass
+
+    @property
+    def uses_dual_heads(self) -> bool:
+        """Whether the model uses dual heads (dense_only or dual annotation modes)."""
+        return self.annotation_mode in ["dense_only", "dual"]
+
+    @property
+    def num_frames(self) -> int:
+        """Total number of frames in sequence.
+
+        For training: 1 + n_obs_steps + max_rewind_steps
+        The sequence is: [obs_frames (n_obs_steps + 1)] + [rewind_frames (max_rewind_steps)]
+        """
+        return 1 + self.n_obs_steps + self.max_rewind_steps
+
+    @property
+    def max_length(self) -> int:
+        return self.num_frames
+
+    @property
+    def observation_delta_indices(self) -> list[int]:
+        """Bidirectional frame sampling centered on target frame.
+
+        Example with n_obs_steps=8, gap=30:
+        Before: [-120, -90, -60, -30]  (4 frames)
+        Current: [0]                   (1 frame)
+        After:  [30, 60, 90, 120]      (4 frames)
+        Total: 9 frames
+        """
+        half_steps = self.n_obs_steps // 2
+
+        past_deltas = [-self.frame_gap * i for i in range(half_steps, 0, -1)]
+        future_deltas = [self.frame_gap * i for i in range(1, half_steps + 1)]
+        obs_deltas = past_deltas + [0] + future_deltas
+
+        # Rewind placeholders
+        rewind_deltas = [-self.frame_gap * (i + 1) for i in range(self.max_rewind_steps)]
+
+        return obs_deltas + rewind_deltas
+
+    @property
+    def action_delta_indices(self) -> None:
+        """SARM is a reward model, not an action policy."""
+        return None
+
+    @property
+    def reward_delta_indices(self) -> None:
+        return None
@@ -0,0 +1,793 @@
+#!/usr/bin/env python
+
+# Copyright 2025 Qianzhong Chen, Justin Yu, Mac Schwager, Pieter Abbeel, Yide Shentu, Philipp Wu
+# 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.
+
+"""
+SARM: Stage-Aware Reward Modeling for Long Horizon Robot Manipulation.
+
+Paper: https://arxiv.org/abs/2509.25358
+
+- StageTransformer: Predicts stage classification (sparse/dense)
+- SubtaskTransformer: Predicts within-stage progress (tau) conditioned on stage
+"""
+
+import json
+import logging
+import random
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F  # noqa: N812
+from torch import Tensor
+
+from lerobot.policies.pretrained import PreTrainedPolicy
+from lerobot.policies.sarm.configuration_sarm import SARMConfig
+from lerobot.policies.sarm.sarm_utils import (
+    normalize_stage_tau,
+    pad_state_to_max_dim,
+)
+
+
+class StageTransformer(nn.Module):
+    """
+    Stage classification transformer for SARM.
+
+    Predicts which stage/subtask the current frame belongs to.
+    Supports both sparse (high-level) and dense (fine-grained) annotation schemes.
+
+    Input streams: [vis_proj, lang_proj, state_proj] concatenated -> (B, N+2, T, D)
+    Output: stage logits (B, T, num_classes)
+    """
+
+    def __init__(
+        self,
+        d_model: int = 512,
+        vis_emb_dim: int = 512,
+        text_emb_dim: int = 512,
+        state_dim: int = 32,
+        n_layers: int = 6,
+        n_heads: int = 8,
+        dropout: float = 0.1,
+        num_cameras: int = 1,
+        num_classes_sparse: int = 4,
+        num_classes_dense: int = 8,
+    ):
+        super().__init__()
+        self.d_model = d_model
+        self.num_cameras = num_cameras
+
+        # Projections
+        self.lang_proj = nn.Linear(text_emb_dim, d_model)
+        self.visual_proj = nn.Linear(vis_emb_dim, d_model)
+        self.state_proj = nn.Linear(state_dim, d_model)
+
+        # Encoder
+        enc_layer = nn.TransformerEncoderLayer(d_model, n_heads, 4 * d_model, dropout, batch_first=True)
+        self.transformer = nn.TransformerEncoder(enc_layer, n_layers)
+
+        # Positional bias on first visual frame
+        self.first_pos = nn.Parameter(torch.zeros(1, d_model))
+
+        # Shared fusion MLP
+        # Fuses (num_cameras + 2) streams: cameras + lang + state
+        fused_in = d_model * (num_cameras + 2)
+        self.fusion_backbone = nn.Sequential(
+            nn.LayerNorm(fused_in),
+            nn.Linear(fused_in, d_model),
+            nn.ReLU(),
+        )
+
+        # Scheme-specific heads
+        self.heads = nn.ModuleDict(
+            {
+                "sparse": nn.Linear(d_model, num_classes_sparse),
+                "dense": nn.Linear(d_model, num_classes_dense),
+            }
+        )
+
+    def _prep_lang(self, lang_emb: torch.Tensor, B: int, T: int, D: int) -> torch.Tensor:  # noqa: N803
+        """
+        Prepare language embeddings for fusion.
+
+        Accepts lang_emb of shape:
+          - (B, text_emb_dim) -> broadcast across time
+          - (B, T, text_emb_dim) -> per-timestep (dense annotation mode)
+
+        Returns: (B, 1, T, D)
+        """
+        if lang_emb.dim() == 3:
+            # (B, T, E) -> (B, T, D) -> (B, 1, T, D)
+            lang_proj = self.lang_proj(lang_emb).unsqueeze(1)
+        else:
+            # (B, E) -> (B, 1, 1, D) -> expand to (B, 1, T, D)
+            lang_proj = self.lang_proj(lang_emb).unsqueeze(1).unsqueeze(2).expand(B, 1, T, D)
+        return lang_proj
+
+    def forward(
+        self,
+        img_seq: torch.Tensor,  # (B, N, T, vis_emb_dim)
+        lang_emb: torch.Tensor,  # (B, E) or (B, T, E)
+        state: torch.Tensor,  # (B, T, state_dim)
+        lengths: torch.Tensor,  # (B,) - valid sequence lengths
+        scheme: str = "sparse",  # "sparse" or "dense"
+    ) -> torch.Tensor:
+        """
+        Forward pass for stage classification.
+
+        Args:
+            img_seq: Image embeddings (B, N, T, vis_emb_dim) where N=num_cameras
+            lang_emb: Language embeddings (B, E) or (B, T, E) for dense
+            state: State features (B, T, state_dim)
+            lengths: Valid sequence lengths (B,) for masking
+            scheme: "sparse" or "dense" for head selection
+
+        Returns:
+            Stage logits (B, T, num_classes)
+        """
+        assert scheme in self.heads, f"Unknown scheme '{scheme}'. Use one of {list(self.heads.keys())}."
+
+        B, N, T, _ = img_seq.shape  # noqa: N806
+        D = self.d_model  # noqa: N806
+        device = img_seq.device
+
+        # Project inputs
+        vis_proj = self.visual_proj(img_seq)  # (B, N, T, D)
+        state_proj = self.state_proj(state).unsqueeze(1)  # (B, 1, T, D)
+        lang_proj = self._prep_lang(lang_emb, B, T, D)  # (B, 1, T, D)
+
+        # Concatenate streams
+        # cameras + lang + state -> (B, N+2, T, D)
+        x = torch.cat([vis_proj, lang_proj, state_proj], dim=1)
+
+        # Add positional bias to first visual frame
+        x[:, :N, 0, :] = x[:, :N, 0, :] + self.first_pos
+
+        # Flatten to tokens for Transformer
+        x_tokens = x.view(B, (N + 2) * T, D)
+        L = x_tokens.size(1)  # noqa: N806
+
+        # Create padding mask
+        base_mask = torch.arange(T, device=device).expand(B, T) >= lengths.unsqueeze(1)  # (B, T)
+        mask = base_mask.unsqueeze(1).expand(B, N + 2, T).reshape(B, (N + 2) * T)
+
+        # Create causal mask
+        causal_mask = torch.triu(torch.ones(L, L, device=device, dtype=torch.bool), diagonal=1)
+
+        # Encode
+        h = self.transformer(x_tokens, mask=causal_mask, src_key_padding_mask=mask, is_causal=True)
+
+        # Reshape and fuse
+        h = h.view(B, N + 2, T, D).permute(0, 2, 1, 3).reshape(B, T, (N + 2) * D)
+        fused = self.fusion_backbone(h)  # (B, T, D)
+
+        # Scheme-specific logits
+        logits = self.heads[scheme](fused)  # (B, T, num_classes)
+        return logits
+
+
+class SubtaskTransformer(nn.Module):
+    """
+    Subtask progress regression transformer for SARM.
+
+    Predicts within-stage normalized progress (tau) conditioned on stage prior.
+    The stage prior is a one-hot encoding passed from StageTransformer predictions.
+
+    Input streams: [vis_proj, lang_proj, state_proj, stage_emb] -> (B, N+3, T, D)
+    Output: tau predictions (B, T) in [0, 1]
+    """
+
+    def __init__(
+        self,
+        d_model: int = 512,
+        vis_emb_dim: int = 512,
+        text_emb_dim: int = 512,
+        state_dim: int = 32,
+        n_layers: int = 6,
+        n_heads: int = 8,
+        dropout: float = 0.1,
+        num_cameras: int = 1,
+    ):
+        super().__init__()
+        self.d_model = d_model
+        self.num_cameras = num_cameras
+
+        # Projections
+        self.lang_proj = nn.Linear(text_emb_dim, d_model)
+        self.visual_proj = nn.Linear(vis_emb_dim, d_model)
+        self.state_proj = nn.Linear(state_dim, d_model)
+
+        # Encoder
+        enc = nn.TransformerEncoderLayer(d_model, n_heads, 4 * d_model, dropout, batch_first=True)
+        self.transformer = nn.TransformerEncoder(enc, n_layers)
+
+        # Learned bias on first visual frame
+        self.first_pos = nn.Parameter(torch.zeros(1, d_model))
+
+        # Shared fusion backbone
+        # Fuses (num_cameras + 3) streams: cameras + lang + state + stage_emb
+        fused_in = d_model * (num_cameras + 3)
+        self.fusion_backbone = nn.Sequential(
+            nn.LayerNorm(fused_in),
+            nn.Linear(fused_in, d_model),
+            nn.ReLU(),
+        )
+
+        # Scheme-specific regression heads
+        self.heads = nn.ModuleDict(
+            {
+                "sparse": nn.Linear(d_model, 1),
+                "dense": nn.Linear(d_model, 1),
+            }
+        )
+
+    def _prep_lang(self, lang_emb: torch.Tensor, B: int, T: int, D: int) -> torch.Tensor:  # noqa: N803
+        """
+        Prepare language embeddings for fusion.
+        """
+        if lang_emb.dim() == 3:
+            # (B, T, E) -> (B, T, D) -> (B, 1, T, D)
+            return self.lang_proj(lang_emb).unsqueeze(1)
+        else:
+            # (B, E) -> (B, 1, 1, D) -> (B, 1, T, D)
+            return self.lang_proj(lang_emb).unsqueeze(1).unsqueeze(2).expand(B, 1, T, D)
+
+    def _stage_to_dmodel(self, stage_prior: torch.Tensor) -> torch.Tensor:
+        """
+        Deterministic projection of one-hot stage to d_model by pad/truncate.
+
+        Args:
+            stage_prior: One-hot stage embedding (B, 1, T, C)
+
+        Returns:
+            Projected stage embedding (B, 1, T, d_model)
+        """
+        B, one, T, C = stage_prior.shape  # noqa: N806
+        D = self.d_model  # noqa: N806
+        if D == C:
+            return stage_prior
+        elif D > C:
+            pad = torch.zeros(B, one, T, D - C, device=stage_prior.device, dtype=stage_prior.dtype)
+            return torch.cat([stage_prior, pad], dim=-1)
+        else:
+            return stage_prior[..., :D]
+
+    def forward(
+        self,
+        img_seq: torch.Tensor,  # (B, N, T, vis_emb_dim)
+        lang_emb: torch.Tensor,  # (B, E) or (B, T, E)
+        state: torch.Tensor,  # (B, T, state_dim)
+        lengths: torch.Tensor,  # (B,) - valid sequence lengths
+        stage_prior: torch.Tensor,  # (B, 1, T, C) one-hot from gen_stage_emb
+        scheme: str = "sparse",  # "sparse" or "dense"
+    ) -> torch.Tensor:
+        """
+        Forward pass for subtask progress regression.
+
+        Args:
+            img_seq: Image embeddings (B, N, T, vis_emb_dim)
+            lang_emb: Language embeddings (B, E) or (B, T, E)
+            state: State features (B, T, state_dim)
+            lengths: Valid sequence lengths (B,) for masking
+            stage_prior: One-hot stage prior (B, 1, T, num_classes)
+            scheme: "sparse" or "dense" for head selection
+
+        Returns:
+            Tau predictions (B, T) in [0, 1] via sigmoid
+        """
+        assert scheme in self.heads, f"Unknown scheme '{scheme}'. Use one of {list(self.heads.keys())}."
+
+        B, N, T, _ = img_seq.shape  # noqa: N806
+        D = self.d_model  # noqa: N806
+        device = img_seq.device
+
+        # Project inputs
+        vis_proj = self.visual_proj(img_seq)  # (B, N, T, D)
+        state_proj = self.state_proj(state).unsqueeze(1)  # (B, 1, T, D)
+        lang_proj = self._prep_lang(lang_emb, B, T, D)  # (B, 1, T, D)
+        stage_emb = self._stage_to_dmodel(stage_prior)  # (B, 1, T, D)
+
+        # Concatenate all streams
+        # cameras + lang + state + stage_emb -> (B, N+3, T, D)
+        x = torch.cat([vis_proj, lang_proj, state_proj, stage_emb], dim=1)
+
+        # Add positional bias to first visual frame
+        x[:, :N, 0, :] = x[:, :N, 0, :] + self.first_pos
+
+        # Flatten to tokens
+        x_tokens = x.view(B, (N + 3) * T, D)
+        L = x_tokens.size(1)  # noqa: N806
+
+        # Create padding mask
+        base_mask = torch.arange(T, device=device).expand(B, T) >= lengths.unsqueeze(1)
+        mask = base_mask.unsqueeze(1).expand(B, N + 3, T).reshape(B, (N + 3) * T)
+
+        # Create causal mask
+        causal_mask = torch.triu(torch.ones(L, L, device=device, dtype=torch.bool), diagonal=1)
+
+        # Encode
+        h = self.transformer(x_tokens, mask=causal_mask, src_key_padding_mask=mask, is_causal=True)
+
+        # Reshape and fuse
+        h = h.view(B, N + 3, T, D)
+        h_flat = h.permute(0, 2, 1, 3).reshape(B, T, (N + 3) * D)
+        fused = self.fusion_backbone(h_flat)  # (B, T, D)
+
+        # Scheme-specific regression head -> sigmoid
+        r = torch.sigmoid(self.heads[scheme](fused)).squeeze(-1)  # (B, T)
+        return r
+
+
+def gen_stage_emb(num_classes: int, targets: torch.Tensor) -> torch.Tensor:
+    """
+    Generate one-hot stage embeddings from targets.
+
+    Args:
+        num_classes: Number of stage classes
+        targets: Target values (B, T) where integer part is stage index
+
+    Returns:
+        One-hot stage embedding (B, 1, T, num_classes)
+    """
+    # Integer part of float targets -> [0, C-1]
+    idx = targets.long().clamp(min=0, max=num_classes - 1)  # (B, T)
+    C = num_classes  # noqa: N806
+    # Identity-lookup one-hot
+    stage_onehot = torch.eye(C, device=targets.device)[idx]  # (B, T, C)
+    stage_onehot = stage_onehot.unsqueeze(1)  # (B, 1, T, C)
+    return stage_onehot
+
+
+class SARMRewardModel(PreTrainedPolicy):
+    """
+    SARM Reward Model for stage-aware task completion rewards.
+
+    Uses two separate transformer models:
+    - StageTransformer: Classifies which stage/subtask
+    - SubtaskTransformer: Predicts within-stage progress (tau)
+
+    Training uses 75%/25% GT/predicted stage conditioning (teacher forcing).
+    """
+
+    name = "sarm"
+    config_class = SARMConfig
+
+    def __init__(self, config: SARMConfig, dataset_stats: dict | None = None, dataset_meta=None):
+        super().__init__(config, dataset_stats)
+        config.validate_features()
+        self.config = config
+        self.dataset_stats = dataset_stats
+        self.device = torch.device(
+            config.device if config.device else "cuda" if torch.cuda.is_available() else "cpu"
+        )
+
+        # Load temporal proportions based on annotation_mode
+        if config.annotation_mode == "single_stage":
+            logging.info(f"Using single_stage mode: sparse_subtask_names={config.sparse_subtask_names}")
+        elif dataset_meta is not None:
+            self._load_temporal_proportions(dataset_meta)
+
+        # Create two separate models
+        self.stage_model = StageTransformer(
+            d_model=config.hidden_dim,
+            vis_emb_dim=config.image_dim,
+            text_emb_dim=config.text_dim,
+            state_dim=config.max_state_dim,
+            n_layers=config.num_layers,
+            n_heads=config.num_heads,
+            dropout=config.dropout,
+            num_cameras=1,  # Single camera for now
+            num_classes_sparse=config.num_sparse_stages,
+            num_classes_dense=config.num_dense_stages or config.num_sparse_stages,
+        )
+
+        self.subtask_model = SubtaskTransformer(
+            d_model=config.hidden_dim,
+            vis_emb_dim=config.image_dim,
+            text_emb_dim=config.text_dim,
+            state_dim=config.max_state_dim,
+            n_layers=config.num_layers,
+            n_heads=config.num_heads,
+            dropout=config.dropout,
+            num_cameras=1,
+        )
+
+        self.stage_model.to(self.device)
+        self.subtask_model.to(self.device)
+
+        # GT/predicted stage ratio for teacher forcing
+        self.gt_stage_ratio = 0.75
+
+        if config.uses_dual_heads:
+            logging.info(
+                f"SARM initialized with dual heads: {config.num_sparse_stages} sparse stages, "
+                f"{config.num_dense_stages} dense stages"
+            )
+        else:
+            logging.info(f"SARM initialized with sparse head only: {config.num_sparse_stages} stages")
+
+        logging.info(f"SARM initialized on {self.device}")
+
+    def _load_proportions_from_json(self, path, annotation_type: str) -> tuple[list[str], list[float]]:
+        """Load temporal proportions from a JSON file (preserving order)."""
+        if not path.exists():
+            raise ValueError(
+                f"{annotation_type.capitalize()} temporal proportions not found at {path}. "
+                f"Run the subtask annotation tool with --{annotation_type}-subtasks to generate annotations."
+            )
+        with open(path) as f:
+            proportions_dict = json.load(f)
+        names = list(proportions_dict.keys())
+        logging.info(f"Loaded {len(names)} {annotation_type} subtasks: {names}")
+        logging.info(f"{annotation_type.capitalize()} temporal proportions: {proportions_dict}")
+        return names, [proportions_dict[name] for name in names]
+
+    def _load_temporal_proportions(self, dataset_meta) -> None:
+        """Load temporal proportions based on annotation_mode."""
+        meta_path = dataset_meta.root / "meta"
+
+        if self.config.annotation_mode == "dual":
+            names, props = self._load_proportions_from_json(
+                meta_path / "temporal_proportions_sparse.json", "sparse"
+            )
+            (
+                self.config.num_sparse_stages,
+                self.config.sparse_subtask_names,
+                self.config.sparse_temporal_proportions,
+            ) = len(names), names, props
+
+        if self.config.annotation_mode in ["dense_only", "dual"]:
+            names, props = self._load_proportions_from_json(
+                meta_path / "temporal_proportions_dense.json", "dense"
+            )
+            (
+                self.config.num_dense_stages,
+                self.config.dense_subtask_names,
+                self.config.dense_temporal_proportions,
+            ) = len(names), names, props
+            if self.config.annotation_mode == "dense_only":
+                logging.info(f"Using auto-generated sparse 'task' stage: {self.config.sparse_subtask_names}")
+
+    def to(self, device):
+        """Override to method to ensure all components move together."""
+        super().to(device)
+        self.device = device if isinstance(device, torch.device) else torch.device(device)
+        self.stage_model.to(device)
+        self.subtask_model.to(device)
+        return self
+
+    @torch.no_grad()
+    def calculate_rewards(
+        self,
+        text_embeddings: np.ndarray | torch.Tensor,
+        video_embeddings: np.ndarray | torch.Tensor,
+        state_features: np.ndarray | torch.Tensor | None = None,
+        lengths: np.ndarray | torch.Tensor | None = None,
+        return_all_frames: bool = False,
+        return_stages: bool = False,
+        return_confidence: bool = False,
+        head_mode: str | None = "sparse",
+        frame_index: int | None = None,
+    ) -> np.ndarray | tuple:
+        """
+        Calculate rewards for given text, video, and state representations.
+
+        This is the canonical method for SARM reward computation, used for:
+        - Inference/visualization
+        - RA-BC weight computation
+
+        Args:
+            text_embeddings: Encoded text representations (batch_size, 512)
+            video_embeddings: Encoded video representations (batch_size, num_frames, 512)
+            state_features: Joint state features (batch_size, num_frames, state_dim)
+            lengths: Valid sequence lengths (batch_size,)
+            return_all_frames: If True, return rewards for all frames
+            return_stages: If True, also return stage predictions
+            return_confidence: If True, also return stage confidence
+            head_mode: Which head to use ("sparse" or "dense")
+            frame_index: Index of the target frame to extract (default: n_obs_steps).
+
+        Returns:
+            Rewards and optionally stage probs/confidence.
+        """
+        if isinstance(text_embeddings, np.ndarray):
+            text_embeddings = torch.tensor(text_embeddings, dtype=torch.float32)
+        if isinstance(video_embeddings, np.ndarray):
+            video_embeddings = torch.tensor(video_embeddings, dtype=torch.float32)
+        if state_features is not None and isinstance(state_features, np.ndarray):
+            state_features = torch.tensor(state_features, dtype=torch.float32)
+
+        # Handle single sample case
+        if text_embeddings.dim() == 1:
+            text_embeddings = text_embeddings.unsqueeze(0)
+            video_embeddings = video_embeddings.unsqueeze(0)
+            if state_features is not None:
+                state_features = state_features.unsqueeze(0)
+            single_sample = True
+        else:
+            single_sample = False
+
+        batch_size = video_embeddings.shape[0]
+        seq_len = video_embeddings.shape[1]
+
+        scheme = head_mode
+
+        # Default lengths if not provided
+        if lengths is None:
+            lengths = torch.full((batch_size,), seq_len, dtype=torch.int32)
+        elif isinstance(lengths, np.ndarray):
+            lengths = torch.tensor(lengths, dtype=torch.int32)
+
+        # Reshape video to (B, N, T, D) for multi-camera format
+        # Currently single camera: (B, T, D) -> (B, 1, T, D)
+        img_seq = video_embeddings.unsqueeze(1).to(self.device)
+        lang_emb = text_embeddings.to(self.device)
+        state = (
+            state_features.to(self.device)
+            if state_features is not None
+            else torch.zeros(batch_size, seq_len, self.config.max_state_dim, device=self.device)
+        )
+        lens = lengths.to(self.device)
+
+        # Pad state to max_state_dim
+        state = pad_state_to_max_dim(state, self.config.max_state_dim)
+
+        # Get num_classes for this scheme
+        num_classes = self.config.num_sparse_stages if scheme == "sparse" else self.config.num_dense_stages
+
+        # Run stage model
+        stage_logits = self.stage_model(img_seq, lang_emb, state, lens, scheme=scheme)
+        stage_probs = F.softmax(stage_logits, dim=-1)  # (B, T, num_classes)
+        stage_idx = stage_probs.argmax(dim=-1)  # (B, T)
+        stage_conf = stage_probs.gather(-1, stage_idx.unsqueeze(-1)).squeeze(-1)  # (B, T)
+
+        # Create one-hot stage prior
+        stage_onehot = F.one_hot(stage_idx, num_classes=num_classes).float()  # (B, T, C)
+        stage_emb = stage_onehot.unsqueeze(1)  # (B, 1, T, C)
+
+        # Run subtask model
+        tau_pred = self.subtask_model(img_seq, lang_emb, state, lens, stage_emb, scheme=scheme)
+
+        # Compute final reward: stage + tau
+        raw_reward = stage_idx.float() + tau_pred  # (B, T)
+
+        # Normalize to [0, 1] using temporal proportions for proper weighting
+        if scheme == "sparse":
+            normalized_reward = normalize_stage_tau(
+                raw_reward,
+                num_stages=num_classes,
+                temporal_proportions=self.config.sparse_temporal_proportions,
+                subtask_names=self.config.sparse_subtask_names,
+            )
+        else:
+            normalized_reward = normalize_stage_tau(
+                raw_reward,
+                num_stages=num_classes,
+                temporal_proportions=self.config.dense_temporal_proportions,
+                subtask_names=self.config.dense_subtask_names,
+            )
+
+        # Default frame index is n_obs_steps (last observation frame)
+        if frame_index is None:
+            frame_index = self.config.n_obs_steps
+
+        # Prepare outputs (batch mode or no smoothing)
+        if return_all_frames:
+            rewards = normalized_reward.cpu().numpy()
+        else:
+            rewards = normalized_reward[:, frame_index].cpu().numpy()
+
+        if single_sample:
+            rewards = rewards[0] if not return_all_frames else rewards[0]
+
+        outputs = [rewards]
+        if return_stages:
+            probs = stage_probs.cpu().numpy()
+            if single_sample:
+                probs = probs[0]
+            outputs.append(probs)
+        if return_confidence:
+            conf = stage_conf.cpu().numpy()
+            if single_sample:
+                conf = conf[0]
+            outputs.append(conf)
+
+        return outputs[0] if len(outputs) == 1 else tuple(outputs)
+
+    def train(self, mode: bool = True):
+        """Set training mode for both models."""
+        super().train(mode)
+        self.stage_model.train(mode)
+        self.subtask_model.train(mode)
+        return self
+
+    def eval(self):
+        """Set evaluation mode for both models."""
+        return self.train(False)
+
+    def parameters(self):
+        """Override to return trainable parameters from both models."""
+        from itertools import chain
+
+        return chain(self.stage_model.parameters(), self.subtask_model.parameters())
+
+    def get_optim_params(self):
+        """Override to return optimizer parameters from both models."""
+        return self.parameters()
+
+    def reset(self):
+        """Required by PreTrainedPolicy but not used for reward models."""
+        pass
+
+    def predict_action_chunk(self, batch: dict[str, Tensor]) -> Tensor:
+        """Required by PreTrainedPolicy but not used for reward models."""
+        raise NotImplementedError("SARM model does not predict action chunks")
+
+    def select_action(self, batch: dict[str, Tensor]) -> Tensor:
+        """Required by PreTrainedPolicy but not used for SARM."""
+        raise NotImplementedError("SARM model does not select actions")
+
+    def _train_step(
+        self,
+        img_emb: torch.Tensor,  # (B, N, T, D)
+        lang_emb: torch.Tensor,  # (B, E) or (B, T, E)
+        state: torch.Tensor,  # (B, T, state_dim)
+        lengths: torch.Tensor,  # (B,)
+        targets: torch.Tensor,  # (B, T) - format: stage.tau
+        scheme: str,
+    ) -> dict[str, torch.Tensor]:
+        """
+        Single training step for one annotation scheme.
+
+        Implements 75%/25% GT/predicted stage conditioning.
+
+        Args:
+            img_emb: Image embeddings (B, N, T, D)
+            lang_emb: Language embeddings
+            state: State features
+            lengths: Valid sequence lengths
+            targets: Target values where floor=stage, remainder=tau
+            scheme: "sparse" or "dense"
+
+        Returns:
+            Dict with stage_loss, subtask_loss, total_loss
+        """
+        num_classes = self.config.num_sparse_stages if scheme == "sparse" else self.config.num_dense_stages
+
+        # Ground truth: stage (integer) and tau (fractional)
+        # Clamp stage indices to valid range [0, num_classes-1] to handle edge cases
+        # where targets may exceed expected range (e.g., frames between subtasks)
+        gt_stage = torch.floor(targets).long().clamp(0, num_classes - 1)  # (B, T)
+        gt_tau = torch.remainder(targets, 1.0)  # (B, T)
+
+        # Run stage model
+        stage_pred = self.stage_model(img_emb, lang_emb, state, lengths, scheme=scheme)
+
+        # 75%/25% GT/predicted stage conditioning
+        if random.random() < self.gt_stage_ratio:
+            # Mode 1: Use ground truth stage -> one-hot
+            stage_emb = gen_stage_emb(num_classes, targets)  # (B, 1, T, C)
+        else:
+            # Mode 2: Use predicted stage argmax -> one-hot
+            stage_idx = stage_pred.argmax(dim=-1)  # (B, T)
+            stage_onehot = F.one_hot(stage_idx, num_classes=num_classes).float()  # (B, T, C)
+            stage_emb = stage_onehot.unsqueeze(1)  # (B, 1, T, C)
+
+        # Run subtask model with stage prior
+        tau_pred = self.subtask_model(img_emb, lang_emb, state, lengths, stage_emb, scheme=scheme)
+
+        # Compute losses
+        stage_loss = F.cross_entropy(stage_pred.view(-1, num_classes), gt_stage.view(-1), reduction="mean")
+        subtask_loss = F.mse_loss(tau_pred, gt_tau, reduction="mean")
+
+        return {
+            "stage_loss": stage_loss,
+            "subtask_loss": subtask_loss,
+            "total_loss": stage_loss + subtask_loss,
+        }
+
+    def forward(self, batch):
+        """
+        Forward pass for SARM reward model training.
+
+        Uses stage+tau target format where:
+        - Integer part = stage index
+        - Fractional part = within-stage progress (tau)
+
+        Training uses 75%/25% GT/predicted stage conditioning.
+
+        Args:
+            batch: Dictionary with 'observation' containing:
+                - 'video_features': (B, T, 512) pre-encoded video features
+                - 'text_features': (B, 512) or (B, T, 512) text features
+                - 'state_features': (B, T, state_dim) joint state features
+                - 'lengths': (B,) valid sequence lengths
+                - 'sparse_targets': (B, T) sparse targets (stage.tau format)
+                - 'dense_targets': (B, T) dense targets (optional, for dual mode)
+
+        Returns:
+            Tuple of (total_loss, output_dict with loss components)
+        """
+        observation = batch.get("observation", batch)
+
+        # Extract features
+        video_features = observation["video_features"].to(self.device)
+        text_features = observation["text_features"].to(self.device)
+        state_features = observation.get("state_features")
+        if state_features is not None:
+            state_features = state_features.to(self.device)
+
+        batch_size = video_features.shape[0]
+        seq_len = video_features.shape[1]
+
+        # Get lengths (default to full sequence)
+        lengths = observation.get("lengths")
+        if lengths is None:
+            lengths = torch.full((batch_size,), seq_len, dtype=torch.int32, device=self.device)
+        else:
+            lengths = lengths.to(self.device)
+
+        # Reshape video to (B, N, T, D) - single camera
+        img_emb = video_features.unsqueeze(1)
+
+        # Pad state to max_state_dim
+        if state_features is None:
+            state_features = torch.zeros(batch_size, seq_len, self.config.max_state_dim, device=self.device)
+        else:
+            state_features = pad_state_to_max_dim(state_features, self.config.max_state_dim)
+
+        output_dict = {}
+        total_loss = torch.tensor(0.0, device=self.device)
+
+        # Sparse training (always)
+        sparse_targets = observation.get("sparse_targets")
+        if sparse_targets is None:
+            # Try legacy format
+            sparse_targets = observation.get("targets")
+        if sparse_targets is None:
+            raise ValueError("sparse_targets (or targets) is required for SARM training")
+        sparse_targets = sparse_targets.to(self.device)
+
+        sparse_result = self._train_step(
+            img_emb, text_features, state_features, lengths, sparse_targets, scheme="sparse"
+        )
+        output_dict["sparse_stage_loss"] = sparse_result["stage_loss"].item()
+        output_dict["sparse_subtask_loss"] = sparse_result["subtask_loss"].item()
+        total_loss = total_loss + sparse_result["total_loss"]
+
+        # Dense training (if dual mode)
+        if self.config.uses_dual_heads:
+            dense_targets = observation.get("dense_targets")
+            if dense_targets is not None:
+                dense_targets = dense_targets.to(self.device)
+                dense_result = self._train_step(
+                    img_emb, text_features, state_features, lengths, dense_targets, scheme="dense"
+                )
+                output_dict["dense_stage_loss"] = dense_result["stage_loss"].item()
+                output_dict["dense_subtask_loss"] = dense_result["subtask_loss"].item()
+                total_loss = total_loss + dense_result["total_loss"]
+
+        output_dict["total_loss"] = total_loss.item()
+        return total_loss, output_dict
+
+
+def compute_stage_loss(stage_logits: torch.Tensor, target_stages: torch.Tensor) -> torch.Tensor:
+    """Compute cross-entropy loss for stage classification."""
+    _, _, num_stages = stage_logits.shape
+    stage_logits_flat = stage_logits.reshape(-1, num_stages)
+    # Clamp target stage indices to valid range [0, num_stages-1]
+    target_stages_flat = target_stages.reshape(-1).clamp(0, num_stages - 1)
+    return F.cross_entropy(stage_logits_flat, target_stages_flat)
@@ -0,0 +1,518 @@
+#!/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.
+
+"""SARM Processor for encoding images/text and generating stage+tau targets."""
+
+import random
+from typing import Any
+
+import numpy as np
+import pandas as pd
+import torch
+from faker import Faker
+from PIL import Image
+from transformers import CLIPModel, CLIPProcessor
+
+from lerobot.configs.types import FeatureType, PolicyFeature
+from lerobot.policies.sarm.configuration_sarm import SARMConfig
+from lerobot.policies.sarm.sarm_utils import (
+    apply_rewind_augmentation,
+    compute_absolute_indices,
+    find_stage_and_tau,
+    pad_state_to_max_dim,
+)
+from lerobot.processor import (
+    AddBatchDimensionProcessorStep,
+    DeviceProcessorStep,
+    NormalizerProcessorStep,
+    PolicyAction,
+    PolicyProcessorPipeline,
+    ProcessorStep,
+    RenameObservationsProcessorStep,
+)
+from lerobot.processor.converters import (
+    from_tensor_to_numpy,
+    policy_action_to_transition,
+    transition_to_policy_action,
+)
+from lerobot.processor.core import EnvTransition, TransitionKey
+from lerobot.processor.pipeline import PipelineFeatureType
+from lerobot.utils.constants import POLICY_POSTPROCESSOR_DEFAULT_NAME, POLICY_PREPROCESSOR_DEFAULT_NAME
+
+
+class SARMEncodingProcessorStep(ProcessorStep):
+    """ProcessorStep that encodes images and text with CLIP and generates stage and progress labels for SARM."""
+
+    def __init__(
+        self,
+        config: SARMConfig,
+        image_key: str | None = None,
+        dataset_meta=None,
+        dataset_stats: dict | None = None,
+    ):
+        super().__init__()
+        self.config = config
+        self.image_key = image_key or config.image_key
+        self.dataset_meta = dataset_meta
+        self.dataset_stats = dataset_stats
+        self.annotation_mode = config.annotation_mode
+
+        # Helper to create temporal proportions dict
+        def make_props_dict(names, props):
+            return dict(zip(names, props, strict=True)) if names and props else None
+
+        # Sparse annotations (always needed)
+        self.sparse_temporal_proportions = make_props_dict(
+            config.sparse_subtask_names, config.sparse_temporal_proportions
+        )
+        self.sparse_subtask_names = config.sparse_subtask_names
+
+        # Dense annotations (only for dual mode)
+        self.dense_subtask_names = config.dense_subtask_names if config.uses_dual_heads else None
+        self.dense_temporal_proportions = (
+            make_props_dict(config.dense_subtask_names, config.dense_temporal_proportions)
+            if config.uses_dual_heads
+            else None
+        )
+
+        self.device = torch.device(
+            self.config.device if self.config.device else "cuda" if torch.cuda.is_available() else "cpu"
+        )
+
+        self.clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+        self.clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32", use_fast=True)
+        self.clip_model.to(self.device)
+        self.clip_model.eval()
+
+        self.verbs = ["move", "grasp", "rotate", "push", "pull", "slide", "lift", "place"]
+        self.fake = Faker()
+
+    def _find_episode_for_frame(self, frame_idx: int) -> int:
+        """Find the episode index for a given frame index."""
+        for ep_idx in range(len(self.dataset_meta.episodes)):
+            ep_start = self.dataset_meta.episodes[ep_idx]["dataset_from_index"]
+            ep_end = self.dataset_meta.episodes[ep_idx]["dataset_to_index"]
+            if ep_start <= frame_idx < ep_end:
+                return ep_idx
+        return 0
+
+    def _get_episode_indices(self, frame_indices: np.ndarray, episode_index) -> np.ndarray:
+        """Get episode indices for each frame index."""
+        if episode_index is None:
+            return np.array([self._find_episode_for_frame(int(f)) for f in frame_indices])
+
+        episode_indices = np.atleast_1d(np.asarray(from_tensor_to_numpy(episode_index)))
+
+        # If single episode but multiple frames, compute episode for each frame
+        if len(episode_indices) == 1 and len(frame_indices) > 1:
+            return np.array([self._find_episode_for_frame(int(f)) for f in frame_indices])
+
+        return episode_indices
+
+    def _generate_perturbed_task(self) -> str:
+        """Generate a random perturbed task string for language perturbation."""
+        num_words = random.randint(1, 5)
+        verb = random.choice(self.verbs)
+        phrase = " ".join([verb] + self.fake.words(nb=num_words))
+        return phrase
+
+    def _get_annotation_config(self, annotation_type: str) -> tuple[list[str], dict[str, float] | None]:
+        """Get global subtask names and temporal proportions for an annotation type."""
+        if annotation_type == "dense":
+            return self.dense_subtask_names, self.dense_temporal_proportions
+        return self.sparse_subtask_names, self.sparse_temporal_proportions
+
+    def _load_episode_annotations(
+        self,
+        ep_idx: int,
+        episodes_df: pd.DataFrame | None,
+        annotation_type: str,
+        global_names: list[str],
+    ) -> tuple[list | None, list | None, list | None]:
+        """Load subtask annotations for an episode from DataFrame."""
+        # Single-stage mode: (linear progress 0→1)
+        if episodes_df is None or len(global_names) == 1:
+            return None, None, None
+
+        # Resolve column name with fallback
+        def col(suffix):
+            prefixed = f"{annotation_type}_{suffix}"
+            return prefixed if prefixed in episodes_df.columns else suffix
+
+        col_names = col("subtask_names")
+        if col_names not in episodes_df.columns or ep_idx >= len(episodes_df):
+            return None, None, None
+
+        subtask_names = episodes_df.loc[ep_idx, col_names]
+        if subtask_names is None or (isinstance(subtask_names, float) and pd.isna(subtask_names)):
+            return None, None, None
+
+        return (
+            subtask_names,
+            episodes_df.loc[ep_idx, col("subtask_start_frames")],
+            episodes_df.loc[ep_idx, col("subtask_end_frames")],
+        )
+
+    def __call__(self, transition: EnvTransition) -> EnvTransition:
+        """
+        Encode images, text, and normalize states in the transition.
+
+        Implements SARM training data preparation:
+        - Applies language perturbation (20% probability)
+        - Applies rewind augmentation (80% probability)
+        - Generates stage+tau targets for all frames
+        - Outputs lengths tensor for valid sequence masking
+        """
+        new_transition = transition.copy() if hasattr(transition, "copy") else dict(transition)
+        observation = new_transition.get(TransitionKey.OBSERVATION)
+        comp_data = new_transition.get(TransitionKey.COMPLEMENTARY_DATA, {})
+
+        frame_index = comp_data.get("index")
+        episode_index = comp_data.get("episode_index")
+
+        if frame_index is None:
+            raise ValueError("Frame index ('index') not found in COMPLEMENTARY_DATA")
+        if episode_index is None:
+            raise ValueError("Episode index ('episode_index') not found in COMPLEMENTARY_DATA")
+
+        frame_indices = np.atleast_1d(np.asarray(from_tensor_to_numpy(frame_index)))
+        episode_indices = self._get_episode_indices(frame_indices, episode_index)
+
+        image = observation.get(self.image_key)
+        if isinstance(image, torch.Tensor):
+            image = image.cpu().numpy()
+
+        # If 4D (T, C, H, W) from delta_timestamps, add batch dim
+        # If 3D (C, H, W) single frame, add batch and time dims
+        if image.ndim == 4:
+            image = image[np.newaxis, ...]  # (T, C, H, W) -> (1, T, C, H, W)
+        elif image.ndim == 3:
+            image = image[np.newaxis, np.newaxis, ...]  # (C, H, W) -> (1, 1, C, H, W)
+
+        batch_size = image.shape[0]
+        total_frames = image.shape[1]  # Should be 13: 9 obs + 4 rewind placeholders
+        n_obs_steps = self.config.n_obs_steps
+        max_rewind_steps = self.config.max_rewind_steps
+        n_obs_frames = 1 + n_obs_steps  # 9 observation frames (including current)
+
+        # Rewind augmentation
+        rewind_steps = torch.zeros(batch_size, dtype=torch.int32)
+        apply_rewind = self.training and random.random() < self.config.rewind_probability
+
+        if apply_rewind and self.dataset_meta is not None:
+            for b_idx, (ep_idx, frame_idx) in enumerate(
+                zip(episode_indices.tolist(), frame_indices.tolist(), strict=True)
+            ):
+                ep_idx, frame_idx = int(ep_idx), int(frame_idx)
+                ep_start = self.dataset_meta.episodes[ep_idx]["dataset_from_index"]
+
+                rewind_step, _ = apply_rewind_augmentation(
+                    frame_idx, ep_start, n_obs_steps, max_rewind_steps, frame_gap=self.config.frame_gap
+                )
+                rewind_steps[b_idx] = rewind_step
+
+        # Compute valid lengths: n_obs_frames + rewind_steps
+        lengths = n_obs_frames + rewind_steps  # (B,)
+
+        # Apply rewind masking to images
+        # For frames beyond valid length, we mask with zeros (or copy last valid frame)
+        for b_idx in range(batch_size):
+            valid_len = lengths[b_idx].item()
+            if valid_len < total_frames:
+                image[b_idx, valid_len:] = 0  # Zero out frames beyond valid length
+
+        # Encode images with CLIP
+        video_features = self._encode_images_batch(image)
+        observation["video_features"] = video_features
+
+        state_key = self.config.state_key
+        state_data = observation.get(state_key)
+
+        if isinstance(state_data, torch.Tensor):
+            state_tensor = state_data.float()
+        else:
+            state_tensor = torch.tensor(state_data, dtype=torch.float32)
+
+        if state_tensor.ndim == 2:
+            state_tensor = state_tensor.unsqueeze(0)  # (T, D) -> (1, T, D)
+        elif state_tensor.ndim == 1:
+            state_tensor = state_tensor.unsqueeze(0).unsqueeze(0)  # (D,) -> (1, 1, D)
+
+        # Apply same rewind masking to state
+        for b_idx in range(batch_size):
+            valid_len = lengths[b_idx].item()
+            if valid_len < state_tensor.shape[1]:
+                state_tensor[b_idx, valid_len:] = 0  # Zero out frames beyond valid length
+
+        observation["state_features"] = pad_state_to_max_dim(state_tensor, self.config.max_state_dim)
+
+        task = comp_data.get("task")
+        if isinstance(task, list):
+            task = task[0] if task else ""
+
+        # Apply language perturbation during training (20% probability)
+        # When perturbed, targets will be zeroed to train model to output low values for irrelevant text
+        apply_perturbation = self.training and random.random() < self.config.language_perturbation_probability
+        if apply_perturbation:
+            task = self._generate_perturbed_task()
+
+        # Encode text with CLIP
+        observation["text_features"] = self._encode_text_clip(task, batch_size)
+
+        # Store lengths for model
+        observation["lengths"] = lengths
+
+        # When language is perturbed, targets are zero so perturbed samples don't contribute to progress loss
+        if self.dataset_meta is not None:
+            episodes_df = None
+            if self.sparse_subtask_names != ["task"]:
+                episodes_df = self.dataset_meta.episodes.to_pandas()
+
+            # Generate sparse targets
+            if self.sparse_temporal_proportions is not None:
+                if apply_perturbation:
+                    # Zero targets when language is perturbed
+                    sparse_targets = torch.zeros(batch_size, total_frames, dtype=torch.float32)
+                else:
+                    sparse_targets = self._compute_batch_targets(
+                        frame_indices, episode_indices, lengths, rewind_steps, episodes_df, "sparse"
+                    )
+                observation["sparse_targets"] = sparse_targets
+
+            # Generate dense targets (for dual mode)
+            if self.config.uses_dual_heads and self.dense_temporal_proportions is not None:
+                if apply_perturbation:
+                    # Zero targets when language is perturbed
+                    dense_targets = torch.zeros(batch_size, total_frames, dtype=torch.float32)
+                else:
+                    dense_targets = self._compute_batch_targets(
+                        frame_indices, episode_indices, lengths, rewind_steps, episodes_df, "dense"
+                    )
+                observation["dense_targets"] = dense_targets
+
+        new_transition[TransitionKey.OBSERVATION] = observation
+        return new_transition
+
+    def _compute_batch_targets(
+        self,
+        frame_indices: np.ndarray,
+        episode_indices: np.ndarray,
+        lengths: torch.Tensor,
+        rewind_steps: torch.Tensor,
+        episodes_df: pd.DataFrame | None,
+        annotation_type: str,
+    ) -> torch.Tensor:
+        """Compute stage+tau targets for a batch of samples."""
+        batch_size = len(frame_indices)
+        n_obs_steps = self.config.n_obs_steps
+        max_rewind_steps = self.config.max_rewind_steps
+        total_frames = 1 + n_obs_steps + max_rewind_steps
+        frame_gap = self.config.frame_gap
+
+        global_names, temporal_props = self._get_annotation_config(annotation_type)
+        targets = torch.zeros(batch_size, total_frames, dtype=torch.float32)
+
+        for b_idx in range(batch_size):
+            ep_idx = int(episode_indices[b_idx])
+            frame_idx = int(frame_indices[b_idx])
+
+            ep_start = self.dataset_meta.episodes[ep_idx]["dataset_from_index"]
+            ep_end = self.dataset_meta.episodes[ep_idx]["dataset_to_index"]
+            ep_length = ep_end - ep_start
+
+            subtask_names, subtask_start_frames, subtask_end_frames = self._load_episode_annotations(
+                ep_idx, episodes_df, annotation_type, global_names
+            )
+
+            # Compute observation frame indices
+            obs_indices, _ = compute_absolute_indices(
+                frame_idx, ep_start, ep_end, n_obs_steps, frame_gap=frame_gap
+            )
+            obs_indices = obs_indices.tolist()
+
+            # Compute targets for observation frames
+            for t_idx, abs_idx in enumerate(obs_indices):
+                rel_frame = abs_idx - ep_start
+                targets[b_idx, t_idx] = find_stage_and_tau(
+                    rel_frame,
+                    ep_length,
+                    subtask_names,
+                    subtask_start_frames,
+                    subtask_end_frames,
+                    global_names,
+                    temporal_props,
+                    return_combined=True,
+                )
+
+            # Compute targets for rewind frames (if any)
+            rewind_step = rewind_steps[b_idx].item()
+            if rewind_step > 0:
+                _, rewind_indices = apply_rewind_augmentation(
+                    frame_idx,
+                    ep_start,
+                    n_obs_steps,
+                    max_rewind_steps,
+                    frame_gap=frame_gap,
+                    rewind_step=rewind_step,
+                )
+
+                for r_idx, abs_idx in enumerate(rewind_indices[:rewind_step]):
+                    rel_frame = max(0, abs_idx - ep_start)
+                    targets[b_idx, n_obs_steps + 1 + r_idx] = find_stage_and_tau(
+                        rel_frame,
+                        ep_length,
+                        subtask_names,
+                        subtask_start_frames,
+                        subtask_end_frames,
+                        global_names,
+                        temporal_props,
+                        return_combined=True,
+                    )
+
+        return targets
+
+    @property
+    def training(self) -> bool:
+        return getattr(self, "_training_mode", True)
+
+    def train(self, mode: bool = True):
+        """Set training mode for augmentation decisions."""
+        self._training_mode = mode
+        return self
+
+    def eval(self):
+        """Set evaluation mode (disable augmentations)."""
+        return self.train(False)
+
+    @torch.no_grad()
+    def _encode_images_batch(self, images: np.ndarray) -> torch.Tensor:
+        """Encode a batch of images using CLIP.
+
+        Args:
+            images: Batched images with shape: (B, T, C, H, W)
+
+        Returns:
+            Encoded feature vectors with shape (B, T, 512)
+        """
+
+        batch_size, seq_length = images.shape[0], images.shape[1]
+        images = images.reshape(batch_size * seq_length, *images.shape[2:])
+
+        num_frames = images.shape[0]
+        images_list = []
+        for i in range(num_frames):
+            img = images[i]
+            if img.shape[0] in [1, 3]:  # Channel first (C, H, W)
+                img = img.transpose(1, 2, 0)
+
+            # Handle single channel
+            if img.shape[-1] == 1:
+                img = np.repeat(img, 3, axis=-1)
+
+            if img.dtype != np.uint8:
+                img = (img * 255).astype(np.uint8) if img.max() <= 1.0 else img.astype(np.uint8)
+
+            images_list.append(Image.fromarray(img))
+
+        all_embeddings = []
+        for i in range(0, num_frames, self.config.clip_batch_size):
+            batch_imgs = images_list[i : i + self.config.clip_batch_size]
+
+            inputs = self.clip_processor(images=batch_imgs, return_tensors="pt")
+            inputs = {k: v.to(self.device) for k, v in inputs.items()}
+
+            # Get image embeddings
+            embeddings = self.clip_model.get_image_features(**inputs).detach().cpu()
+
+            # Handle single frame case
+            if embeddings.dim() == 1:
+                embeddings = embeddings.unsqueeze(0)
+
+            all_embeddings.append(embeddings)
+
+        all_embeddings = torch.cat(all_embeddings)  # (B*T, 512)
+        all_embeddings = all_embeddings.reshape(batch_size, seq_length, -1)  # (B, T, 512)
+
+        return all_embeddings
+
+    @torch.no_grad()
+    def _encode_text_clip(self, text: str, batch_size: int) -> torch.Tensor:
+        """Encode text using CLIP text encoder (per SARM paper A.4).
+
+        Args:
+            text: Task description text to encode
+            batch_size: Batch size to replicate for
+
+        Returns:
+            Encoded text features with shape (B, 512)
+        """
+        inputs = self.clip_processor.tokenizer([text], return_tensors="pt", padding=True, truncation=True)
+        inputs = {k: v.to(self.device) for k, v in inputs.items()}
+
+        text_embedding = self.clip_model.get_text_features(**inputs).detach().cpu()
+        text_embedding = text_embedding.expand(batch_size, -1)
+
+        return text_embedding
+
+    def transform_features(
+        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
+    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
+        """Add encoded features to the observation features."""
+        features[PipelineFeatureType.OBSERVATION]["video_features"] = PolicyFeature(
+            type=FeatureType.VISUAL, shape=(self.config.num_frames, self.config.image_dim)
+        )
+        features[PipelineFeatureType.OBSERVATION]["text_features"] = PolicyFeature(
+            type=FeatureType.LANGUAGE, shape=(self.config.text_dim,)
+        )
+        features[PipelineFeatureType.OBSERVATION]["state_features"] = PolicyFeature(
+            type=FeatureType.STATE, shape=(self.config.num_frames, self.config.max_state_dim)
+        )
+        return features
+
+
+def make_sarm_pre_post_processors(
+    config: SARMConfig,
+    dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None,
+    dataset_meta=None,
+) -> tuple[
+    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
+    PolicyProcessorPipeline[PolicyAction, PolicyAction],
+]:
+    """Create pre-processor and post-processor pipelines for SARM."""
+    return (
+        PolicyProcessorPipeline[dict[str, Any], dict[str, Any]](
+            steps=[
+                AddBatchDimensionProcessorStep(),
+                RenameObservationsProcessorStep(rename_map={}),
+                NormalizerProcessorStep(
+                    features={**config.input_features, **config.output_features},
+                    norm_map=config.normalization_mapping,
+                    stats=dataset_stats,
+                ),
+                SARMEncodingProcessorStep(
+                    config=config, dataset_meta=dataset_meta, dataset_stats=dataset_stats
+                ),
+                DeviceProcessorStep(device=config.device),
+            ],
+            name=POLICY_PREPROCESSOR_DEFAULT_NAME,
+        ),
+        PolicyProcessorPipeline[PolicyAction, PolicyAction](
+            steps=[DeviceProcessorStep(device="cpu")],
+            name=POLICY_POSTPROCESSOR_DEFAULT_NAME,
+            to_transition=policy_action_to_transition,
+            to_output=transition_to_policy_action,
+        ),
+    )
@@ -0,0 +1,295 @@
+#!/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.
+
+import random
+
+import numpy as np
+import torch
+import torch.nn.functional as F  # noqa: N812
+
+
+def find_stage_and_tau(
+    current_frame: int,
+    episode_length: int,
+    subtask_names: list | None,
+    subtask_start_frames: list | None,
+    subtask_end_frames: list | None,
+    global_subtask_names: list,
+    temporal_proportions: dict,
+    return_combined: bool = False,
+) -> tuple[int, float] | float:
+    """Find stage and within-stage progress (tau) for a frame.
+
+    Args:
+        current_frame: Frame index relative to episode start
+        episode_length: Total frames in episode
+        subtask_names: Subtask names for this episode (None for single_stage)
+        subtask_start_frames: Subtask start frames
+        subtask_end_frames: Subtask end frames
+        global_subtask_names: Global list of all subtask names
+        temporal_proportions: Dict of temporal proportions
+        return_combined: If True, return stage+tau as float; else (stage_idx, tau) tuple
+
+    Returns:
+        Float (stage.tau) if return_combined, else (stage_idx, tau) tuple
+    """
+    stage_idx, tau = 0, 0.0
+    num_stages = len(global_subtask_names)
+
+    # Single-stage mode: linear progress from 0 to 1
+    if num_stages == 1:
+        tau = min(1.0, max(0.0, current_frame / max(episode_length - 1, 1)))
+    elif subtask_names is None:
+        pass  # stage_idx=0, tau=0.0
+    elif current_frame < subtask_start_frames[0]:
+        pass  # Before first subtask: stage_idx=0, tau=0.0
+    elif current_frame > subtask_end_frames[-1]:
+        stage_idx, tau = num_stages - 1, 0.999  # After last subtask
+    else:
+        # Find which subtask this frame belongs to
+        found = False
+        for name, start, end in zip(subtask_names, subtask_start_frames, subtask_end_frames, strict=True):
+            if start <= current_frame <= end:
+                stage_idx = global_subtask_names.index(name) if name in global_subtask_names else 0
+                tau = compute_tau(current_frame, start, end)
+                found = True
+                break
+        # Frame between subtasks - use previous subtask's end state
+        if not found:
+            for j in range(len(subtask_names) - 1):
+                if subtask_end_frames[j] < current_frame < subtask_start_frames[j + 1]:
+                    name = subtask_names[j]
+                    stage_idx = global_subtask_names.index(name) if name in global_subtask_names else j
+                    tau = 1.0
+                    break
+
+    if return_combined:
+        # Clamp to avoid overflow at end
+        if stage_idx >= num_stages - 1 and tau >= 1.0:
+            return num_stages - 1 + 0.999
+        return stage_idx + tau
+    return stage_idx, tau
+
+
+def compute_absolute_indices(
+    frame_idx: int,
+    ep_start: int,
+    ep_end: int,
+    n_obs_steps: int,
+    frame_gap: int = 30,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Compute absolute frame indices with clamping for bidirectional observation sequence.
+
+    Bidirectional sampling centered on target frame:
+    - Before: [-frame_gap * half_steps, ..., -frame_gap] (half_steps frames)
+    - Current: [0] (1 frame)
+    - After: [frame_gap, ..., frame_gap * half_steps] (half_steps frames)
+    - Total: n_obs_steps + 1 frames
+
+    Out-of-bounds frames are clamped (duplicated from boundary).
+
+    Args:
+        frame_idx: Target frame index (center frame of sequence)
+        ep_start: Episode start index
+        ep_end: Episode end index (exclusive)
+        n_obs_steps: Number of observation steps (must be even for symmetric sampling)
+        frame_gap: Gap between observation frames
+
+    Returns:
+        Tuple of (indices, out_of_bounds_flags)
+    """
+    half_steps = n_obs_steps // 2
+
+    # Bidirectional deltas: past + current + future
+    past_deltas = [-frame_gap * i for i in range(half_steps, 0, -1)]
+    future_deltas = [frame_gap * i for i in range(1, half_steps + 1)]
+    delta_indices = past_deltas + [0] + future_deltas
+
+    frames = []
+    out_of_bounds = []
+
+    for delta in delta_indices:
+        target_idx = frame_idx + delta
+        # Clamp to episode bounds (duplicate boundary frames for out-of-bounds)
+        clamped_idx = max(ep_start, min(ep_end - 1, target_idx))
+        frames.append(clamped_idx)
+        # Flag as out-of-bounds if clamping occurred
+        out_of_bounds.append(1 if target_idx != clamped_idx else 0)
+
+    return torch.tensor(frames), torch.tensor(out_of_bounds)
+
+
+def apply_rewind_augmentation(
+    frame_idx: int,
+    ep_start: int,
+    n_obs_steps: int,
+    max_rewind_steps: int,
+    frame_gap: int = 30,
+    rewind_step: int | None = None,
+) -> tuple[int, list[int]]:
+    """
+    Generate rewind frame indices for temporal augmentation.
+
+    Rewind simulates going backwards through previously seen frames,
+    starting from before the earliest observation frame (for bidirectional sampling).
+    Appends reversed frames after the observation sequence.
+
+    Args:
+        frame_idx: Target frame index (center of bidirectional observation window)
+        ep_start: Episode start index
+        n_obs_steps: Number of observation steps
+        max_rewind_steps: Maximum rewind steps
+        frame_gap: Gap between frames
+        rewind_step: If provided, use this exact rewind step (for deterministic behavior).
+                     If None, sample randomly.
+
+    Returns:
+        Tuple of (rewind_step, rewind_indices)
+    """
+    # For bidirectional sampling, earliest obs frame is at frame_idx - half_steps * frame_gap
+    half_steps = n_obs_steps // 2
+    earliest_obs_frame = frame_idx - half_steps * frame_gap
+
+    # Required history: frames before earliest observation frame
+    if earliest_obs_frame <= ep_start:
+        return 0, []  # No history before observation window
+
+    # Max valid rewind steps based on available history before earliest obs frame
+    available_history = earliest_obs_frame - ep_start
+    max_valid_step = available_history // frame_gap
+    max_rewind = min(max_rewind_steps, max(0, max_valid_step))
+
+    if max_rewind <= 0:
+        return 0, []
+
+    # Sample rewind steps if not provided
+    rewind_step = random.randint(1, max_rewind) if rewind_step is None else min(rewind_step, max_rewind)
+
+    if rewind_step == 0:
+        return 0, []
+
+    # Generate rewind indices going backwards from earliest obs frame
+    # rewind_indices[0] is closest to obs window, rewind_indices[-1] is furthest back
+    rewind_indices = []
+    for i in range(1, rewind_step + 1):
+        idx = earliest_obs_frame - i * frame_gap
+        idx = max(ep_start, idx)  # Clamp to episode start
+        rewind_indices.append(idx)
+
+    return rewind_step, rewind_indices
+
+
+def compute_tau(current_frame: int | float, subtask_start: int | float, subtask_end: int | float) -> float:
+    """Compute τ_t = (t - s_k) / (e_k - s_k) ∈ [0, 1]. Returns 1.0 for zero-duration subtasks."""
+    duration = subtask_end - subtask_start
+    if duration <= 0:
+        return 1.0
+    return float(np.clip((current_frame - subtask_start) / duration, 0.0, 1.0))
+
+
+def pad_state_to_max_dim(state: torch.Tensor, max_state_dim: int) -> torch.Tensor:
+    """Pad the state tensor's last dimension to max_state_dim with zeros."""
+    current_dim = state.shape[-1]
+    if current_dim >= max_state_dim:
+        return state[..., :max_state_dim]  # Truncate if larger
+
+    # Pad with zeros on the right
+    padding = (0, max_state_dim - current_dim)  # (left, right) for last dim
+    return F.pad(state, padding, mode="constant", value=0)
+
+
+def temporal_proportions_to_breakpoints(
+    temporal_proportions: dict[str, float] | list[float] | None,
+    subtask_names: list[str] | None = None,
+) -> list[float] | None:
+    """Convert temporal proportions to cumulative breakpoints for normalization."""
+    if temporal_proportions is None:
+        return None
+
+    if isinstance(temporal_proportions, dict):
+        if subtask_names is not None:
+            proportions = [temporal_proportions.get(name, 0.0) for name in subtask_names]
+        else:
+            proportions = list(temporal_proportions.values())
+    else:
+        proportions = list(temporal_proportions)
+
+    total = sum(proportions)
+    if total > 0 and abs(total - 1.0) > 1e-6:
+        proportions = [p / total for p in proportions]
+
+    breakpoints = [0.0]
+    cumsum = 0.0
+    for prop in proportions:
+        cumsum += prop
+        breakpoints.append(cumsum)
+    breakpoints[-1] = 1.0
+
+    return breakpoints
+
+
+def normalize_stage_tau(
+    x: float | torch.Tensor,
+    num_stages: int | None = None,
+    breakpoints: list[float] | None = None,
+    temporal_proportions: dict[str, float] | list[float] | None = None,
+    subtask_names: list[str] | None = None,
+) -> float | torch.Tensor:
+    """
+    Normalize stage+tau reward to [0, 1] with custom breakpoints.
+
+    Maps stage index + within-stage tau to normalized progress [0, 1].
+    The breakpoints are designed to give appropriate weight to each stage
+    based on their importance in the task (using temporal proportions).
+
+    Priority: breakpoints > temporal_proportions > linear fallback
+
+    Args:
+        x: Raw reward value (stage index + tau) where stage ∈ [0, num_stages-1] and tau ∈ [0, 1)
+        num_stages: Number of stages (required if breakpoints/proportions not provided)
+        breakpoints: Optional custom breakpoints list of length num_stages + 1.
+        temporal_proportions: Optional temporal proportions dict/list to compute breakpoints.
+        subtask_names: Optional ordered list of subtask names (for dict proportions)
+
+    Returns:
+        Normalized progress value ∈ [0, 1]
+    """
+    if breakpoints is not None:
+        num_stages = len(breakpoints) - 1
+    elif temporal_proportions is not None:
+        breakpoints = temporal_proportions_to_breakpoints(temporal_proportions, subtask_names)
+        num_stages = len(breakpoints) - 1
+    elif num_stages is not None:
+        breakpoints = [i / num_stages for i in range(num_stages + 1)]
+    else:
+        raise ValueError("Either num_stages, breakpoints, or temporal_proportions must be provided")
+
+    if isinstance(x, torch.Tensor):
+        result = torch.zeros_like(x)
+        for i in range(num_stages):
+            mask = (x >= i) & (x < i + 1)
+            tau_in_stage = x - i
+            result[mask] = breakpoints[i] + tau_in_stage[mask] * (breakpoints[i + 1] - breakpoints[i])
+        result[x >= num_stages] = 1.0
+        return result.clamp(0.0, 1.0)
+    else:
+        if x < 0:
+            return 0.0
+        if x >= num_stages:
+            return 1.0
+        stage = int(x)
+        tau = x - stage
+        return breakpoints[stage] + tau * (breakpoints[stage + 1] - breakpoints[stage])
@@ -231,6 +231,7 @@ class SmolVLAPolicy(PreTrainedPolicy):
    def __init__(
        self,
        config: SmolVLAConfig,
+        **kwargs,
    ):
        """
        Args:
@@ -352,8 +353,19 @@ class SmolVLAPolicy(PreTrainedPolicy):
    def _rtc_enabled(self) -> bool:
        return self.config.rtc_config is not None and self.config.rtc_config.enabled

-    def forward(self, batch: dict[str, Tensor], noise=None, time=None) -> dict[str, Tensor]:
-        """Do a full training forward pass to compute the loss"""
+    def forward(
+        self, batch: dict[str, Tensor], noise=None, time=None, reduction: str = "mean"
+    ) -> dict[str, Tensor]:
+        """Do a full training forward pass to compute the loss.
+
+        Args:
+            batch: Training batch containing observations and actions.
+            noise: Optional noise tensor for flow matching.
+            time: Optional time tensor for flow matching.
+            reduction: How to reduce the loss. Options:
+                - "mean": Return scalar mean loss (default, backward compatible)
+                - "none": Return per-sample losses of shape (batch_size,) for RA-BC weighting
+        """
        if self.config.adapt_to_pi_aloha:
            batch[OBS_STATE] = self._pi_aloha_decode_state(batch[OBS_STATE])
            batch[ACTION] = self._pi_aloha_encode_actions_inv(batch[ACTION])
@@ -377,11 +389,16 @@ class SmolVLAPolicy(PreTrainedPolicy):
        losses = losses[:, :, : self.config.max_action_dim]
        loss_dict["losses_after_rm_padding"] = losses.clone()

-        # For backward pass
-        loss = losses.mean()
-        # For backward pass
-        loss_dict["loss"] = loss.item()
-        return loss, loss_dict
+        if reduction == "none":
+            # Return per-sample losses (B,) by averaging over time and action dims
+            per_sample_loss = losses.mean(dim=(1, 2))
+            loss_dict["loss"] = per_sample_loss.mean().item()
+            return per_sample_loss, loss_dict
+        else:
+            # Default: return scalar mean loss
+            loss = losses.mean()
+            loss_dict["loss"] = loss.item()
+            return loss, loss_dict

    def prepare_images(self, batch):
        """Apply SmolVLA preprocessing to the images, like resizing to 224x224 and padding to keep aspect ratio, and
@@ -527,6 +544,7 @@ class VLAFlowMatching(nn.Module):
            num_vlm_layers=self.config.num_vlm_layers,
            self_attn_every_n_layers=self.config.self_attn_every_n_layers,
            expert_width_multiplier=self.config.expert_width_multiplier,
+            device=self.config.device if self.config.device is not None else "auto",
        )
        self.state_proj = nn.Linear(
            self.config.max_state_dim, self.vlm_with_expert.config.text_config.hidden_size
@@ -783,18 +801,15 @@ class VLAFlowMatching(nn.Module):
            use_cache=self.config.use_cache,
            fill_kv_cache=True,
        )
-        dt = -1.0 / self.config.num_steps
-        dt = torch.tensor(dt, dtype=torch.float32, device=device)
+        num_steps = self.config.num_steps
+        dt = -1.0 / num_steps

        x_t = noise
-        time = torch.tensor(1.0, dtype=torch.float32, device=device)
+        for step in range(num_steps):
+            time = 1.0 + step * dt
+            time_tensor = torch.tensor(time, dtype=torch.float32, device=device).expand(bsize)

-        while time >= -dt / 2:
-            expanded_time = time.expand(bsize)
-
-            # Define a closure function to properly capture expanded_time
-            # This avoids the lambda expression (E731) and loop variable binding (B023) issues
-            def denoise_step_partial_call(input_x_t, current_timestep=expanded_time):
+            def denoise_step_partial_call(input_x_t, current_timestep=time_tensor):
                return self.denoise_step(
                    x_t=input_x_t,
                    prefix_pad_masks=prefix_pad_masks,
@@ -818,15 +833,11 @@ class VLAFlowMatching(nn.Module):
            else:
                v_t = denoise_step_partial_call(x_t)

-            # Euler step
-            x_t += dt * v_t
+            x_t = x_t + dt * v_t

-            # Record x_t and v_t after Euler step (other params are recorded in rtc_processor.denoise_step)
            if self.rtc_processor is not None and self.rtc_processor.is_debug_enabled():
                self.rtc_processor.track(time=time, x_t=x_t, v_t=v_t)

-            time += dt
-
        return x_t

    def denoise_step(
@@ -65,6 +65,7 @@ class TDMPCPolicy(PreTrainedPolicy):
    def __init__(
        self,
        config: TDMPCConfig,
+        **kwargs,
    ):
        """
        Args:
@@ -231,11 +231,20 @@ def validate_visual_features_consistency(
    """
    Validates visual feature consistency between a policy config and provided dataset/environment features.

+    Validation passes if EITHER:
+    - Policy's expected visuals are a subset of dataset (policy uses some cameras, dataset has more)
+    - Dataset's provided visuals are a subset of policy (policy declares extras for flexibility)
+
    Args:
        cfg (PreTrainedConfig): The model or policy configuration containing input_features and type.
        features (Dict[str, PolicyFeature]): A mapping of feature names to PolicyFeature objects.
    """
    expected_visuals = {k for k, v in cfg.input_features.items() if v.type == FeatureType.VISUAL}
    provided_visuals = {k for k, v in features.items() if v.type == FeatureType.VISUAL}
-    if not provided_visuals.issubset(expected_visuals):
+
+    # Accept if either direction is a subset
+    policy_subset_of_dataset = expected_visuals.issubset(provided_visuals)
+    dataset_subset_of_policy = provided_visuals.issubset(expected_visuals)
+
+    if not (policy_subset_of_dataset or dataset_subset_of_policy):
        raise_feature_mismatch_error(provided_visuals, expected_visuals)
@@ -47,6 +47,7 @@ class VQBeTPolicy(PreTrainedPolicy):
    def __init__(
        self,
        config: VQBeTConfig | None = None,
+        **kwargs,
    ):
        """
        Args:
@@ -0,0 +1,35 @@
+# WALL-OSS
+
+This repository contains the Hugging Face port of **WALL-OSS**, a Vision-Language-Action model for cross-embodiment robotic control based on Qwen2.5-VL with flow matching/FAST action prediction.
+
+---
+
+## Model Overview
+
+| Feature            | Description                                           |
+| ------------------ | ----------------------------------------------------- | --- |
+| Base Model         | Qwen2.5-VL (Vision-Language Model)                    |
+| Action Prediction  | Flow Matching (diffusion) or FAST (discrete tokens)   |
+| Architecture       | Mixture of Experts (MoE) with action-specific routing |     |
+| Multi-Modal Inputs | Vision (images/videos), Language, Proprioception      |
+
+---
+
+## Citation
+
+If you use this work, please cite:
+
+```bibtex
+@article{zhai2025igniting,
+    title   = {Igniting VLMs Toward the Embodied Space},
+    author  = {Zhai, Andy and Liu, Brae and Fang, Bruno and Cai, Chalse and Ma, Ellie and Yin, Ethan and Wang, Hao and Zhou, Hugo and Wang, James and Shi, Lights and Liang, Lucy and Wang, Make and Wang, Qian and Gan, Roy and Yu, Ryan and Li, Shalfun and Liu, Starrick and Chen, Sylas and Chen, Vincent and Xu, Zach},
+    journal = {arXiv preprint arXiv:2509.11766},
+    year    = {2025}
+}
+```
+
+---
+
+## License
+
+This port follows the **Apache 2.0 License**.
@@ -0,0 +1,19 @@
+#!/usr/bin/env python
+
+# Copyright 2025 Physical Intelligence 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 .configuration_wall_x import WallXConfig
+
+__all__ = ["WallXConfig", "WallXPolicy", "make_wall_x_pre_post_processors"]
@@ -0,0 +1,165 @@
+# Copyright 2025 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 FeatureType, NormalizationMode, PolicyFeature
+from lerobot.optim.optimizers import AdamWConfig
+from lerobot.optim.schedulers import CosineDecayWithWarmupSchedulerConfig
+
+
+@PreTrainedConfig.register_subclass("wall_x")
+@dataclass
+class WallXConfig(PreTrainedConfig):
+    """
+    Configuration class for Wall-X policy.
+
+    Wall-X is based on Qwen2.5-VL with action prediction capabilities using flow matching.
+    It supports cross-embodiment robotic control through unified action representations.
+
+    This config supports multi-modal learning with vision, language, and action data.
+    """
+
+    # ==================== Input / Output Structure ====================
+    n_obs_steps: int = 1
+    chunk_size: int = 32  # action_horizon in wall-x
+    n_action_steps: int = 32
+
+    # Action dimension - wall-x uses 20
+    max_action_dim: int = 20
+    max_state_dim: int = 20  # For proprioception
+
+    normalization_mapping: dict[str, NormalizationMode] = field(
+        default_factory=lambda: {
+            "VISUAL": NormalizationMode.IDENTITY,
+            "STATE": NormalizationMode.MEAN_STD,
+            "ACTION": NormalizationMode.MEAN_STD,
+        }
+    )
+
+    # ==================== Action Prediction ====================
+    # Pretrained model paths
+    pretrained_name_or_path: str = "x-square-robot/wall-oss-flow"
+
+    # Tokenizer settings
+    action_tokenizer_path: str | None = "physical-intelligence/fast"
+
+    # Action prediction mode: "diffusion" or "fast"
+    prediction_mode: str = "diffusion"
+
+    # Attention Implementation, options: "eager", "flash_attention_2", "sdpa"
+    # NOTE: flash-attn==2.7.4.post1 is required for flash_attention_2 implementation
+    attn_implementation: str = "eager"
+
+    # ==================== Optimizer Presets ====================
+    optimizer_lr: float = 2e-5
+    optimizer_betas: tuple[float, float] = (0.9, 0.95)
+    optimizer_eps: float = 1e-8
+    optimizer_weight_decay: float = 0.01
+    optimizer_grad_clip_norm: float = 1.0
+
+    scheduler_warmup_steps: int = 1000
+    scheduler_decay_steps: int = 100000
+    scheduler_decay_lr: float = 1e-6
+
+    def __post_init__(self):
+        super().__post_init__()
+
+        # Input validation
+        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 "
+                f"{self.n_action_steps} for `n_action_steps` and {self.chunk_size} for `chunk_size`."
+            )
+
+        if self.prediction_mode not in ["diffusion", "fast"]:
+            raise ValueError(f"prediction_mode must be 'diffusion' or 'fast', got {self.prediction_mode}")
+
+        # Assign use_fast_tokenizer based on prediction_mode
+        if self.prediction_mode == "fast":
+            self.use_fast_tokenizer = True
+        elif self.prediction_mode == "diffusion":
+            self.use_fast_tokenizer = False
+            self.action_tokenizer_path = None  # disable action tokenizer for diffusion mode
+        else:
+            raise ValueError(f"prediction_mode must be 'diffusion' or 'fast', got {self.prediction_mode}")
+
+    def validate_features(self) -> None:
+        """Validate and set up input/output features."""
+        image_features = [key for key, feat in self.input_features.items() if feat.type == FeatureType.VISUAL]
+        if not image_features:
+            raise ValueError(
+                "Wall-X policy requires at least one visual input feature. "
+                "No features of type FeatureType.VISUAL found in input_features."
+            )
+
+        if "observation.state" not in self.input_features:
+            state_feature = PolicyFeature(
+                type=FeatureType.STATE,
+                shape=(self.max_state_dim,),  # Padded to max_state_dim
+            )
+            self.input_features["observation.state"] = state_feature
+        else:
+            state_shape = self.input_features["observation.state"].shape
+            state_dim = state_shape[0] if state_shape else 0
+            if state_dim > self.max_state_dim:
+                raise ValueError(
+                    f"State dimension {state_dim} exceeds max_state_dim {self.max_state_dim}. "
+                    f"Either reduce state dimension or increase max_state_dim in config."
+                )
+
+        if "action" not in self.output_features:
+            action_feature = PolicyFeature(
+                type=FeatureType.ACTION,
+                shape=(self.max_action_dim,),  # Padded to max_action_dim
+            )
+            self.output_features["action"] = action_feature
+        else:
+            action_shape = self.output_features["action"].shape
+            action_dim = action_shape[0] if action_shape else 0
+            if action_dim > self.max_action_dim:
+                raise ValueError(
+                    f"Action dimension {action_dim} exceeds max_action_dim {self.max_action_dim}. "
+                    f"Either reduce action dimension or increase max_action_dim in config."
+                )
+
+    def get_optimizer_preset(self) -> AdamWConfig:
+        return AdamWConfig(
+            lr=self.optimizer_lr,
+            betas=self.optimizer_betas,
+            eps=self.optimizer_eps,
+            weight_decay=self.optimizer_weight_decay,
+            grad_clip_norm=self.optimizer_grad_clip_norm,
+        )
+
+    def get_scheduler_preset(self):
+        return CosineDecayWithWarmupSchedulerConfig(
+            peak_lr=self.optimizer_lr,
+            decay_lr=self.scheduler_decay_lr,
+            num_warmup_steps=self.scheduler_warmup_steps,
+            num_decay_steps=self.scheduler_decay_steps,
+        )
+
+    @property
+    def observation_delta_indices(self) -> list:
+        return None
+
+    @property
+    def action_delta_indices(self) -> list:
+        return list(range(self.chunk_size))
+
+    @property
+    def reward_delta_indices(self) -> None:
+        return None
@@ -0,0 +1,41 @@
+#!/usr/bin/env python
+
+# Copyright 2025 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.
+
+"""
+Wall-X Constants and Configuration Data.
+"""
+
+CAMERA_NAME_MAPPING = {
+    "face_view": "front view",
+    "left_wrist_view": "left wrist view",
+    "right_wrist_view": "right wrist view",
+    "move1_view": "move view",
+    "move2_view": "move view",
+    "wall_view": "wall view",
+    "top_view": "top view",
+}
+
+RESOLUTION = 256
+
+# Parameters for preprocessing
+MAX_PIXELS = 16384 * 28 * 28
+MIN_PIXELS = 4 * 28 * 28
+IMAGE_FACTOR = 28
+PRIORITY_ORDER = None
+GENERATE_SUBTASK_RATIO = 0.0
+MODEL_TYPE = "qwen2_5"
+
+TOKENIZER_MAX_LENGTH = 768
@@ -0,0 +1,133 @@
+#!/usr/bin/env python
+
+# Copyright 2025 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 typing import Any
+
+import torch
+
+from lerobot.configs.types import PipelineFeatureType, PolicyFeature
+from lerobot.policies.wall_x.configuration_wall_x import WallXConfig
+from lerobot.processor import (
+    AddBatchDimensionProcessorStep,
+    ComplementaryDataProcessorStep,
+    DeviceProcessorStep,
+    NormalizerProcessorStep,
+    PolicyAction,
+    PolicyProcessorPipeline,
+    ProcessorStepRegistry,
+    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_wall_x_pre_post_processors(
+    config: WallXConfig,
+    dataset_stats: dict[str, dict[str, torch.Tensor]] | None = None,
+) -> tuple[
+    PolicyProcessorPipeline[dict[str, Any], dict[str, Any]],
+    PolicyProcessorPipeline[PolicyAction, PolicyAction],
+]:
+    """
+    Constructs pre-processor and post-processor pipelines for the Wall-X policy.
+
+    The pre-processing pipeline prepares input data for the model by:
+    1. Renaming features to match pretrained configurations
+    2. Adding a batch dimension
+    4. Normalizing input and output features based on dataset statistics
+    5. Moving all data to the specified device
+
+    The post-processing pipeline handles the model's output by:
+    1. Unnormalizing the output actions to their original scale
+    2. Moving data to the CPU
+
+    Args:
+        config: The configuration object for the Wall-X policy
+        dataset_stats: A dictionary of statistics for normalization
+
+    Returns:
+        A tuple containing the configured pre-processor and post-processor pipelines
+    """
+
+    input_steps = [
+        RenameObservationsProcessorStep(rename_map={}),
+        AddBatchDimensionProcessorStep(),
+        WallXTaskProcessor(),  # Process task description
+        NormalizerProcessorStep(
+            features={**config.input_features, **config.output_features},
+            norm_map=config.normalization_mapping,
+            stats=dataset_stats,
+        ),
+        DeviceProcessorStep(device=config.device),
+    ]
+
+    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,
+        ),
+    )
+
+
+@ProcessorStepRegistry.register(name="wall_x_task_processor")
+class WallXTaskProcessor(ComplementaryDataProcessorStep):
+    """
+    A processor step that ensures the task description is properly formatted for Wall-X.
+
+    This step handles task preprocessing similar to Qwen-VL requirements.
+    """
+
+    def complementary_data(self, complementary_data):
+        if "task" not in complementary_data:
+            return complementary_data
+
+        task = complementary_data["task"]
+        if task is None:
+            # Provide default task if none specified
+            complementary_data["task"] = "Execute the robot action."
+            return complementary_data
+
+        new_complementary_data = dict(complementary_data)
+
+        # Handle both string and list of strings
+        if isinstance(task, str):
+            # Single string: ensure proper formatting
+            if not task.endswith("."):
+                new_complementary_data["task"] = f"{task}."
+        elif isinstance(task, list) and all(isinstance(t, str) for t in task):
+            # List of strings: format each
+            new_complementary_data["task"] = [t if t.endswith(".") else f"{t}." for t in task]
+
+        return new_complementary_data
+
+    def transform_features(
+        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
+    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
+        return features
@@ -0,0 +1,248 @@
+from transformers.configuration_utils import PretrainedConfig
+from transformers.modeling_rope_utils import rope_config_validation
+
+
+class Qwen2_5_VLVisionConfig(PretrainedConfig):
+    model_type = "qwen2_5_vl"
+    base_config_key = "vision_config"
+
+    def __init__(
+        self,
+        depth=32,
+        hidden_size=3584,
+        hidden_act="silu",
+        intermediate_size=3420,
+        num_heads=16,
+        in_channels=3,
+        patch_size=14,
+        spatial_merge_size=2,
+        temporal_patch_size=2,
+        tokens_per_second=4,
+        window_size=112,
+        out_hidden_size=3584,
+        fullatt_block_indexes=[7, 15, 23, 31],
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.depth = depth
+        self.hidden_size = hidden_size
+        self.hidden_act = hidden_act
+        self.intermediate_size = intermediate_size
+        self.num_heads = num_heads
+        self.in_channels = in_channels
+        self.patch_size = patch_size
+        self.spatial_merge_size = spatial_merge_size
+        self.temporal_patch_size = temporal_patch_size
+        self.tokens_per_second = tokens_per_second
+        self.window_size = window_size
+        self.fullatt_block_indexes = fullatt_block_indexes
+        self.out_hidden_size = out_hidden_size
+
+
+class Qwen2_5_VLConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Qwen2_5_VLModel`]. It is used to instantiate a
+    Qwen2-VL model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of
+    Qwen2-VL-7B-Instruct [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct).
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 152064):
+            Vocabulary size of the Qwen2_5_VL model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`Qwen2_5_VLModel`]
+        hidden_size (`int`, *optional*, defaults to 8192):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 29568):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 80):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 64):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 8):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 32768):
+            The maximum sequence length that this model might ever be used with.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether the model's input and output word embeddings should be tied.
+        rope_theta (`float`, *optional*, defaults to 1000000.0):
+            The base period of the RoPE embeddings.
+        use_sliding_window (`bool`, *optional*, defaults to `False`):
+            Whether to use sliding window attention.
+        sliding_window (`int`, *optional*, defaults to 4096):
+            Sliding window attention (SWA) window size. If not specified, will default to `4096`.
+        max_window_layers (`int`, *optional*, defaults to 80):
+            The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        vision_config (`Dict`, *optional*):
+            The config for the visual encoder initialization.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
+            and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
+            accordingly.
+            Expected contents:
+                `rope_type` (`str`):
+                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
+                    'llama3'], with 'default' being the original RoPE implementation.
+                `factor` (`float`, *optional*):
+                    Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
+                    most scaling types, a `factor` of x will enable the model to handle sequences of length x *
+                    original maximum pre-trained length.
+                `original_max_position_embeddings` (`int`, *optional*):
+                    Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
+                    pretraining.
+                `attention_factor` (`float`, *optional*):
+                    Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
+                    computation. If unspecified, it defaults to value recommended by the implementation, using the
+                    `factor` field to infer the suggested value.
+                `beta_fast` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
+                    ramp function. If unspecified, it defaults to 32.
+                `beta_slow` (`float`, *optional*):
+                    Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
+                    ramp function. If unspecified, it defaults to 1.
+                `short_factor` (`List[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+                    size divided by the number of attention heads divided by 2
+                `long_factor` (`List[float]`, *optional*):
+                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
+                    size divided by the number of attention heads divided by 2
+                `low_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
+                `high_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
+
+    ```python
+    >>> from transformers import Qwen2_5_VLForConditionalGeneration, Qwen2_5_VLConfig
+
+    >>> # Initializing a Qwen2_5_VL style configuration
+    >>> configuration = Qwen2_5_VLConfig()
+
+    >>> # Initializing a model from the Qwen2-VL-7B style configuration
+    >>> model = Qwen2_5_VLForConditionalGeneration(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "qwen2_5_vl"
+    sub_configs = {"vision_config": Qwen2_5_VLVisionConfig}
+    keys_to_ignore_at_inference = ["past_key_values"]
+    # Default tensor parallel plan for base model `Qwen2_5_VL`
+    base_model_tp_plan = {
+        "layers.*.self_attn.q_proj": "colwise",
+        "layers.*.self_attn.k_proj": "colwise",
+        "layers.*.self_attn.v_proj": "colwise",
+        "layers.*.self_attn.o_proj": "rowwise",
+        "layers.*.mlp.gate_proj": "colwise",
+        "layers.*.mlp.up_proj": "colwise",
+        "layers.*.mlp.down_proj": "rowwise",
+    }
+    base_model_pp_plan = {
+        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
+        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
+        "norm": (["hidden_states"], ["hidden_states"]),
+    }
+
+    def __init__(
+        self,
+        vocab_size=152064,
+        hidden_size=8192,
+        intermediate_size=29568,
+        num_hidden_layers=80,
+        num_attention_heads=64,
+        num_key_value_heads=8,
+        hidden_act="silu",
+        max_position_embeddings=32768,
+        initializer_range=0.02,
+        rms_norm_eps=1e-05,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_theta=1000000.0,
+        use_sliding_window=False,
+        sliding_window=4096,
+        max_window_layers=80,
+        attention_dropout=0.0,
+        vision_config=None,
+        rope_scaling=None,
+        num_experts=4,
+        experts=None,
+        dof_config=None,
+        noise_scheduler=None,
+        dim_inputs=(1536, 1536),
+        attention_moe=False,
+        mlp_moe=False,
+        **kwargs,
+    ):
+        if isinstance(vision_config, dict):
+            self.vision_config = self.sub_configs["vision_config"](**vision_config)
+        elif vision_config is None:
+            self.vision_config = self.sub_configs["vision_config"]()
+
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.use_sliding_window = use_sliding_window
+        self.sliding_window = sliding_window
+        self.max_window_layers = max_window_layers
+        self.layer_types = ["dense"] * num_hidden_layers
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.attention_dropout = attention_dropout
+        self.rope_scaling = rope_scaling
+
+        self.num_experts = num_experts
+        self.experts = experts
+        self.dof_config = dof_config
+        self.noise_scheduler = noise_scheduler
+        self.dim_inputs = tuple(dim_inputs)
+        self.attention_moe = attention_moe
+        self.mlp_moe = mlp_moe
+
+        if self.rope_scaling is not None and "type" in self.rope_scaling:
+            if self.rope_scaling["type"] == "mrope":
+                self.rope_scaling["type"] = "default"
+            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
+        rope_config_validation(self, ignore_keys={"mrope_section"})
+
+        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
+
+    @property
+    def text_config(self):
+        return self
+
+
+__all__ = ["Qwen2_5_VLConfig"]
@@ -0,0 +1,631 @@
+#!/usr/bin/env python
+
+# Copyright 2025 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.
+
+"""
+Wall-X Utility Functions.
+
+Contains data processing utilities, text formatting functions, and helper classes
+for the Wall-X cross-embodiment robotic control model.
+"""
+
+import random
+import re
+from collections import OrderedDict
+from dataclasses import dataclass, field
+from typing import Any
+
+import torch
+from transformers import BatchFeature
+
+from lerobot.policies.wall_x.constant import (
+    CAMERA_NAME_MAPPING,
+)
+from lerobot.utils.constants import OBS_IMAGES
+
+
+@dataclass
+class X2RDataProcessingConfig:
+    """Configuration class for X2R data processing pipeline.
+
+    This class contains all the necessary parameters for processing robotic data
+    including camera mappings, tactile sensor configurations, action predictions,
+    and various processing options.
+    """
+
+    # Action prediction configuration
+    predict_action_keys: list[str] = field(default_factory=list)
+    obs_action_keys: list[str] = field(default_factory=list)
+
+    # Image resolution settings for different views
+    resolution: dict[str, int] = field(
+        default_factory=lambda: {
+            "face_view": -1,
+            "left_wrist_view": 128,
+            "right_wrist_view": 128,
+        }
+    )
+
+    # Dataset splitting
+    train_test_split: float = 0.9
+    split_seed: int = 42
+
+    # Instruction handling
+    priority_order: dict[str, float] | None = None
+
+    # Vision model parameters
+    model_type: str = "qwen2_5"
+    max_pixels: int = 16384 * 28 * 28
+    min_pixels: int = 4 * 28 * 28
+    image_factor: int = 28
+
+    generate_subtask_ratio: float = 0.0
+
+    def __post_init__(self):
+        """Post-initialization validation and setup."""
+        # Validate train/test split
+        if not 0 < self.train_test_split < 1:
+            raise ValueError(f"train_test_split must be between 0 and 1, got {self.train_test_split}")
+
+    def as_dict(self) -> dict:
+        """Convert configuration to dictionary format.
+
+        Returns:
+            Dict: Configuration as dictionary
+        """
+        return self.__dict__
+
+    def update(self, **kwargs) -> "X2RDataProcessingConfig":
+        """Update configuration parameters.
+
+        Args:
+            **kwargs: Key-value pairs to update
+
+        Returns:
+            X2RDataProcessingConfig: Updated configuration instance
+        """
+        for key, value in kwargs.items():
+            if hasattr(self, key):
+                setattr(self, key, value)
+            else:
+                raise ValueError(f"Unknown configuration parameter: {key}")
+        return self
+
+
+def preprocesser_call(
+    processor,
+    images: list | Any | None = None,
+    text: str | list[str] | None = None,
+    videos: list | Any | None = None,
+    padding: bool | str = False,
+    truncation: bool | None = None,
+    max_length: int | None = None,
+    return_tensors: str = "pt",
+) -> BatchFeature:
+    """Unified preprocessing function for Wall-X model handling text, image and video inputs.
+
+    Processes inputs into format suitable for multimodal transformer models, including:
+    - Text tokenization and special token handling
+    - Image/video processing through image processor
+    - Attention mask and label generation
+    - Padding and truncation handling
+
+    Args:
+        processor: Multimodal processor containing tokenizer and image processor
+        images: Input images (PIL, numpy arrays, or torch tensors)
+        text: Text or list of texts to tokenize
+        videos: Input videos (numpy arrays or torch tensors)
+        padding: Whether to pad sequences to same length
+        truncation: Whether to truncate sequences longer than max_length
+        max_length: Maximum length for truncation/padding
+        return_tensors: Format for returned tensors ('pt', 'np', etc.)
+
+    Returns:
+        BatchFeature containing processed inputs with keys:
+        - input_ids: Tokenized text
+        - attention_mask: Attention mask for text
+        - pixel_values: Processed image pixels
+        - pixel_values_videos: Processed video frames
+        - image_grid_thw: Image grid dimensions for LLM
+        - video_grid_thw: Video grid dimensions for LLM
+        - labels: Training labels with masking
+    """
+    # Process image inputs
+    if images is not None and len(images) > 0:
+        image_inputs = processor.image_processor(images=images, videos=None, return_tensors=return_tensors)
+        image_grid_thw = image_inputs["image_grid_thw"]
+    else:
+        image_inputs = {}
+        image_grid_thw = None
+
+    # Process video inputs
+    if videos is not None:
+        videos_inputs = processor.image_processor(images=None, videos=videos, return_tensors=return_tensors)
+        video_grid_thw = videos_inputs["video_grid_thw"]
+    else:
+        videos_inputs = {}
+        video_grid_thw = None
+
+    # Ensure text input is in list format
+    if not isinstance(text, list):
+        text = [text]
+
+    # Process image placeholder tokens in text
+    if image_grid_thw is not None:
+        merge_length = processor.image_processor.merge_size**2
+        index = 0
+        for i in range(len(text)):
+            while "<|image_pad|>" in text[i]:
+                # Add bounds checking to avoid index overflow
+                if index >= len(image_grid_thw):
+                    print(
+                        f"Warning: Number of image placeholders ({index + 1}) "
+                        f"exceeds actual images ({len(image_grid_thw)}), "
+                        f"skipping remaining placeholder processing"
+                    )
+                    break
+                # Replace image placeholder with actual token count
+                token_count = image_grid_thw[index].prod() // merge_length
+                text[i] = text[i].replace("<|image_pad|>", "<|placeholder|>" * token_count, 1)
+                index += 1
+            text[i] = text[i].replace("<|placeholder|>", "<|image_pad|>")
+
+    # Process video placeholder tokens in text
+    if video_grid_thw is not None:
+        merge_length = processor.image_processor.merge_size**2
+        index = 0
+        for i in range(len(text)):
+            while "<|video_pad|>" in text[i]:
+                # Replace video placeholder with actual token count
+                token_count = video_grid_thw[index].prod() // merge_length
+                text[i] = text[i].replace("<|video_pad|>", "<|placeholder|>" * token_count, 1)
+                index += 1
+            text[i] = text[i].replace("<|placeholder|>", "<|video_pad|>")
+
+    # Tokenize complete input text
+    text_inputs = processor.tokenizer(
+        text,
+        return_tensors=return_tensors,
+        padding=padding,
+        truncation=truncation,
+        max_length=max_length,
+    )
+
+    # Get pad token ID for label generation
+    pad_token_id = processor.tokenizer.pad_token_id
+    if pad_token_id is None:
+        pad_token_id = processor.tokenizer.eos_token_id
+
+    # Generate labels for multi-turn dialogue, keeping only assistant response loss
+    labels = torch.full_like(text_inputs.input_ids, -100)
+    assistant_marker = "<|im_start|>assistant\n"
+    im_end_token_id = processor.tokenizer.convert_tokens_to_ids("<|im_end|>")
+    assistant_tokens = processor.tokenizer("<|im_start|>assistant\n", add_special_tokens=False).input_ids
+
+    for i in range(len(text)):
+        assistant_regions = []
+        parts = text[i].split(assistant_marker)
+
+        # Process each part to determine which tokens belong to assistant responses
+        # Count left padding tokens
+        num_left_pads = 0
+        for token_id in text_inputs.input_ids[i]:
+            if token_id == pad_token_id:
+                num_left_pads += 1
+            else:
+                break
+        current_pos = num_left_pads
+
+        for j, part in enumerate(parts):
+            part_tokens = processor.tokenizer(part, add_special_tokens=False).input_ids
+            if j == 0:
+                # First part is system prompt or user question, all labels are -100
+                current_pos += len(part_tokens)
+                continue
+
+            # From second part onwards, each part starts with assistant response
+            for k in range(current_pos + 1, len(text_inputs.input_ids[i])):
+                if text_inputs.input_ids[i][k] == im_end_token_id:
+                    assistant_regions.append((current_pos + len(assistant_tokens), k + 2))
+                    break
+            current_pos += len(part_tokens) + 3
+
+        # Set labels for assistant response regions
+        for start, end in assistant_regions:
+            labels[i][start:end] = text_inputs.input_ids[i][start:end]
+
+    # Mask special action tokens in labels
+    action_token_id = processor.tokenizer.encode("<|action|>")[0]
+    propri_token_id = processor.tokenizer.encode("<|propri|>")[0]
+    labels[labels == action_token_id] = -100
+    labels[labels == propri_token_id] = -100
+    labels[labels == processor.tokenizer.pad_token_id] = -100
+
+    # Set labels to None if all are invalid to skip cross entropy loss
+    if (labels != -100).any().item():
+        text_inputs["labels"] = labels
+    else:
+        text_inputs["labels"] = None
+
+    return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs})
+
+
+def process_grounding_points(
+    text: str,
+    orig_height: int,
+    orig_width: int,
+    resized_height: int,
+    resized_width: int,
+    model_type: str,
+) -> str:
+    """Process grounding point coordinates in text based on image resizing.
+
+    Adjusts coordinate values in <point> tags to match resized image dimensions
+    for different model types (qwen2, qwen2_5).
+
+    Args:
+        text: Input text containing <point> tags with coordinates
+        orig_height: Original image height
+        orig_width: Original image width
+        resized_height: Resized image height
+        resized_width: Resized image width
+        model_type: Model type for coordinate processing ('qwen2' or 'qwen2_5')
+
+    Returns:
+        Text with adjusted coordinate values
+    """
+    # Regex pattern to match <point> tags and their contents
+    point_pattern = re.compile(r"<point>(.*?)</point>")
+
+    def process_match(match):
+        """Process a single point match and adjust coordinates."""
+        coords_str = match.group(1)
+        try:
+            # Extract coordinates from string
+            coords = list(map(int, re.findall(r"\d+", coords_str)))
+
+            # Calculate resize scale factors
+            scale_w = resized_width / orig_width
+            scale_h = resized_height / orig_height
+
+            if len(coords) == 2:
+                x, y = coords
+                if model_type == "qwen2_5":
+                    # Qwen2.5 uses pixel coordinates
+                    new_x = max(0, min(round(x * scale_w), resized_width - 1))
+                    new_y = max(0, min(round(y * scale_h), resized_height - 1))
+                elif model_type == "qwen2":
+                    # Qwen2 normalizes to [0, 1000) range
+                    new_x = max(0, min(999.999, (x / orig_width) * 1000))
+                    new_y = max(0, min(999.999, (y / orig_height) * 1000))
+                else:
+                    raise ValueError(f"Unsupported model type: {model_type}")
+                coords = [new_x, new_y]
+
+            elif len(coords) == 4:
+                x1, y1, x2, y2 = coords
+                if model_type == "qwen2_5":
+                    new_x1 = max(0, min(round(x1 * scale_w), resized_width - 1))
+                    new_y1 = max(0, min(round(y1 * scale_h), resized_height - 1))
+                    new_x2 = max(0, min(round(x2 * scale_w), resized_width - 1))
+                    new_y2 = max(0, min(round(y2 * scale_h), resized_height - 1))
+                elif model_type == "qwen2":
+                    new_x1 = max(0, min(999.999, (x1 / orig_width) * 1000))
+                    new_y1 = max(0, min(999.999, (y1 / orig_height) * 1000))
+                    new_x2 = max(0, min(999.999, (x2 / orig_width) * 1000))
+                    new_y2 = max(0, min(999.999, (y2 / orig_height) * 1000))
+                else:
+                    raise ValueError(f"Unsupported model type: {model_type}")
+                coords = [new_x1, new_y1, new_x2, new_y2]
+
+            # Return processed point tag
+            return f"<point>[{', '.join(map(str, coords))}]</point>"
+
+        except (ValueError, TypeError):
+            # Return original content if processing fails
+            return match.group(0)
+
+    # Replace all matching point tags
+    processed_text = point_pattern.sub(process_match, text)
+    return processed_text
+
+
+def get_frame_instruction(
+    instruction_info: dict[str, Any],
+    frame_idx: int | None = None,
+    truncate_keys: list[str] | None = None,
+) -> tuple[dict[str, Any], int | None]:
+    """Extract frame-specific instruction from instruction dictionary.
+
+    Args:
+        instruction_info: Dictionary containing instruction components
+        frame_idx: Current frame index
+        truncate_keys: Keys that trigger truncation when found
+
+    Returns:
+        Tuple of (frame_instruction_dict, split_end_frame)
+    """
+    if truncate_keys is None:
+        truncate_keys = [
+            "subtask_generation",
+            "distribute",
+            "subtask_generation_zh",
+            "distribute_zh",
+        ]
+
+    instruction_for_frame = {}
+    split_end = None
+
+    for key, value in instruction_info.items():
+        if isinstance(value, dict):
+            # Handle frame-range specific instructions
+            for frame_range, frame_instruction in value.items():
+                start_frame, end_frame = map(int, frame_range.split(" "))
+                if start_frame <= frame_idx < end_frame or (start_frame == frame_idx):
+                    instruction_for_frame[key] = frame_instruction
+                    if truncate_keys is not None and split_end is None and key in truncate_keys:
+                        split_end = end_frame + 1
+                    break
+        else:
+            instruction_for_frame[key] = value
+
+    return instruction_for_frame, split_end
+
+
+def get_task_instruction(
+    frame_instruction_info: dict[str, Any], priority_order: OrderedDict | None = None
+) -> str:
+    """Construct task instruction from available instruction fields using priority sampling.
+
+    Args:
+        frame_instruction_info: Dictionary containing instruction fields
+        priority_order: OrderedDict specifying sampling probability for each field
+
+    Returns:
+        Combined instruction string with priority components
+    """
+    # Default priority settings
+    default_priority_order = OrderedDict(
+        {
+            "subtask_generation": 0.25,
+            "subtask_generation_zh": 0.25,
+            "distribute": 0.25,
+            "distribute_zh": 0.25,
+        }
+    )
+
+    if priority_order is not None:
+        priority_order = OrderedDict(priority_order)
+    else:
+        priority_order = default_priority_order
+
+    got_instruction = False
+    task_instruction = ""
+
+    # Sample instruction components based on priority probabilities
+    for key, prob in priority_order.items():
+        if key in frame_instruction_info and frame_instruction_info[key] != "":
+            if got_instruction:
+                if random.random() >= prob:
+                    continue
+
+            task_instruction += f"\n{frame_instruction_info[key]}"
+            got_instruction = True
+            break
+
+    # Fall back to base instruction if no priority components found
+    if not got_instruction:
+        task_instruction = frame_instruction_info.get("instruction", "")
+
+    return task_instruction
+
+
+def get_wallx_normal_text(
+    instruction_info: dict[str, Any],
+    action_chunk_size: int,
+    frame_idx: int,
+    priority_order: OrderedDict | None = None,
+    img_keys: list[str] | None = None,
+    generate_subtask_ratio: float = 0.0,
+) -> tuple[str, bool]:
+    """Construct complete multimodal prompt text for Wall-X model.
+
+    Formats input using special tokens including:
+    - System message
+    - User observations (with image placeholders)
+    - Task instructions
+    - Proprioception prompts
+    - Assistant responses (with action tokens)
+
+    Args:
+        instruction_info: Dictionary containing instruction components
+        action_chunk_size: Number of action tokens to generate
+        frame_idx: Current frame index
+        priority_order: Priority order for instruction sampling
+        img_keys: List of image keys
+        generate_subtask_ratio: Probability of generating subtask instead of actions
+
+    Returns:
+        Tuple of (formatted_prompt_text, is_subtask_generation)
+    """
+    # Special tokens for formatting
+    role_start_symbol = "<|im_start|>"
+    role_end_symbol = "<|im_end|>"
+    vision_start_symbol = "<|vision_start|>"
+    vision_end_symbol = "<|vision_end|>"
+    image_pad_symbol = "<|image_pad|>"
+    propri_symbol = "<|propri|>"
+    action_symbol = "<|action|>"
+    action_fast_symbol = "<|action_fast|>"
+
+    # System prologue
+    prologue = f"{role_start_symbol}system\nYou are a helpful assistant.{role_end_symbol}\n"
+
+    # User request with observation
+    user_request = f"{role_start_symbol}user\nObservation:"
+    if img_keys:
+        img_keys = img_key_mapping(img_keys)
+        for key in img_keys:
+            user_request += f" {key}: {vision_start_symbol}{image_pad_symbol}{vision_end_symbol}"
+    user_request += "\nInstruction:"
+
+    # Get frame-specific instruction
+    frame_instruction_info, _ = get_frame_instruction(instruction_info, frame_idx=frame_idx)
+
+    generate_subtask = False
+    priority_keys = ["subtask_generation", "distribute"]
+
+    # Decide whether to generate subtask or actions
+    if (
+        bool(set(frame_instruction_info.keys()) & set(priority_keys))
+        and random.random() < generate_subtask_ratio
+    ):
+        # Generate subtask (equivalent to VQA task)
+        instruction = frame_instruction_info.get("instruction", "")
+        text_prompt = "\nPredict the next action in language.\n"
+        user_message = f"{user_request} {instruction}{text_prompt}{role_end_symbol}\n"
+
+        # Find output instruction from priority keys
+        for key in priority_keys:
+            if key in frame_instruction_info:
+                output_instruction = frame_instruction_info[key]
+                break
+
+        assistant_output = f"{role_start_symbol}assistant\n{output_instruction}\n{role_end_symbol}"
+        generate_subtask = True
+    else:
+        # Generate actions
+        instruction = get_task_instruction(frame_instruction_info, priority_order=priority_order)
+        text_prompt = f"\nPredict the next action in robot action.\nProprioception: {propri_symbol}\n"
+        user_message = f"{user_request} {instruction}{text_prompt}{role_end_symbol}\n"
+        assistant_output = f"{role_start_symbol}assistant\n{action_fast_symbol}{role_end_symbol}\n{action_symbol * action_chunk_size}"
+
+    complete_text = prologue + user_message + assistant_output
+    return complete_text, generate_subtask
+
+
+def img_key_mapping(img_keys: list[str]) -> list[str]:
+    """Map image keys to camera names.
+
+    Args:
+        img_keys: List of image keys
+
+    Returns:
+        List of camera names
+    """
+    processed_img_keys = []
+    for key in img_keys:
+        key = key.replace(OBS_IMAGES + ".", "")
+        if key in CAMERA_NAME_MAPPING:
+            key = CAMERA_NAME_MAPPING[key]
+        else:
+            if "view" in key:
+                key = key.replace("_", " ")
+            else:
+                key = key + " view"
+        processed_img_keys.append(key)
+    return processed_img_keys
+
+
+def get_action_tokens(normalized_actions: torch.Tensor | list, action_tokenizer) -> list[list[str]]:
+    """Convert normalized actions to action token strings.
+
+    Args:
+        normalized_actions: Normalized action arrays/tensors
+        action_tokenizer: Tokenizer for converting actions to tokens
+
+    Returns:
+        List of action token string lists for each sample
+    """
+    if isinstance(normalized_actions, torch.Tensor):
+        normalized_actions = normalized_actions.cpu().numpy()
+
+    all_action_tokens = []
+    for i in range(len(normalized_actions)):
+        if isinstance(normalized_actions[i], torch.Tensor):
+            normalized_actions[i] = normalized_actions[i].cpu().numpy()
+
+        token_id = action_tokenizer(normalized_actions[i])
+        action_tokens = [f"<|action_token_{j}|>" for j in token_id[0]]
+        all_action_tokens.append(action_tokens)
+
+    return all_action_tokens
+
+
+def pad_action_token_strs(
+    actions_token_lists: list[list[str]],
+    pad_token: str = "<|endoftext|>",  # nosec B107
+) -> list[str]:
+    """Pad action token lists to same length and join as strings.
+
+    Args:
+        actions_token_lists: List of action token lists for each sample
+        pad_token: Token used for padding
+
+    Returns:
+        List of padded action token strings
+    """
+    max_len = max(len(tokens) for tokens in actions_token_lists)
+    padded_action_strs = []
+
+    for tokens in actions_token_lists:
+        padded_tokens = tokens + ["<|im_end|>\n"] + [pad_token] * (max_len - len(tokens))
+        padded_action_strs.append("".join(padded_tokens))
+
+    return padded_action_strs
+
+
+def replace_action_token(
+    text: list[str],
+    norm_action: torch.Tensor | None,
+    action_tokenizer,
+    dof_masks: torch.Tensor | None = None,
+) -> list[str]:
+    """Replace action placeholders in text with actual action tokens.
+
+    Args:
+        text: List of text strings with action placeholders
+        norm_action: Normalized action tensors
+        action_tokenizer: Tokenizer for converting actions to tokens
+        dof_masks: Masks for degrees of freedom
+
+    Returns:
+        List of text strings with action tokens replaced
+    """
+    if action_tokenizer is not None and norm_action is not None:
+        # Extract actions based on chunk sizes and DOF masks
+        norm_action = [action[:32, dof_masks[i, 0].bool()] for i, action in enumerate(norm_action)]
+
+        # Convert to action tokens and pad
+        actions_fast_tokens = get_action_tokens(norm_action, action_tokenizer)
+        actions_fast_token_strs = pad_action_token_strs(actions_fast_tokens)
+
+        # Replace action placeholders with actual tokens
+        actions_fast_token_idx = 0
+        for i in range(len(text)):
+            if "<|action_fast|>" in text[i]:
+                text[i] = text[i].replace(
+                    "<|action_fast|><|im_end|>\n",
+                    actions_fast_token_strs[actions_fast_token_idx],
+                )
+                actions_fast_token_idx += 1
+
+        # Remove remaining action placeholders
+        text = [t.replace("<|action|>", "") for t in text]
+    else:
+        # Remove action placeholders when no tokenizer available
+        text = [t.replace("<|action_fast|><|im_end|>\n", "") for t in text]
+
+    return text
@@ -273,7 +273,7 @@ class XVLAPolicy(PreTrainedPolicy):
    config_class = XVLAConfig
    name = "xvla"

-    def __init__(self, config: XVLAConfig):
+    def __init__(self, config: XVLAConfig, **kwargs):
        super().__init__(config)
        config.validate_features()
        florence_config = config.get_florence_config()
@@ -170,8 +170,9 @@ def _extract_complementary_data(batch: dict[str, Any]) -> dict[str, Any]:
    task_key = {"task": batch["task"]} if "task" in batch else {}
    index_key = {"index": batch["index"]} if "index" in batch else {}
    task_index_key = {"task_index": batch["task_index"]} if "task_index" in batch else {}
+    episode_index_key = {"episode_index": batch["episode_index"]} if "episode_index" in batch else {}

-    return {**pad_keys, **task_key, **index_key, **task_index_key}
+    return {**pad_keys, **task_key, **index_key, **task_index_key, **episode_index_key}


 def create_transition(
@@ -112,7 +112,32 @@ class WandBLogger:
        artifact_name = f"{self._group}-{step_id}"
        artifact_name = get_safe_wandb_artifact_name(artifact_name)
        artifact = self._wandb.Artifact(artifact_name, type="model")
-        artifact.add_file(checkpoint_dir / PRETRAINED_MODEL_DIR / SAFETENSORS_SINGLE_FILE)
+        pretrained_model_dir = checkpoint_dir / PRETRAINED_MODEL_DIR
+
+        # Check if this is a PEFT model (has adapter files instead of model.safetensors)
+        adapter_model_file = pretrained_model_dir / "adapter_model.safetensors"
+        standard_model_file = pretrained_model_dir / SAFETENSORS_SINGLE_FILE
+
+        if adapter_model_file.exists():
+            # PEFT model: add adapter files and configs
+            artifact.add_file(adapter_model_file)
+            adapter_config_file = pretrained_model_dir / "adapter_config.json"
+            if adapter_config_file.exists():
+                artifact.add_file(adapter_config_file)
+            # Also add the policy config which is needed for loading
+            config_file = pretrained_model_dir / "config.json"
+            if config_file.exists():
+                artifact.add_file(config_file)
+        elif standard_model_file.exists():
+            # Standard model: add the single safetensors file
+            artifact.add_file(standard_model_file)
+        else:
+            logging.warning(
+                f"No {SAFETENSORS_SINGLE_FILE} or adapter_model.safetensors found in {pretrained_model_dir}. "
+                "Skipping model artifact upload to WandB."
+            )
+            return
+
        self._wandb.log_artifact(artifact)

    def log_dict(
@@ -0,0 +1,21 @@
+#!/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.
+
+# OMX is a fully open-source robot from ROBOTIS.
+# More information at: https://ai.robotis.com/omx/introduction_omx.html
+
+from .config_omx_follower import OmxFollowerConfig
+from .omx_follower import OmxFollower
@@ -0,0 +1,39 @@
+# 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 dataclasses import dataclass, field
+
+from lerobot.cameras import CameraConfig
+
+from ..config import RobotConfig
+
+
+@RobotConfig.register_subclass("omx_follower")
+@dataclass
+class OmxFollowerConfig(RobotConfig):
+    # Port to connect to the arm
+    port: str
+
+    disable_torque_on_disconnect: bool = True
+
+    # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.
+    # Set this to a positive scalar to have the same value for all motors, or a dictionary that maps motor
+    # names to the max_relative_target value for that motor.
+    max_relative_target: float | dict[str, float] | None = None
+
+    # cameras
+    cameras: dict[str, CameraConfig] = field(default_factory=dict)
+
+    # Set to `True` for backward compatibility with previous policies/dataset
+    use_degrees: bool = False
@@ -0,0 +1,225 @@
+#!/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 functools import cached_property
+from typing import Any
+
+from lerobot.cameras.utils import make_cameras_from_configs
+from lerobot.motors import Motor, MotorCalibration, MotorNormMode
+from lerobot.motors.dynamixel import (
+    DriveMode,
+    DynamixelMotorsBus,
+    OperatingMode,
+)
+from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
+
+from ..robot import Robot
+from ..utils import ensure_safe_goal_position
+from .config_omx_follower import OmxFollowerConfig
+
+logger = logging.getLogger(__name__)
+
+
+class OmxFollower(Robot):
+    """
+    - [OMX](https://github.com/ROBOTIS-GIT/open_manipulator),
+        expansion, developed by Woojin Wie and Junha Cha from [ROBOTIS](https://ai.robotis.com/)
+    """
+
+    config_class = OmxFollowerConfig
+    name = "omx_follower"
+
+    def __init__(self, config: OmxFollowerConfig):
+        super().__init__(config)
+        self.config = config
+        norm_mode_body = MotorNormMode.DEGREES if config.use_degrees else MotorNormMode.RANGE_M100_100
+        self.bus = DynamixelMotorsBus(
+            port=self.config.port,
+            motors={
+                "shoulder_pan": Motor(11, "xl430-w250", norm_mode_body),
+                "shoulder_lift": Motor(12, "xl430-w250", norm_mode_body),
+                "elbow_flex": Motor(13, "xl430-w250", norm_mode_body),
+                "wrist_flex": Motor(14, "xl330-m288", norm_mode_body),
+                "wrist_roll": Motor(15, "xl330-m288", norm_mode_body),
+                "gripper": Motor(16, "xl330-m288", MotorNormMode.RANGE_0_100),
+            },
+            calibration=self.calibration,
+        )
+        self.cameras = make_cameras_from_configs(config.cameras)
+
+    @property
+    def _motors_ft(self) -> dict[str, type]:
+        return {f"{motor}.pos": float for motor in self.bus.motors}
+
+    @property
+    def _cameras_ft(self) -> dict[str, tuple]:
+        return {
+            cam: (self.config.cameras[cam].height, self.config.cameras[cam].width, 3) for cam in self.cameras
+        }
+
+    @cached_property
+    def observation_features(self) -> dict[str, type | tuple]:
+        return {**self._motors_ft, **self._cameras_ft}
+
+    @cached_property
+    def action_features(self) -> dict[str, type]:
+        return self._motors_ft
+
+    @property
+    def is_connected(self) -> bool:
+        return self.bus.is_connected and all(cam.is_connected for cam in self.cameras.values())
+
+    def connect(self, calibrate: bool = True) -> None:
+        """
+        For OMX robots that come pre-calibrated:
+        - If default calibration from package doesn't match motors, read from motors and save
+        - This allows using pre-calibrated robots without manual calibration
+        - If no calibration file exists, use factory default values (homing_offset=0, range_min=0, range_max=4095)
+        """
+        if self.is_connected:
+            raise DeviceAlreadyConnectedError(f"{self} already connected")
+
+        self.bus.connect()
+        if not self.is_calibrated and calibrate:
+            logger.info(
+                "Mismatch between calibration values in the motor and the calibration file or no calibration file found"
+            )
+            self.calibrate()
+
+        for cam in self.cameras.values():
+            cam.connect()
+
+        self.configure()
+        logger.info(f"{self} connected.")
+
+    @property
+    def is_calibrated(self) -> bool:
+        return self.bus.is_calibrated
+
+    def calibrate(self) -> None:
+        self.bus.disable_torque()
+        logger.info(f"\nUsing factory default calibration values for {self}")
+        logger.info(f"\nWriting default configuration of {self} to the motors")
+        for motor in self.bus.motors:
+            self.bus.write("Operating_Mode", motor, OperatingMode.EXTENDED_POSITION.value)
+
+        for motor in self.bus.motors:
+            self.bus.write("Drive_Mode", motor, DriveMode.NON_INVERTED.value)
+
+        self.calibration = {}
+        for motor, m in self.bus.motors.items():
+            self.calibration[motor] = MotorCalibration(
+                id=m.id,
+                drive_mode=0,
+                homing_offset=0,
+                range_min=0,
+                range_max=4095,
+            )
+
+        self.bus.write_calibration(self.calibration)
+        self._save_calibration()
+        logger.info(f"Calibration saved to {self.calibration_fpath}")
+
+    def configure(self) -> None:
+        with self.bus.torque_disabled():
+            self.bus.configure_motors()
+            # Use 'extended position mode' for all motors except gripper, because in joint mode the servos
+            # can't rotate more than 360 degrees (from 0 to 4095) And some mistake can happen while assembling
+            # the arm, you could end up with a servo with a position 0 or 4095 at a crucial point
+            for motor in self.bus.motors:
+                if motor != "gripper":
+                    self.bus.write("Operating_Mode", motor, OperatingMode.EXTENDED_POSITION.value)
+
+            # Use 'position control current based' for gripper to be limited by the limit of the current. For
+            # the follower gripper, it means it can grasp an object without forcing too much even tho, its
+            # goal position is a complete grasp (both gripper fingers are ordered to join and reach a touch).
+            # For the leader gripper, it means we can use it as a physical trigger, since we can force with
+            # our finger to make it move, and it will move back to its original target position when we
+            # release the force.
+            self.bus.write("Operating_Mode", "gripper", OperatingMode.CURRENT_POSITION.value)
+
+            # Set better PID values to close the gap between recorded states and actions
+            # TODO(rcadene): Implement an automatic procedure to set optimal PID values for each motor
+            self.bus.write("Position_P_Gain", "elbow_flex", 1500)
+            self.bus.write("Position_I_Gain", "elbow_flex", 0)
+            self.bus.write("Position_D_Gain", "elbow_flex", 600)
+
+    def setup_motors(self) -> None:
+        for motor in reversed(self.bus.motors):
+            input(f"Connect the controller board to the '{motor}' motor only and press enter.")
+            self.bus.setup_motor(motor)
+            print(f"'{motor}' motor id set to {self.bus.motors[motor].id}")
+
+    def get_observation(self) -> dict[str, Any]:
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
+
+        # Read arm position
+        start = time.perf_counter()
+        obs_dict = self.bus.sync_read("Present_Position")
+        obs_dict = {f"{motor}.pos": val for motor, val in obs_dict.items()}
+        dt_ms = (time.perf_counter() - start) * 1e3
+        logger.debug(f"{self} read state: {dt_ms:.1f}ms")
+
+        # Capture images from cameras
+        for cam_key, cam in self.cameras.items():
+            start = time.perf_counter()
+            obs_dict[cam_key] = cam.async_read()
+            dt_ms = (time.perf_counter() - start) * 1e3
+            logger.debug(f"{self} read {cam_key}: {dt_ms:.1f}ms")
+
+        return obs_dict
+
+    def send_action(self, action: dict[str, float]) -> dict[str, float]:
+        """Command arm to move to a target joint configuration.
+
+        The relative action magnitude may be clipped depending on the configuration parameter
+        `max_relative_target`. In this case, the action sent differs from original action.
+        Thus, this function always returns the action actually sent.
+
+        Args:
+            action (dict[str, float]): The goal positions for the motors.
+
+        Returns:
+            dict[str, float]: The action sent to the motors, potentially clipped.
+        """
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
+
+        goal_pos = {key.removesuffix(".pos"): val for key, val in action.items() if key.endswith(".pos")}
+
+        # Cap goal position when too far away from present position.
+        # /!\ Slower fps expected due to reading from the follower.
+        if self.config.max_relative_target is not None:
+            present_pos = self.bus.sync_read("Present_Position")
+            goal_present_pos = {key: (g_pos, present_pos[key]) for key, g_pos in goal_pos.items()}
+            goal_pos = ensure_safe_goal_position(goal_present_pos, self.config.max_relative_target)
+
+        # Send goal position to the arm
+        self.bus.sync_write("Goal_Position", goal_pos)
+        return {f"{motor}.pos": val for motor, val in goal_pos.items()}
+
+    def disconnect(self):
+        if not self.is_connected:
+            raise DeviceNotConnectedError(f"{self} is not connected.")
+
+        self.bus.disconnect(self.config.disable_torque_on_disconnect)
+        for cam in self.cameras.values():
+            cam.disconnect()
+
+        logger.info(f"{self} disconnected.")
@@ -51,5 +51,8 @@ class UnitreeG1Config(RobotConfig):

    control_dt: float = 1.0 / 250.0  # 250Hz

+    # launch mujoco simulation
+    is_simulation: bool = True
+
    # socket config for ZMQ bridge
    robot_ip: str = "192.168.123.164"
@@ -99,11 +99,12 @@ def state_forward_loop(
    lowstate_sub: ChannelSubscriber,
    lowstate_sock: zmq.Socket,
    state_period: float,
+    shutdown_event: threading.Event,
 ) -> None:
    """Read observation from DDS and forward to ZMQ clients."""
    last_state_time = 0.0

-    while True:
+    while not shutdown_event.is_set():
        # read from DDS
        msg = lowstate_sub.Read()
        if msg is None:
@@ -128,7 +129,10 @@ def cmd_forward_loop(
 ) -> None:
    """Receive commands from ZMQ and forward to DDS."""
    while True:
-        payload = lowcmd_sock.recv()
+        try:
+            payload = lowcmd_sock.recv()
+        except zmq.ContextTerminated:
+            break
        msg_dict = json.loads(payload.decode("utf-8"))

        topic = msg_dict.get("topic", "")
@@ -182,30 +186,26 @@ def main() -> None:
    lowstate_sock.bind(f"tcp://0.0.0.0:{LOWSTATE_PORT}")

    state_period = 0.002  # ~500 hz
+    shutdown_event = threading.Event()

-    # start observation forwarding thread
+    # start observation forwarding in background thread
    t_state = threading.Thread(
        target=state_forward_loop,
-        args=(lowstate_sub, lowstate_sock, state_period),
-        daemon=True,
+        args=(lowstate_sub, lowstate_sock, state_period, shutdown_event),
    )
    t_state.start()

-    # start action forwarding thread
-    t_cmd = threading.Thread(
-        target=cmd_forward_loop,
-        args=(lowcmd_sock, lowcmd_pub_debug, crc),
-        daemon=True,
-    )
-    t_cmd.start()
-
    print("bridge running (lowstate -> zmq, lowcmd -> dds)")
-    # keep main thread alive so daemon threads don't exit
+
+    # run command forwarding in main thread
    try:
-        while True:
-            time.sleep(1.0)
+        cmd_forward_loop(lowcmd_sock, lowcmd_pub_debug, crc)
    except KeyboardInterrupt:
        print("shutting down bridge...")
+    finally:
+        shutdown_event.set()
+        ctx.term()  # terminates blocking zmq.recv() calls
+        t_state.join(timeout=2.0)


 if __name__ == "__main__":
@@ -30,12 +30,8 @@ from unitree_sdk2py.idl.unitree_hg.msg.dds_ import (
 )
 from unitree_sdk2py.utils.crc import CRC

+from lerobot.envs.factory import make_env
 from lerobot.robots.unitree_g1.g1_utils import G1_29_JointIndex
-from lerobot.robots.unitree_g1.unitree_sdk2_socket import (
-    ChannelFactoryInitialize,
-    ChannelPublisher,
-    ChannelSubscriber,
-)

 from ..robot import Robot
 from .config_unitree_g1 import UnitreeG1Config
@@ -127,7 +123,21 @@ class UnitreeG1(Robot):

        self.control_dt = config.control_dt

+        if config.is_simulation:
+            from unitree_sdk2py.core.channel import (
+                ChannelFactoryInitialize,
+                ChannelPublisher,
+                ChannelSubscriber,
+            )
+        else:
+            from lerobot.robots.unitree_g1.unitree_sdk2_socket import (
+                ChannelFactoryInitialize,
+                ChannelPublisher,
+                ChannelSubscriber,
+            )
+
        # connect robot
+        self.ChannelFactoryInitialize = ChannelFactoryInitialize
        self.connect()

        # initialize direct motor control interface
@@ -138,8 +148,8 @@ class UnitreeG1(Robot):
        self.lowstate_buffer = DataBuffer()

        # initialize subscribe thread to read robot state
+        self._shutdown_event = threading.Event()
        self.subscribe_thread = threading.Thread(target=self._subscribe_motor_state)
-        self.subscribe_thread.daemon = True
        self.subscribe_thread.start()

        while not self.is_connected:
@@ -174,7 +184,7 @@ class UnitreeG1(Robot):
        self.remote_controller = self.RemoteController()

    def _subscribe_motor_state(self):  # polls robot state @ 250Hz
-        while True:
+        while not self._shutdown_event.is_set():
            start_time = time.time()
            msg = self.lowstate_subscriber.Read()
            if msg is not None:
@@ -218,10 +228,17 @@ class UnitreeG1(Robot):
        pass

    def connect(self, calibrate: bool = True) -> None:  # connect to DDS
-        ChannelFactoryInitialize(0)
+        if self.config.is_simulation:
+            self.ChannelFactoryInitialize(0, "lo")
+            self.mujoco_env = make_env("lerobot/unitree-g1-mujoco", trust_remote_code=True)
+        else:
+            self.ChannelFactoryInitialize(0)

    def disconnect(self):
-        pass
+        self._shutdown_event.set()
+        self.subscribe_thread.join(timeout=2.0)
+        if self.config.is_simulation:
+            self.mujoco_env["hub_env"][0].envs[0].kill_sim()

    def get_observation(self) -> dict[str, Any]:
        return self.lowstate_buffer.get_data()
@@ -28,6 +28,10 @@ def make_robot_from_config(config: RobotConfig) -> Robot:
        from .koch_follower import KochFollower

        return KochFollower(config)
+    elif config.type == "omx_follower":
+        from .omx_follower import OmxFollower
+
+        return OmxFollower(config)
    elif config.type == "so100_follower":
        from .so100_follower import SO100Follower

@@ -40,6 +40,7 @@ from lerobot.robots import (  # noqa: F401
    koch_follower,
    lekiwi,
    make_robot_from_config,
+    omx_follower,
    so100_follower,
    so101_follower,
 )
@@ -49,6 +50,7 @@ from lerobot.teleoperators import (  # noqa: F401
    homunculus,
    koch_leader,
    make_teleoperator_from_config,
+    omx_leader,
    so100_leader,
    so101_leader,
 )
@@ -18,7 +18,8 @@
 Edit LeRobot datasets using various transformation tools.

 This script allows you to delete episodes, split datasets, merge datasets,
-and remove features. When new_repo_id is specified, creates a new dataset.
+remove features, and convert image datasets to video format.
+When new_repo_id is specified, creates a new dataset.

 Usage Examples:

@@ -65,6 +66,25 @@ Remove camera feature:
        --operation.type remove_feature \
        --operation.feature_names "['observation.images.top']"

+Convert image dataset to video format (saves locally):
+    python -m lerobot.scripts.lerobot_edit_dataset \
+        --repo_id lerobot/pusht_image \
+        --operation.type convert_to_video \
+        --operation.output_dir /path/to/output/pusht_video
+
+Convert image dataset and save with new repo_id:
+    python -m lerobot.scripts.lerobot_edit_dataset \
+        --repo_id lerobot/pusht_image \
+        --new_repo_id lerobot/pusht_video \
+        --operation.type convert_to_video
+
+Convert and push to hub:
+    python -m lerobot.scripts.lerobot_edit_dataset \
+        --repo_id lerobot/pusht_image \
+        --new_repo_id lerobot/pusht_video \
+        --operation.type convert_to_video \
+        --push_to_hub true
+
 Using JSON config file:
    python -m lerobot.scripts.lerobot_edit_dataset \
        --config_path path/to/edit_config.json
@@ -72,9 +92,13 @@ Using JSON config file:

 import logging
 import shutil
+from concurrent.futures import ThreadPoolExecutor, as_completed
 from dataclasses import dataclass
 from pathlib import Path

+import pandas as pd
+from tqdm import tqdm
+
 from lerobot.configs import parser
 from lerobot.datasets.dataset_tools import (
    delete_episodes,
@@ -82,8 +106,10 @@ from lerobot.datasets.dataset_tools import (
    remove_feature,
    split_dataset,
 )
-from lerobot.datasets.lerobot_dataset import LeRobotDataset
-from lerobot.utils.constants import HF_LEROBOT_HOME
+from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata
+from lerobot.datasets.utils import write_stats, write_tasks
+from lerobot.datasets.video_utils import encode_video_frames, get_video_info
+from lerobot.utils.constants import HF_LEROBOT_HOME, OBS_IMAGE
 from lerobot.utils.utils import init_logging


@@ -111,10 +137,23 @@ class RemoveFeatureConfig:
    feature_names: list[str] | None = None


+@dataclass
+class ConvertToVideoConfig:
+    type: str = "convert_to_video"
+    output_dir: str | None = None
+    vcodec: str = "libsvtav1"
+    pix_fmt: str = "yuv420p"
+    g: int = 2
+    crf: int = 30
+    fast_decode: int = 0
+    episode_indices: list[int] | None = None
+    num_workers: int = 4
+
+
@dataclass
 class EditDatasetConfig:
    repo_id: str
-    operation: DeleteEpisodesConfig | SplitConfig | MergeConfig | RemoveFeatureConfig
+    operation: DeleteEpisodesConfig | SplitConfig | MergeConfig | RemoveFeatureConfig | ConvertToVideoConfig
    root: str | None = None
    new_repo_id: str | None = None
    push_to_hub: bool = False
@@ -258,6 +297,415 @@ def handle_remove_feature(cfg: EditDatasetConfig) -> None:
        LeRobotDataset(output_repo_id, root=output_dir).push_to_hub()


+def save_episode_images_for_video(
+    dataset: LeRobotDataset,
+    imgs_dir: Path,
+    img_key: str,
+    episode_index: int,
+    num_workers: int = 4,
+) -> None:
+    """Save images from a specific episode and camera to disk for video encoding.
+
+    Args:
+        dataset: The LeRobot dataset to extract images from
+        imgs_dir: Directory to save images to
+        img_key: The image key (camera) to extract
+        episode_index: Index of the episode to save
+        num_workers: Number of threads for parallel image saving
+    """
+    # Create directory
+    imgs_dir.mkdir(parents=True, exist_ok=True)
+
+    # Get dataset without torch format for PIL image access
+    hf_dataset = dataset.hf_dataset.with_format(None)
+
+    # Select only this camera's images
+    imgs_dataset = hf_dataset.select_columns(img_key)
+
+    # Get episode start and end indices
+    from_idx = dataset.meta.episodes["dataset_from_index"][episode_index]
+    to_idx = dataset.meta.episodes["dataset_to_index"][episode_index]
+
+    # Get all items for this episode
+    episode_dataset = imgs_dataset.select(range(from_idx, to_idx))
+
+    # Define function to save a single image
+    def save_single_image(i_item_tuple):
+        i, item = i_item_tuple
+        img = item[img_key]
+        # Use frame-XXXXXX.png format to match encode_video_frames expectations
+        img.save(str(imgs_dir / f"frame-{i:06d}.png"), quality=100)
+        return i
+
+    # Save images with proper naming convention for encode_video_frames (frame-XXXXXX.png)
+    items = list(enumerate(episode_dataset))
+
+    with ThreadPoolExecutor(max_workers=num_workers) as executor:
+        futures = [executor.submit(save_single_image, item) for item in items]
+        for future in as_completed(futures):
+            future.result()  # This will raise any exceptions that occurred
+
+
+def encode_episode_videos(
+    dataset: LeRobotDataset,
+    new_meta: LeRobotDatasetMetadata,
+    episode_index: int,
+    vcodec: str,
+    pix_fmt: str,
+    g: int,
+    crf: int,
+    fast_decode: int,
+    temp_dir: Path,
+    num_image_workers: int = 4,
+) -> dict[str, dict]:
+    """Encode videos for a single episode and return video metadata.
+
+    Args:
+        dataset: Source dataset with images
+        new_meta: Metadata object for the new video dataset
+        episode_index: Episode index to process
+        vcodec: Video codec
+        pix_fmt: Pixel format
+        g: Group of pictures size
+        crf: Constant rate factor
+        fast_decode: Fast decode tuning
+        temp_dir: Temporary directory for images
+        num_image_workers: Number of workers for saving images
+
+    Returns:
+        Dictionary mapping video keys to their metadata (chunk_index, file_index, timestamps)
+    """
+    hf_dataset = dataset.hf_dataset.with_format(None)
+    img_keys = [key for key in hf_dataset.features if key.startswith(OBS_IMAGE)]
+
+    video_metadata = {}
+    fps = int(dataset.fps)  # Convert to int for PyAV compatibility
+    episode_length = dataset.meta.episodes["length"][episode_index]
+    episode_duration = episode_length / dataset.fps  # Use original fps for duration calculation
+
+    for img_key in img_keys:
+        # Save images temporarily
+        imgs_dir = temp_dir / f"episode_{episode_index:06d}" / img_key
+        save_episode_images_for_video(dataset, imgs_dir, img_key, episode_index, num_image_workers)
+
+        # Determine chunk and file indices
+        # For simplicity, we'll put each episode in its own file
+        chunk_idx = episode_index // new_meta.chunks_size
+        file_idx = episode_index % new_meta.chunks_size
+
+        # Create video path in the new dataset structure
+        video_path = new_meta.root / new_meta.video_path.format(
+            video_key=img_key, chunk_index=chunk_idx, file_index=file_idx
+        )
+        video_path.parent.mkdir(parents=True, exist_ok=True)
+
+        # Encode video
+        encode_video_frames(
+            imgs_dir=imgs_dir,
+            video_path=video_path,
+            fps=fps,
+            vcodec=vcodec,
+            pix_fmt=pix_fmt,
+            g=g,
+            crf=crf,
+            fast_decode=fast_decode,
+            overwrite=True,
+        )
+
+        # Clean up temporary images
+        shutil.rmtree(imgs_dir)
+
+        # Store video metadata
+        video_metadata[img_key] = {
+            f"videos/{img_key}/chunk_index": chunk_idx,
+            f"videos/{img_key}/file_index": file_idx,
+            f"videos/{img_key}/from_timestamp": 0.0,
+            f"videos/{img_key}/to_timestamp": episode_duration,
+        }
+
+    return video_metadata
+
+
+def convert_dataset_to_videos(
+    dataset: LeRobotDataset,
+    output_dir: Path,
+    repo_id: str | None = None,
+    vcodec: str = "libsvtav1",
+    pix_fmt: str = "yuv420p",
+    g: int = 2,
+    crf: int = 30,
+    fast_decode: int = 0,
+    episode_indices: list[int] | None = None,
+    num_workers: int = 4,
+) -> LeRobotDataset:
+    """Convert image-based dataset to video-based dataset.
+
+    Creates a new LeRobotDataset with videos instead of images, following the proper
+    LeRobot dataset structure with videos stored in chunked MP4 files.
+
+    Args:
+        dataset: The source LeRobot dataset with images
+        output_dir: Directory to save the new video dataset
+        repo_id: Repository ID for the new dataset (default: original_id + "_video")
+        vcodec: Video codec (default: libsvtav1)
+        pix_fmt: Pixel format (default: yuv420p)
+        g: Group of pictures size (default: 2)
+        crf: Constant rate factor (default: 30)
+        fast_decode: Fast decode tuning (default: 0)
+        episode_indices: List of episode indices to convert (None = all episodes)
+        num_workers: Number of threads for parallel processing (default: 4)
+
+    Returns:
+        New LeRobotDataset with videos
+    """
+    # Check that it's an image dataset
+    if len(dataset.meta.video_keys) > 0:
+        raise ValueError(
+            f"This operation is for image datasets only. Video dataset provided: {dataset.repo_id}"
+        )
+
+    # Get all image keys
+    hf_dataset = dataset.hf_dataset.with_format(None)
+    img_keys = [key for key in hf_dataset.features if key.startswith(OBS_IMAGE)]
+
+    if len(img_keys) == 0:
+        raise ValueError(f"No image keys found in dataset {dataset.repo_id}")
+
+    # Determine which episodes to process
+    if episode_indices is None:
+        episode_indices = list(range(dataset.meta.total_episodes))
+
+    if repo_id is None:
+        repo_id = f"{dataset.repo_id}_video"
+
+    logging.info(
+        f"Converting {len(episode_indices)} episodes with {len(img_keys)} cameras from {dataset.repo_id}"
+    )
+    logging.info(f"Video codec: {vcodec}, pixel format: {pix_fmt}, GOP: {g}, CRF: {crf}")
+
+    # Create new features dict, converting image features to video features
+    new_features = {}
+    for key, value in dataset.meta.features.items():
+        if key not in img_keys:
+            new_features[key] = value
+        else:
+            # Convert image key to video format
+            new_features[key] = value.copy()
+            new_features[key]["dtype"] = "video"  # Change dtype from "image" to "video"
+            # Video info will be updated after episodes are encoded
+
+    # Create new metadata for video dataset
+    new_meta = LeRobotDatasetMetadata.create(
+        repo_id=repo_id,
+        fps=dataset.meta.fps,
+        features=new_features,
+        robot_type=dataset.meta.robot_type,
+        root=output_dir,
+        use_videos=True,
+        chunks_size=dataset.meta.chunks_size,
+        data_files_size_in_mb=dataset.meta.data_files_size_in_mb,
+        video_files_size_in_mb=dataset.meta.video_files_size_in_mb,
+    )
+
+    # Create temporary directory for image extraction
+    temp_dir = output_dir / "temp_images"
+    temp_dir.mkdir(parents=True, exist_ok=True)
+
+    # Process each episode
+    all_episode_metadata = []
+
+    try:
+        for ep_idx in tqdm(episode_indices, desc="Converting episodes to videos"):
+            # Get episode metadata from source
+            src_episode = dataset.meta.episodes[ep_idx]
+
+            # Encode videos for this episode
+            video_metadata = encode_episode_videos(
+                dataset=dataset,
+                new_meta=new_meta,
+                episode_index=ep_idx,
+                vcodec=vcodec,
+                pix_fmt=pix_fmt,
+                g=g,
+                crf=crf,
+                fast_decode=fast_decode,
+                temp_dir=temp_dir,
+                num_image_workers=num_workers,
+            )
+
+            # Build episode metadata
+            episode_meta = {
+                "episode_index": ep_idx,
+                "length": src_episode["length"],
+                "dataset_from_index": ep_idx * src_episode["length"],
+                "dataset_to_index": (ep_idx + 1) * src_episode["length"],
+            }
+
+            # Add video metadata
+            for img_key in img_keys:
+                episode_meta.update(video_metadata[img_key])
+
+            # Add data chunk/file info (using same structure as source)
+            if "data/chunk_index" in src_episode:
+                episode_meta["data/chunk_index"] = src_episode["data/chunk_index"]
+                episode_meta["data/file_index"] = src_episode["data/file_index"]
+
+            all_episode_metadata.append(episode_meta)
+
+        # Copy and transform data files (removing image columns)
+        _copy_data_without_images(dataset, new_meta, episode_indices, img_keys)
+
+        # Save episode metadata
+        episodes_df = pd.DataFrame(all_episode_metadata)
+        episodes_path = new_meta.root / "meta" / "episodes" / "chunk-000" / "file-000.parquet"
+        episodes_path.parent.mkdir(parents=True, exist_ok=True)
+        episodes_df.to_parquet(episodes_path, index=False)
+
+        # Update metadata info
+        new_meta.info["total_episodes"] = len(episode_indices)
+        new_meta.info["total_frames"] = sum(ep["length"] for ep in all_episode_metadata)
+        new_meta.info["total_tasks"] = dataset.meta.total_tasks
+        new_meta.info["splits"] = {"train": f"0:{len(episode_indices)}"}
+
+        # Update video info for all image keys (now videos)
+        # We need to manually set video info since update_video_info() checks video_keys first
+        for img_key in img_keys:
+            if not new_meta.features[img_key].get("info", None):
+                video_path = new_meta.root / new_meta.video_path.format(
+                    video_key=img_key, chunk_index=0, file_index=0
+                )
+                new_meta.info["features"][img_key]["info"] = get_video_info(video_path)
+
+        from lerobot.datasets.utils import write_info
+
+        write_info(new_meta.info, new_meta.root)
+
+        # Copy stats and tasks
+        if dataset.meta.stats is not None:
+            # Remove image stats
+            new_stats = {k: v for k, v in dataset.meta.stats.items() if k not in img_keys}
+            write_stats(new_stats, new_meta.root)
+
+        if dataset.meta.tasks is not None:
+            write_tasks(dataset.meta.tasks, new_meta.root)
+
+    finally:
+        # Clean up temporary directory
+        if temp_dir.exists():
+            shutil.rmtree(temp_dir)
+
+    logging.info(f"✓ Completed converting {dataset.repo_id} to video format")
+    logging.info(f"New dataset saved to: {output_dir}")
+
+    # Return new dataset
+    return LeRobotDataset(repo_id=repo_id, root=output_dir)
+
+
+def _copy_data_without_images(
+    src_dataset: LeRobotDataset,
+    dst_meta: LeRobotDatasetMetadata,
+    episode_indices: list[int],
+    img_keys: list[str],
+) -> None:
+    """Copy data files without image columns.
+
+    Args:
+        src_dataset: Source dataset
+        dst_meta: Destination metadata
+        episode_indices: Episodes to include
+        img_keys: Image keys to remove
+    """
+    from lerobot.datasets.utils import DATA_DIR
+
+    data_dir = src_dataset.root / DATA_DIR
+    parquet_files = sorted(data_dir.glob("*/*.parquet"))
+
+    if not parquet_files:
+        raise ValueError(f"No parquet files found in {data_dir}")
+
+    episode_set = set(episode_indices)
+
+    for src_path in tqdm(parquet_files, desc="Processing data files"):
+        df = pd.read_parquet(src_path).reset_index(drop=True)
+
+        # Filter to only include selected episodes
+        df = df[df["episode_index"].isin(episode_set)].copy()
+
+        if len(df) == 0:
+            continue
+
+        # Remove image columns
+        columns_to_drop = [col for col in img_keys if col in df.columns]
+        if columns_to_drop:
+            df = df.drop(columns=columns_to_drop)
+
+        # Get chunk and file indices from path
+        relative_path = src_path.relative_to(src_dataset.root)
+        chunk_dir = relative_path.parts[1]
+        file_name = relative_path.parts[2]
+        chunk_idx = int(chunk_dir.split("-")[1])
+        file_idx = int(file_name.split("-")[1].split(".")[0])
+
+        # Write to destination without pandas index
+        dst_path = dst_meta.root / f"data/chunk-{chunk_idx:03d}/file-{file_idx:03d}.parquet"
+        dst_path.parent.mkdir(parents=True, exist_ok=True)
+        df.to_parquet(dst_path, index=False)
+
+
+def handle_convert_to_video(cfg: EditDatasetConfig) -> None:
+    # Note: Parser may create any config type with the right fields, so we access fields directly
+    # instead of checking isinstance()
+    dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)
+
+    # Determine output directory and repo_id
+    # Priority: 1) new_repo_id, 2) operation.output_dir, 3) auto-generated name
+    output_dir_config = getattr(cfg.operation, "output_dir", None)
+
+    if cfg.new_repo_id:
+        # Use new_repo_id for both local storage and hub push
+        output_repo_id = cfg.new_repo_id
+        output_dir = Path(cfg.root) / cfg.new_repo_id if cfg.root else HF_LEROBOT_HOME / cfg.new_repo_id
+        logging.info(f"Saving to new dataset: {cfg.new_repo_id}")
+    elif output_dir_config:
+        # Use custom output directory for local-only storage
+        output_dir = Path(output_dir_config)
+        # Extract repo name from output_dir for the dataset
+        output_repo_id = output_dir.name
+        logging.info(f"Saving to local directory: {output_dir}")
+    else:
+        # Auto-generate name: append "_video" to original repo_id
+        output_repo_id = f"{cfg.repo_id}_video"
+        output_dir = Path(cfg.root) / output_repo_id if cfg.root else HF_LEROBOT_HOME / output_repo_id
+        logging.info(f"Saving to auto-generated location: {output_dir}")
+
+    logging.info(f"Converting dataset {cfg.repo_id} to video format")
+
+    new_dataset = convert_dataset_to_videos(
+        dataset=dataset,
+        output_dir=output_dir,
+        repo_id=output_repo_id,
+        vcodec=getattr(cfg.operation, "vcodec", "libsvtav1"),
+        pix_fmt=getattr(cfg.operation, "pix_fmt", "yuv420p"),
+        g=getattr(cfg.operation, "g", 2),
+        crf=getattr(cfg.operation, "crf", 30),
+        fast_decode=getattr(cfg.operation, "fast_decode", 0),
+        episode_indices=getattr(cfg.operation, "episode_indices", None),
+        num_workers=getattr(cfg.operation, "num_workers", 4),
+    )
+
+    logging.info("Video dataset created successfully!")
+    logging.info(f"Location: {output_dir}")
+    logging.info(f"Episodes: {new_dataset.meta.total_episodes}")
+    logging.info(f"Frames: {new_dataset.meta.total_frames}")
+
+    if cfg.push_to_hub:
+        logging.info(f"Pushing to hub as {output_repo_id}...")
+        new_dataset.push_to_hub()
+        logging.info("✓ Successfully pushed to hub!")
+    else:
+        logging.info("Dataset saved locally (not pushed to hub)")
+
+
@parser.wrap()
 def edit_dataset(cfg: EditDatasetConfig) -> None:
    operation_type = cfg.operation.type
@@ -270,10 +718,12 @@ def edit_dataset(cfg: EditDatasetConfig) -> None:
        handle_merge(cfg)
    elif operation_type == "remove_feature":
        handle_remove_feature(cfg)
+    elif operation_type == "convert_to_video":
+        handle_convert_to_video(cfg)
    else:
        raise ValueError(
            f"Unknown operation type: {operation_type}\n"
-            f"Available operations: delete_episodes, split, merge, remove_feature"
+            f"Available operations: delete_episodes, split, merge, remove_feature, convert_to_video"
        )


@@ -278,9 +278,17 @@ def eval_policy(
        raise ValueError("If max_episodes_rendered > 0, videos_dir must be provided.")

    if not isinstance(policy, PreTrainedPolicy):
-        raise ValueError(
-            f"Policy of type 'PreTrainedPolicy' is expected, but type '{type(policy)}' was provided."
-        )
+        try:
+            from peft import PeftModel
+
+            if not isinstance(policy, PeftModel):
+                raise ValueError(
+                    f"Policy of type 'PreTrainedPolicy' is expected, but type '{type(policy)}' was provided."
+                )
+        except ImportError:
+            raise ValueError(
+                "PEFT is required to evaluate a PEFT-trained policy. Please install PEFT using `pip install peft`."
+            ) from None

    start = time.time()
    policy.eval()
@@ -46,6 +46,7 @@ from lerobot.robots import (  # noqa: F401
    RobotConfig,
    koch_follower,
    make_robot_from_config,
+    omx_follower,
    so100_follower,
    so101_follower,
 )
@@ -54,6 +55,7 @@ from lerobot.teleoperators import (  # noqa: F401
    gamepad,
    koch_leader,
    make_teleoperator_from_config,
+    omx_leader,
    so100_leader,
    so101_leader,
 )
@@ -27,6 +27,25 @@ lerobot-info

 import importlib
 import platform
+import shutil
+import subprocess
+from importlib.metadata import PackageNotFoundError, distribution
+
+PACKAGE_NAME = "lerobot"
+
+
+def get_ffmpeg_version() -> str:
+    """Get the ffmpeg version if installed, otherwise return 'N/A'."""
+    command_path = shutil.which("ffmpeg")
+    if command_path is None:
+        return "N/A"
+    try:
+        result = subprocess.run([command_path, "-version"], capture_output=True, text=True, check=True)
+        first_line = result.stdout.splitlines()[0]
+        version_info = first_line.split(" ")[2]
+        return version_info
+    except (subprocess.SubprocessError, IndexError):
+        return "Installed (version parsing failed)"


 def get_package_version(package_name: str) -> str:
@@ -38,16 +57,17 @@ def get_package_version(package_name: str) -> str:
        return "N/A"


-def get_sys_info() -> dict:
+def get_sys_info() -> dict[str, str]:
    """Run this to get basic system info to help for tracking issues & bugs."""
    # General package versions
    info = {
-        "lerobot version": get_package_version("lerobot"),
+        "LeRobot version": get_package_version(PACKAGE_NAME),
        "Platform": platform.platform(),
        "Python version": platform.python_version(),
        "Huggingface Hub version": get_package_version("huggingface_hub"),
        "Datasets version": get_package_version("datasets"),
        "Numpy version": get_package_version("numpy"),
+        "FFmpeg version": get_ffmpeg_version(),
    }

    # PyTorch and GPU specific information
@@ -58,10 +78,10 @@ def get_sys_info() -> dict:
    try:
        import torch

-        torch_version = torch.__version__
+        torch_version = str(torch.__version__)
        torch_cuda_available = torch.cuda.is_available()
        if torch_cuda_available:
-            cuda_version = torch.version.cuda
+            cuda_version = str(torch.version.cuda)
            # Gets the name of the first available GPU
            gpu_model = torch.cuda.get_device_name(0)
    except ImportError:
@@ -71,24 +91,34 @@ def get_sys_info() -> dict:
    info.update(
        {
            "PyTorch version": torch_version,
-            "Is PyTorch built with CUDA support?": torch_cuda_available,
+            "Is PyTorch built with CUDA support?": str(torch_cuda_available),
            "Cuda version": cuda_version,
            "GPU model": gpu_model,
            "Using GPU in script?": "<fill in>",
        }
    )
+    scripts = "N/A"
+    try:
+        dist = distribution(PACKAGE_NAME)
+        scripts = [ep.name for ep in dist.entry_points if ep.group == "console_scripts"]
+    except PackageNotFoundError:
+        pass
+
+    info.update({f"{PACKAGE_NAME} scripts": str(scripts)})

    return info


-def format_dict_for_markdown(d: dict) -> str:
+def format_dict_for_markdown(d: dict[str, str]) -> str:
    """Formats a dictionary into a markdown-friendly bulleted list."""
    return "\n".join([f"- {prop}: {val}" for prop, val in d.items()])


 def main():
+    """
+    Main function to print system info in markdown format.
+    """
    system_info = get_sys_info()
-    print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the last point.\n")
    print(format_dict_for_markdown(system_info))


@@ -97,6 +97,7 @@ from lerobot.robots import (  # noqa: F401
    hope_jr,
    koch_follower,
    make_robot_from_config,
+    omx_follower,
    so100_follower,
    so101_follower,
 )
@@ -107,6 +108,7 @@ from lerobot.teleoperators import (  # noqa: F401
    homunculus,
    koch_leader,
    make_teleoperator_from_config,
+    omx_leader,
    so100_leader,
    so101_leader,
 )
@@ -191,8 +193,10 @@ class RecordConfig:
    def __post_init__(self):
        # HACK: We parse again the cli args here to get the pretrained path if there was one.
        policy_path = parser.get_path_arg("policy")
+
        if policy_path:
            cli_overrides = parser.get_cli_overrides("policy")
+
            self.policy = PreTrainedConfig.from_pretrained(policy_path, cli_overrides=cli_overrides)
            self.policy.pretrained_path = policy_path

@@ -270,7 +274,12 @@ def record_loop(
                for t in teleop
                if isinstance(
                    t,
-                    (so100_leader.SO100Leader | so101_leader.SO101Leader | koch_leader.KochLeader),
+                    (
+                        so100_leader.SO100Leader
+                        | so101_leader.SO101Leader
+                        | koch_leader.KochLeader
+                        | omx_leader.OmxLeader
+                    ),
                )
            ),
            None,
@@ -397,82 +406,63 @@ def record(cfg: RecordConfig) -> LeRobotDataset:
        ),
    )

-    if cfg.resume:
-        dataset = LeRobotDataset(
-            cfg.dataset.repo_id,
-            root=cfg.dataset.root,
-            batch_encoding_size=cfg.dataset.video_encoding_batch_size,
-        )
+    dataset = None
+    listener = None

-        if hasattr(robot, "cameras") and len(robot.cameras) > 0:
-            dataset.start_image_writer(
-                num_processes=cfg.dataset.num_image_writer_processes,
-                num_threads=cfg.dataset.num_image_writer_threads_per_camera * len(robot.cameras),
-            )
-        sanity_check_dataset_robot_compatibility(dataset, robot, cfg.dataset.fps, dataset_features)
-    else:
-        # Create empty dataset or load existing saved episodes
-        sanity_check_dataset_name(cfg.dataset.repo_id, cfg.policy)
-        dataset = LeRobotDataset.create(
-            cfg.dataset.repo_id,
-            cfg.dataset.fps,
-            root=cfg.dataset.root,
-            robot_type=robot.name,
-            features=dataset_features,
-            use_videos=cfg.dataset.video,
-            image_writer_processes=cfg.dataset.num_image_writer_processes,
-            image_writer_threads=cfg.dataset.num_image_writer_threads_per_camera * len(robot.cameras),
-            batch_encoding_size=cfg.dataset.video_encoding_batch_size,
-        )
-
-    # Load pretrained policy
-    policy = None if cfg.policy is None else make_policy(cfg.policy, ds_meta=dataset.meta)
-    preprocessor = None
-    postprocessor = None
-    if cfg.policy is not None:
-        preprocessor, postprocessor = make_pre_post_processors(
-            policy_cfg=cfg.policy,
-            pretrained_path=cfg.policy.pretrained_path,
-            dataset_stats=rename_stats(dataset.meta.stats, cfg.dataset.rename_map),
-            preprocessor_overrides={
-                "device_processor": {"device": cfg.policy.device},
-                "rename_observations_processor": {"rename_map": cfg.dataset.rename_map},
-            },
-        )
-
-    robot.connect()
-    if teleop is not None:
-        teleop.connect()
-
-    listener, events = init_keyboard_listener()
-
-    with VideoEncodingManager(dataset):
-        recorded_episodes = 0
-        while recorded_episodes < cfg.dataset.num_episodes and not events["stop_recording"]:
-            log_say(f"Recording episode {dataset.num_episodes}", cfg.play_sounds)
-            record_loop(
-                robot=robot,
-                events=events,
-                fps=cfg.dataset.fps,
-                teleop_action_processor=teleop_action_processor,
-                robot_action_processor=robot_action_processor,
-                robot_observation_processor=robot_observation_processor,
-                teleop=teleop,
-                policy=policy,
-                preprocessor=preprocessor,
-                postprocessor=postprocessor,
-                dataset=dataset,
-                control_time_s=cfg.dataset.episode_time_s,
-                single_task=cfg.dataset.single_task,
-                display_data=cfg.display_data,
+    try:
+        if cfg.resume:
+            dataset = LeRobotDataset(
+                cfg.dataset.repo_id,
+                root=cfg.dataset.root,
+                batch_encoding_size=cfg.dataset.video_encoding_batch_size,
            )

-            # Execute a few seconds without recording to give time to manually reset the environment
-            # Skip reset for the last episode to be recorded
-            if not events["stop_recording"] and (
-                (recorded_episodes < cfg.dataset.num_episodes - 1) or events["rerecord_episode"]
-            ):
-                log_say("Reset the environment", cfg.play_sounds)
+            if hasattr(robot, "cameras") and len(robot.cameras) > 0:
+                dataset.start_image_writer(
+                    num_processes=cfg.dataset.num_image_writer_processes,
+                    num_threads=cfg.dataset.num_image_writer_threads_per_camera * len(robot.cameras),
+                )
+            sanity_check_dataset_robot_compatibility(dataset, robot, cfg.dataset.fps, dataset_features)
+        else:
+            # Create empty dataset or load existing saved episodes
+            sanity_check_dataset_name(cfg.dataset.repo_id, cfg.policy)
+            dataset = LeRobotDataset.create(
+                cfg.dataset.repo_id,
+                cfg.dataset.fps,
+                root=cfg.dataset.root,
+                robot_type=robot.name,
+                features=dataset_features,
+                use_videos=cfg.dataset.video,
+                image_writer_processes=cfg.dataset.num_image_writer_processes,
+                image_writer_threads=cfg.dataset.num_image_writer_threads_per_camera * len(robot.cameras),
+                batch_encoding_size=cfg.dataset.video_encoding_batch_size,
+            )
+
+        # Load pretrained policy
+        policy = None if cfg.policy is None else make_policy(cfg.policy, ds_meta=dataset.meta)
+        preprocessor = None
+        postprocessor = None
+        if cfg.policy is not None:
+            preprocessor, postprocessor = make_pre_post_processors(
+                policy_cfg=cfg.policy,
+                pretrained_path=cfg.policy.pretrained_path,
+                dataset_stats=rename_stats(dataset.meta.stats, cfg.dataset.rename_map),
+                preprocessor_overrides={
+                    "device_processor": {"device": cfg.policy.device},
+                    "rename_observations_processor": {"rename_map": cfg.dataset.rename_map},
+                },
+            )
+
+        robot.connect()
+        if teleop is not None:
+            teleop.connect()
+
+        listener, events = init_keyboard_listener()
+
+        with VideoEncodingManager(dataset):
+            recorded_episodes = 0
+            while recorded_episodes < cfg.dataset.num_episodes and not events["stop_recording"]:
+                log_say(f"Recording episode {dataset.num_episodes}", cfg.play_sounds)
                record_loop(
                    robot=robot,
                    events=events,
@@ -481,34 +471,61 @@ def record(cfg: RecordConfig) -> LeRobotDataset:
                    robot_action_processor=robot_action_processor,
                    robot_observation_processor=robot_observation_processor,
                    teleop=teleop,
-                    control_time_s=cfg.dataset.reset_time_s,
+                    policy=policy,
+                    preprocessor=preprocessor,
+                    postprocessor=postprocessor,
+                    dataset=dataset,
+                    control_time_s=cfg.dataset.episode_time_s,
                    single_task=cfg.dataset.single_task,
                    display_data=cfg.display_data,
                )

-            if events["rerecord_episode"]:
-                log_say("Re-record episode", cfg.play_sounds)
-                events["rerecord_episode"] = False
-                events["exit_early"] = False
-                dataset.clear_episode_buffer()
-                continue
+                # Execute a few seconds without recording to give time to manually reset the environment
+                # Skip reset for the last episode to be recorded
+                if not events["stop_recording"] and (
+                    (recorded_episodes < cfg.dataset.num_episodes - 1) or events["rerecord_episode"]
+                ):
+                    log_say("Reset the environment", cfg.play_sounds)
+                    record_loop(
+                        robot=robot,
+                        events=events,
+                        fps=cfg.dataset.fps,
+                        teleop_action_processor=teleop_action_processor,
+                        robot_action_processor=robot_action_processor,
+                        robot_observation_processor=robot_observation_processor,
+                        teleop=teleop,
+                        control_time_s=cfg.dataset.reset_time_s,
+                        single_task=cfg.dataset.single_task,
+                        display_data=cfg.display_data,
+                    )

-            dataset.save_episode()
-            recorded_episodes += 1
+                if events["rerecord_episode"]:
+                    log_say("Re-record episode", cfg.play_sounds)
+                    events["rerecord_episode"] = False
+                    events["exit_early"] = False
+                    dataset.clear_episode_buffer()
+                    continue

-    log_say("Stop recording", cfg.play_sounds, blocking=True)
+                dataset.save_episode()
+                recorded_episodes += 1
+    finally:
+        log_say("Stop recording", cfg.play_sounds, blocking=True)

-    robot.disconnect()
-    if teleop is not None:
-        teleop.disconnect()
+        if dataset:
+            dataset.finalize()

-    if not is_headless() and listener is not None:
-        listener.stop()
+        if robot.is_connected:
+            robot.disconnect()
+        if teleop and teleop.is_connected:
+            teleop.disconnect()

-    if cfg.dataset.push_to_hub:
-        dataset.push_to_hub(tags=cfg.dataset.tags, private=cfg.dataset.private)
+        if not is_headless() and listener:
+            listener.stop()

-    log_say("Exiting", cfg.play_sounds)
+        if cfg.dataset.push_to_hub:
+            dataset.push_to_hub(tags=cfg.dataset.tags, private=cfg.dataset.private)
+
+        log_say("Exiting", cfg.play_sounds)
    return dataset


@@ -58,6 +58,7 @@ from lerobot.robots import (  # noqa: F401
    hope_jr,
    koch_follower,
    make_robot_from_config,
+    omx_follower,
    so100_follower,
    so101_follower,
 )
@@ -33,6 +33,7 @@ from lerobot.robots import (  # noqa: F401
    koch_follower,
    lekiwi,
    make_robot_from_config,
+    omx_follower,
    so100_follower,
    so101_follower,
 )
@@ -40,6 +41,7 @@ from lerobot.teleoperators import (  # noqa: F401
    TeleoperatorConfig,
    koch_leader,
    make_teleoperator_from_config,
+    omx_leader,
    so100_leader,
    so101_leader,
 )
@@ -47,6 +49,8 @@ from lerobot.teleoperators import (  # noqa: F401
 COMPATIBLE_DEVICES = [
    "koch_follower",
    "koch_leader",
+    "omx_follower",
+    "omx_leader",
    "so100_follower",
    "so100_leader",
    "so101_follower",
@@ -75,6 +75,7 @@ from lerobot.robots import (  # noqa: F401
    hope_jr,
    koch_follower,
    make_robot_from_config,
+    omx_follower,
    so100_follower,
    so101_follower,
 )
@@ -87,6 +88,7 @@ from lerobot.teleoperators import (  # noqa: F401
    keyboard,
    koch_leader,
    make_teleoperator_from_config,
+    omx_leader,
    so100_leader,
    so101_leader,
 )
@@ -13,6 +13,7 @@
 # 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 dataclasses
 import logging
 import time
 from contextlib import nullcontext
@@ -62,6 +63,7 @@ def update_policy(
    accelerator: Accelerator,
    lr_scheduler=None,
    lock=None,
+    rabc_weights_provider=None,
 ) -> tuple[MetricsTracker, dict]:
    """
    Performs a single training step to update the policy's weights.
@@ -78,6 +80,7 @@ def update_policy(
        accelerator: The Accelerator instance for distributed training and mixed precision.
        lr_scheduler: An optional learning rate scheduler.
        lock: An optional lock for thread-safe optimizer updates.
+        rabc_weights_provider: Optional RABCWeights instance for sample weighting.

    Returns:
        A tuple containing:
@@ -87,9 +90,30 @@ def update_policy(
    start_time = time.perf_counter()
    policy.train()

+    # Get RA-BC weights if enabled
+    rabc_batch_weights = None
+    rabc_batch_stats = None
+    if rabc_weights_provider is not None:
+        rabc_batch_weights, rabc_batch_stats = rabc_weights_provider.compute_batch_weights(batch)
+
    # Let accelerator handle mixed precision
    with accelerator.autocast():
-        loss, output_dict = policy.forward(batch)
+        # Use per-sample loss when RA-BC is enabled for proper weighting
+        if rabc_batch_weights is not None:
+            # Get per-sample losses
+            per_sample_loss, output_dict = policy.forward(batch, reduction="none")
+
+            # Apply RA-BC weights: L_RA-BC = Σ(w_i * l_i) / (Σw_i + ε)
+            # rabc_batch_weights is already normalized to sum to batch_size
+            epsilon = 1e-6
+            loss = (per_sample_loss * rabc_batch_weights).sum() / (rabc_batch_weights.sum() + epsilon)
+            # Log raw mean weight (before normalization) - this is the meaningful metric
+            output_dict["rabc_mean_weight"] = rabc_batch_stats["raw_mean_weight"]
+            output_dict["rabc_num_zero_weight"] = rabc_batch_stats["num_zero_weight"]
+            output_dict["rabc_num_full_weight"] = rabc_batch_stats["num_full_weight"]
+        else:
+            loss, output_dict = policy.forward(batch)
+
        # TODO(rcadene): policy.unnormalize_outputs(out_dict)

    # Use accelerator's backward method
@@ -124,6 +148,92 @@ def update_policy(
    return train_metrics, output_dict


+def get_default_peft_configuration(policy_type):
+    """Build a basic PEFT configuration for the given policy type assuming that we train a policy from a checkpoint."""
+
+    common_projections = "state_proj|action_in_proj|action_out_proj|action_time_mlp_in|action_time_mlp_out"
+
+    if policy_type == "smolvla":
+        return {
+            "target_modules": rf"(model\.vlm_with_expert\.lm_expert\..*\.(q|v)_proj|model\.({common_projections}))",
+            "modules_to_save": [],
+        }
+    elif policy_type in ("pi0", "pi05"):
+        return {
+            "target_modules": rf"(.*\.gemma_expert\..*\.self_attn.(q|v)_proj|model\.({common_projections}))",
+            "modules_to_save": [],
+        }
+
+    return {"modules_to_save": None}
+
+
+def wrap_policy_in_peft_model(cfg, policy):
+    from peft import PEFT_TYPE_TO_CONFIG_MAPPING, PeftType, get_peft_model
+
+    # Disable all gradients because we'll only train the parameters selected by the PEFT method.
+    # Layers that should receive gradients anyway need to be listed in `modules_to_save`.
+    for p in policy.parameters():
+        p.requires_grad_(False)
+
+    if not cfg.policy.pretrained_path:
+        raise ValueError(
+            "Training from scratch using PEFT. This is unlikely to yield good results. "
+            "Supply a `policy.path` to fine-tune an existing model."
+        )
+
+    if cfg.policy.type == "smolvla" and not cfg.policy.load_vlm_weights:
+        logging.warning(
+            "Training SmolVLA from scratch using PEFT. This is unlikely to yield good results. Set "
+            "`load_vlm_weights=True` to fine-tune the existing policy."
+        )
+
+    peft_config_policy = get_default_peft_configuration(cfg.policy.type)
+    peft_config_cli = dataclasses.asdict(cfg.peft) if cfg.peft else {}
+    peft_config_cli["modules_to_save"] = peft_config_cli["full_training_modules"]  # compatibility with PEFT
+    peft_method_type = PeftType[peft_config_cli["method_type"].upper()]
+    peft_config_cls = PEFT_TYPE_TO_CONFIG_MAPPING[peft_method_type]
+
+    # Handle specific CLI overrides
+    for key in ["target_modules", "modules_to_save", "r"]:
+        if peft_config_cli[key] is not None:
+            peft_config_policy[key] = peft_config_cli[key]
+
+    if "target_modules" not in peft_config_policy:
+        raise ValueError(
+            f"There is no default `target_modules` value for policy {cfg.policy.type}. Please pass it manually."
+        )
+
+    # Init method depends on the used PEFT method, your specific PEFT method
+    # might not be considered here, in that case an error is raised.
+    if peft_config_cli["init_type"] is not None:
+        if peft_method_type == "LORA":
+            peft_config_policy["init_lora_weights"] = peft_config_cli["init_type"]
+        elif peft_method_type == "MISS":
+            peft_config_policy["init_weights"] = peft_config_cli["init_type"]
+        else:
+            raise ValueError(
+                f"Init type {peft_config_cli['init_type']} unknown for PEFT method {peft_method_type}."
+            )
+
+    # PEFT uses this attribute to set adapter_config.base_name_or_path which we use for loading the
+    # correct base model in `make_policy` since in a PEFT loading setting we only get the path to the
+    # adapter, not the base model.
+    if policy.config.pretrained_path:
+        policy.name_or_path = str(policy.config.pretrained_path)
+
+    # Finally wrap the policy in a PEFT model
+    policy = get_peft_model(
+        policy,
+        peft_config_cls(**peft_config_policy),
+    )
+
+    # Make sure that the config is tagged as using PEFT so that the loading code can take the
+    # appropriate steps to use the adapter weights and the PEFT config instead of the full model weights.
+    policy.config.use_peft = True
+
+    return policy
+
+
@parser.wrap()
 def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
    """
@@ -141,8 +251,6 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
        cfg: A `TrainPipelineConfig` object containing all training configurations.
        accelerator: Optional Accelerator instance. If None, one will be created automatically.
    """
-    cfg.validate()
-
    # Create Accelerator if not provided
    # It will automatically detect if running in distributed mode or single-process mode
    # We set step_scheduler_with_optimizer=False to prevent accelerate from adjusting the lr_scheduler steps based on the num_processes
@@ -159,6 +267,8 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
    # When using accelerate, only the main process should log to avoid duplicate outputs
    is_main_process = accelerator.is_main_process

+    cfg.validate()
+
    # Only log on main process
    if is_main_process:
        logging.info(pformat(cfg.to_dict()))
@@ -207,6 +317,10 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
        rename_map=cfg.rename_map,
    )

+    if cfg.peft is not None:
+        logging.info("Using PEFT! Wrapping model.")
+        policy = wrap_policy_in_peft_model(cfg, policy)
+
    # Wait for all processes to finish policy creation before continuing
    accelerator.wait_for_everyone()

@@ -217,6 +331,10 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
        # Only provide dataset_stats when not resuming from saved processor state
        processor_kwargs["dataset_stats"] = dataset.meta.stats

+    # For SARM, always provide dataset_meta for progress normalization
+    if cfg.policy.type == "sarm":
+        processor_kwargs["dataset_meta"] = dataset.meta
+
    if cfg.policy.pretrained_path is not None:
        processor_kwargs["preprocessor_overrides"] = {
            "device_processor": {"device": device.type},
@@ -248,6 +366,29 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
        logging.info("Creating optimizer and scheduler")
    optimizer, lr_scheduler = make_optimizer_and_scheduler(cfg, policy)

+    # Load precomputed SARM progress for RA-BC if enabled
+    # Generate progress using: src/lerobot/policies/sarm/compute_rabc_weights.py
+    rabc_weights = None
+    if cfg.use_rabc:
+        from lerobot.utils.rabc import RABCWeights
+
+        # Get chunk_size from policy config
+        chunk_size = getattr(policy.config, "chunk_size", None)
+        if chunk_size is None:
+            raise ValueError("Chunk size is not found in policy config")
+
+        head_mode = getattr(cfg, "rabc_head_mode", "sparse")
+        logging.info(f"Loading SARM progress for RA-BC from {cfg.rabc_progress_path}")
+        logging.info(f"Using chunk_size={chunk_size} from policy config, head_mode={head_mode}")
+        rabc_weights = RABCWeights(
+            progress_path=cfg.rabc_progress_path,
+            chunk_size=chunk_size,
+            head_mode=head_mode,
+            kappa=getattr(cfg, "rabc_kappa", 0.01),
+            epsilon=getattr(cfg, "rabc_epsilon", 1e-6),
+            device=device,
+        )
+
    step = 0  # number of policy updates (forward + backward + optim)

    if cfg.resume:
@@ -327,7 +468,9 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
    )

    if is_main_process:
-        logging.info("Start offline training on a fixed dataset")
+        logging.info(
+            f"Start offline training on a fixed dataset, with effective batch size: {effective_batch_size}"
+        )

    for _ in range(step, cfg.steps):
        start_time = time.perf_counter()
@@ -343,6 +486,7 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
            cfg.optimizer.grad_clip_norm,
            accelerator=accelerator,
            lr_scheduler=lr_scheduler,
+            rabc_weights_provider=rabc_weights,
        )

        # Note: eval and checkpoint happens *after* the `step`th training update has completed, so we
@@ -359,6 +503,16 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
                wandb_log_dict = train_tracker.to_dict()
                if output_dict:
                    wandb_log_dict.update(output_dict)
+                # Log RA-BC statistics if enabled
+                if rabc_weights is not None:
+                    rabc_stats = rabc_weights.get_stats()
+                    wandb_log_dict.update(
+                        {
+                            "rabc_delta_mean": rabc_stats["delta_mean"],
+                            "rabc_delta_std": rabc_stats["delta_std"],
+                            "rabc_num_frames": rabc_stats["num_frames"],
+                        }
+                    )
                wandb_logger.log_dict(wandb_log_dict, step)
            train_tracker.reset_averages()

@@ -439,7 +593,10 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):

        if cfg.policy.push_to_hub:
            unwrapped_policy = accelerator.unwrap_model(policy)
-            unwrapped_policy.push_model_to_hub(cfg)
+            if cfg.policy.use_peft:
+                unwrapped_policy.push_model_to_hub(cfg, peft_model=unwrapped_policy)
+            else:
+                unwrapped_policy.push_model_to_hub(cfg)
            preprocessor.push_to_hub(cfg.policy.repo_id)
            postprocessor.push_to_hub(cfg.policy.repo_id)

--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Pepijn	56fd0ef378	run precommit and fix issues	2025-12-22 14:50:58 +01:00
Pepijn	486c52c14b	Merge branch 'main' into feature/basic-peft-support	2025-12-22 14:29:51 +01:00
Tong Wu	17c5a0774f	feat: support wallx model (#2593 ) * support wallx * fix bugs in flow * incorporate wallx model into lerobot * update the policy methods * reduce to least config and params & pass lerobot basic test * fixed dtype bugs * add wallx dependencies * update * remove flash-attn requirement && fix bug in inference and fast mode * fix bug for inference * add some small modifications * fix pre-commit errors * remove lerobot[wallx] * fix ci * fix precommit issues * fix: exclude wallx extra properly in CI workflows * fix: add uv conflicts for wallx transformers version * fix: peft test import * pre-commit * only export WallXConfig from wall_x package to avoid peft import in CI * remove torch dep * precommit * add import --------- Co-authored-by: vincentchen <chenlufang@x2robot.com> Co-authored-by: Geoffrey19 <sympathischmann35@gmail.com> Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> Co-authored-by: Pepijn <pepijn@huggingface.co>	2025-12-22 10:12:39 +01:00
Pepijn	0071b1ff6e	Add readme (#2698 ) * Add readme * change ref	2025-12-22 10:04:33 +01:00
nemo	ad4a82b77e	Add peft as extra dependency, mark tests Fast tests currently fail because of the missing dependency.	2025-12-20 19:20:12 +01:00
nemo	4bc75776f7	Formatting	2025-12-20 19:18:56 +01:00
Clément Verrier	00b5f65752	fix(optim): enable and resolve mypy type errors (#2683 ) * fix(optim): enable and resolve mypy type errors Resolves #1729 build(deps): add mypy as dependency and update pre-commit hook * change build's type annotation	2025-12-20 17:19:42 +01:00
Francesco Capuano	2f6c870c4b	Fixes ReadMe Policies Classification (#2682 ) * fix: tdmpc is a model-based RL method, does not learn from expert demonstrations so no IL there * fix: typo * remove trailing space Signed-off-by: Francesco Capuano <74058581+fracapuano@users.noreply.github.com> * fix: minor --------- Signed-off-by: Francesco Capuano <74058581+fracapuano@users.noreply.github.com>	2025-12-20 17:11:02 +01:00
nemo	43ca1dc216	Merge remote-tracking branch 'hf/main' into feature/basic-peft-support	2025-12-19 12:23:37 +01:00
nemo	6a9dac7c20	Add basic documentation	2025-12-19 12:20:50 +01:00
nemo	54c25a4400	Disallow PEFT training on non-pretrained policies At first I thought it would make sense to have this feature in case you want to fine-tune a pre-trained section but in the end it makes more trouble than it's worth. It's still possible to allow this in the future when a concrete need arises.	2025-12-19 11:50:29 +01:00
Steven Palma	0bd1969d0a	feat(docs): modernize readme (#2660 )	2025-12-18 19:45:13 +01:00
Pepijn	f04958527e	Add sarm (#2639 ) * add initial modeling * make rewind pretrained policy * add annotation * small fix * add sarm * subtasks * fix spawn * fix rewind discrepancies * Add script to generate embedding for dataset (#2138) * Add generate and validate script * fix precommit * Improve generate embeddings function by using dataset tools (#2206) --------- Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co> * cleanup * change order train log * print batch size * update sarm processor * add reward output * change expected features * add image validation * change validation * get state input from dataset stats * raise if no state key is found * pass stats * cleanup and refactor * add episode inddex to complementary data * add subtask init and detection * revert lerobot_train changes * pass dataset metadata to policy * change loadig subtasks * add small logging * fix progress conversion and adding initial frame * use large offset for initial frame (ugly) * Remove rewind, use clip tokenizer * add tests, implement formula 1,2 correctly and cleanup * use task from dataset, cleanup visualizer * simplify * simplify and cleanup code and move compute_temporal_proportions to utils * fix normalization in visualization * Fix visualization and change prompt * fix formatting * add visualize subtask annotations * use qwen thinking * try different prompt * format * update prompt * higher temp, long output * different settings * use instruct * show full resp * split message * Temp: increase tolerance dataset * Fix RA-BC (#2572) * Add next observation loading for RA-BC progress deltas * Compute weights based on temporal progress deltas instead of static rewards * Add hard-masking for negative progress deltas in weight computation * Feat/add dual head (#2582) * Add dual dense sparse head and annotation * Add docs * add dual to procesor * cleanup * change sampling in visualize and cleanup * remove validation * remove compile * Feat/test uniform (#2587) * test uniform * add different string for misaligned * Fix rewind and add tests * uncomment text implementation * run precommit * Add head mode for ra-bc * fix visalization of single task * add * return per sample loss * Fix RA_BC (#2602) * update rabc implementation * compute rabc beforehand * fix import * add only progress calulation * use precomputed progress * multi gpu processing * import * fix dataset meta data extraction * add logging * logging * log * progress per episode * split differently * move clip to gpu * pre decode frames for an episode * fix cuda initalization * fix import * multi processing * rename * fix import * fix * fix rabc * use last known progress if oob * use last known progress if oob * add misalignment loss with random embeddings * discard previous changes * add selection of models to docs for ra_bc * add transformers dep * extend tolerance * initial commit with new codebase * add tests * fix * remove temporal sampler * drop last frame for sampler * use original ref * some fixes * fix visualization * remove smoothing and fix order subtasks * add stride rabc computation * add push to hub * add explanation * add kappa expllaination * better rabc logging * feedback pr * remove dataset tolerance * revert dataset tool * revert dataset changes * add credit * run precommit * change path for generate ra_bc * fix type * include sarm in all in pyproject * fix precommit * lazy import matplotlib * lazy import qwen * remove rich console * skip if transformers is not installed? * run only when we have faker * place transformer lazy loading * Dont test if low transformer version * fix * increase transformer * increase as 4.57.0 is yanked * remove pi from all * go back --------- Co-authored-by: Michel Aractingi <michel.aractingi@huggingface.co> Co-authored-by: s1lent4gnt <kmeftah.khalil@gmail.com>	2025-12-18 12:50:32 +01:00
Steven Palma	4a151a9682	chore(ci): minor improvement bug-report template & pr auto label (#2676 ) * chore(ci): minor improvement bug-report template * chore(ci): change triggers for PR auto label	2025-12-18 00:23:23 +01:00
Steven Palma	8667b9ef08	chore(ci): minor improvements auto labeling (#2675 )	2025-12-17 22:54:47 +01:00
Steven Palma	86eee5c1e2	fix(ci): close bracket pattern (#2674 )	2025-12-17 22:40:33 +01:00
Steven Palma	469b855e42	fix(ci): better heuristic + issue type template fix (#2672 ) * fix(ci): better heuristic + issue type template fix * chore(ci): remove keywords in performance tag	2025-12-17 22:31:22 +01:00
Steven Palma	292333cafc	chore(ci): update issue template (#2666 )	2025-12-17 18:02:20 +01:00
Steven Palma	f0c98e23f1	feat(ci): simple automatic labelling (#2667 ) * ci: add pr labeler * ci: add issue labeler * ci: minor fixes for labelers * fix(ci): add explicit path for pr labeler	2025-12-17 17:52:45 +01:00
Steven Palma	7621af5acd	chore(ci): update PR template (#2665 ) * chore: update code of conduct to transformers one * chore: update PR template	2025-12-17 17:10:04 +01:00
Steven Palma	92fdbe9bbf	docs(dataset): add visualization section (#2664 )	2025-12-17 14:14:31 +01:00
Steven Palma	b303d1ab38	feat(scripts): add more info to lerobot-info (#2663 )	2025-12-17 14:14:23 +01:00
Steven Palma	b1d162f333	fix(policies): add device back to smolvlm expert (#2662 )	2025-12-17 12:12:03 +01:00
nemo	e01927f641	Merge remote-tracking branch 'hf/main' into feature/basic-peft-support	2025-12-16 18:35:11 +01:00
nemo	3bc557552b	Adapt state/action projections instead of full-finetuning There doesn't seem to be a benefit to fully fine-tune these layers over just adapting them, so we do that instead.	2025-12-16 18:31:25 +01:00
nemo	51876e7f55	Add test checking that PEFT actually reduces params	2025-12-16 17:58:27 +01:00
nemo	9649f140ca	Make it possible to use PeftModels in eval	2025-12-16 17:57:03 +01:00
Steven Palma	2b304eeb84	feat(dataset): expose tolerance_s argument to training config (#2653 )	2025-12-16 00:53:19 +01:00
Sota Nakamura	4e6048a221	finalize the dataset after recording (#2496 ) Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>	2025-12-15 17:57:04 +01:00
./c²	81ebcac8d7	docs: update IL robots API example and add OpenCV workaround (#2648 ) * docs: update IL robots API example and add OpenCV workaround - Fix import path from lerobot.record to lerobot.scripts.lerobot_record - Add required processor parameters to record_loop calls - Document fourcc="MJPG" workaround for OpenCV async errors - Improve code formatting in robot configuration examples Fixes compatibility issues for users following the tutorial on embedded systems and ensures API examples match current codebase requirements. * Update il_robots.mdx Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Signed-off-by: ./c² <cagataycali@icloud.com> --------- Signed-off-by: ./c² <cagataycali@icloud.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>	2025-12-15 17:56:33 +01:00
Martino Russi	a6c3a0fa09	Feat/add mj env (#2613 ) * add sim support * close fix threading issues	2025-12-15 16:22:27 +01:00
Woojin Wie	c2fb644613	feat(robot): Add support for OMX robot (#2614 ) * upload * feat(omx): simplify motor initialization and remove default calibration files * feat(omx): read motor positions without normalization for improved accuracy * update calibration method for return factory value Signed-off-by: Junha Cha <ckwnsgk1@gachon.ac.kr> * change the drive mode * refactor: clean up code by removing unnecessary blank lines in omx_follower and omx_leader modules * feat(omx): update calibration method to set drive modes for motors * feat(pyproject): add 'ROBOTIS' to extend-ignore-identifiers-re list * feat(omx): enhance calibration method to write default drive modes to motors * Update src/lerobot/robots/omx_follower/__init__.py Add informations about the robot Co-authored-by: Steven Palma <imstevenpmwork@ieee.org> Signed-off-by: Woojin Wie <dnldnwls1123@gmail.com> --------- Signed-off-by: Junha Cha <ckwnsgk1@gachon.ac.kr> Signed-off-by: Woojin Wie <dnldnwls1123@gmail.com> Co-authored-by: Junha02 <chajunha2023@naver.com> Co-authored-by: Junha Cha <ckwnsgk1@gachon.ac.kr> Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>	2025-12-15 15:50:29 +01:00
Jade Choghari	1d07a4aefd	add auto in docs (#2645 ) Signed-off-by: Jade Choghari <chogharijade@gmail.com>	2025-12-13 17:11:19 +01:00
Michel Aractingi	ce348a3460	enable variable image sizes to pi0/pi0.5 (#2609 ) * enable variable image sizes to pi0/pi0.5 * add square image assertion	2025-12-10 19:41:11 +01:00
Jade Choghari	cb920235c4	docs: update X-VLA training strategies/commands (#2611 )	2025-12-09 19:08:09 +01:00
Jade Choghari	7f40b3bf82	feat(dataset): add tool to convert images to video datasets (#2560 ) * add video encoding tool * style * make it work * more fixes	2025-12-08 18:50:21 +01:00
Michel Aractingi	2e9c9fd832	Replay while loop in sample actions with for loops (#2600 )	2025-12-08 14:47:54 +01:00
Steven Palma	f9cb5e659c	chore(ci): skip workflows if not lerobot repository (#2601 ) Co-authored-by: Alex Tyshka <atyshka15@gmail.com>	2025-12-08 12:44:36 +01:00
nemo	76e64521c2	Use correct loading for PEFT in RTC example	2025-12-05 14:26:30 +01:00
nemo	f5bf6bb028	Make it possible to unset policy features This is necessary to train pre-trained policies on new datasets so that the features are inferred from the new dataset and not from the pretrained policy.	2025-12-05 12:48:07 +01:00
nemo	75266860aa	Revamp pretrained model loading There were quite a few factors that convinced me that the status quo is able to load pretrained models from the PEFT adapter config but in fact that didn't work. This commit fixes the following things: - policies wrapped in PEFT will now have a `name_or_path` attribute containing the name or path of the pretrained model we're fine-tuning - we further assume that SmolVLA without `pretrained_path` and `load_vlm_weights==False` must be an user-side error - we assume that using PEFT on from-scratch-policies must be an user-side-error	2025-12-01 18:30:45 +01:00
nemo	e0b6aca97a	Merge remote-tracking branch 'hf/main' into feature/basic-peft-support	2025-11-28 19:00:16 +01:00
nemo	abfed126ff	Warn when encountering from-scratch-training	2025-11-28 18:18:00 +01:00
nemo	e544562c39	formatting	2025-11-24 18:52:18 +01:00
nemo	83aac1b42e	Make sure push_to_hub works Since PEFT only wraps `push_to_hub` and not `push_model_to_hub`, the reference to `self` in `policy.push_model_to_hub` is the unwrapped policy which, of course, doesn't know anything about PEFT. To make the upload process aware of PEFT, we pass the unwrapped policy down to `push_model_to_hub` as a kwarg. This is not ideal but I think it is the best way for now.	2025-11-24 18:50:02 +01:00
nemo	e9b3889bd2	Clean up loading code - Centralized instantiation of the PEFT wrapper in `make_policy` for inference (e.g. in `lerobot-record`) - Training a PEFT policy also sets `cfg.use_peft` so that all inference code loading the policy can rely on that attribute to identify if PEFT loading is needed - Modified RTC example to also include PEFT policies. Mostly because this is an example I'm currently exploring.	2025-11-24 15:30:26 +01:00
nemo	841c76c7d3	Merge remote-tracking branch 'hf/main' into feature/basic-peft-support	2025-11-21 17:37:15 +01:00
nemo	356f2a6150	Merge remote-tracking branch 'origin/main' into feature/basic-peft-support	2025-11-21 11:29:33 +01:00
nemo	de34e876cc	Update default targets Removed ACT since it doesn't make sense to fine-tune ACT without having it pretrained beforehand. SmolVLA and Pi0/0.5 are much more senseful targets.	2025-10-16 16:13:41 +02:00
nemo	7f42338ae9	Add CLI end-to-end tests Currently there don't seem to be any way to test the CLI commands. Since this change mostly happens in those I thought it best to add a way to test these commands end-to-end. More integrated commands like `lerobot-record` need patching but standalone commands like training seem to work fine.	2025-10-16 16:08:20 +02:00
nemo	6c2fd8970c	Correct way of identifying when to save config	2025-10-16 16:07:22 +02:00
nemo	6fb8539159	Don't unload & merge the PEFT model This can make things hard when using quantized layers (user expects quantized base layers with unquantized adapters for example, merging defaults to upcast the layers leading to higher memory).	2025-10-16 16:06:23 +02:00
nemo	e263a1de13	Better documentation for CLI arguments	2025-10-16 14:39:43 +02:00
nemo	d39e7f0bc4	Log adapter config to WandB	2025-10-14 15:36:06 +02:00
nemo	20d4b5d347	Remove `use_peft` parameter from training script Instead we make the PEFT config optional which has the same effect.	2025-10-14 15:35:38 +02:00
nemo	02408fb244	Remove PEFT compatibility changes in config We'll wait for the PEFT release that fixes this for good.	2025-10-13 16:32:04 +02:00
nemo	390bfffa92	Merge remote-tracking branch 'hf/main' into feature/basic-peft-support	2025-10-09 11:30:55 +02:00
nemo	8a9be85306	Fix failing tests	2025-07-14 16:45:11 +02:00
nemo	57014e5bb1	Merge branch 'main' of github.com:huggingface/lerobot into feature/basic-peft-support	2025-07-08 11:23:40 +02:00
pre-commit-ci[bot]	354d118407	[pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci	2025-06-30 13:40:19 +00:00
nemo	72d02feef4	Formatting	2025-06-30 15:33:51 +02:00
nemo	4788434d9f	Support targets like `all-linear`	2025-06-30 15:33:13 +02:00
nemo	56e7979cfd	Add default config for ACT	2025-06-27 19:27:08 +02:00
nemo	44245e714e	Merge branch 'main' of github.com:huggingface/lerobot into feature/basic-peft-support	2025-06-27 13:55:07 +02:00
nemo	dc67b2ff3f	Store policy config alongside PEFT checkpoint Before this change the PEFT-wrapped policy did not save the policy's config alongside the adapter config / weights which prevented us from changing the policy config. Now the policy config is saved both in full training and PEFT training. This change makes loading the PEFT policy adapter much easier as well.	2025-06-22 19:54:10 +02:00
nemo	7fd8b4c773	Implement loading of PEFT adapters Loading a PEFT adapter is currently done by initializing a policy with default config and then applying the adapter on the resulting model. This has the obvious drawback that any configurations done during training are not applied in the adapted model. Currently the `use_peft` attribute of `PreTrainedConfig` is only set during loading to signal the following code that it has to deal with a PEFT adapter. However we could imagine a scenario where this is already set at training time and stored alongside the adapter.	2025-06-22 19:10:10 +02:00
nemo	98856662c1	Add basic support for PEFT adapter methods This changes adds support for training policies with much less parameters by applying adapter methods such as LoRA on specific parts of the policies and therefore possibly higher learning rates / batch sizes. To make this as accessible as possible I thought it useful to provide defaults for `target_modules` and `modules_to_save`. Currently only SmolVLA has such defaults but when we agree that this change is useful I will set out to generate more such defaults. While the user can override these settings, they are expected to only change the peft_method, rank and init_type parameters.	2025-06-22 13:45:07 +02:00