| --- |
| pipeline_tag: robotics |
| --- |
| |
| # AnyPos: Automated Task-Agnostic Actions for Bimanual Manipulation |
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| AnyPos (also referred to as **Vidar** in the codebase) is a unified pipeline that integrates large-scale automated task-agnostic exploration with robust embodiment modeling through inverse dynamics learning. By focusing on exploring all feasible actions of the embodiment, AnyPos learns embodiment dynamics directly from task-agnostic action data and decouples them from high-level policy learning. |
|
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| - **Paper:** [AnyPos: Automated Task-Agnostic Actions for Bimanual Manipulation](https://huggingface.co/papers/2507.12768) |
| - **Project Page:** [https://embodiedfoundation.github.io/vidar_anypos](https://embodiedfoundation.github.io/vidar_anypos) |
| - **Repository:** [https://github.com/yaofeng1998/Vidar](https://github.com/yaofeng1998/Vidar) |
|
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| ## Introduction |
|
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| AnyPos generates diverse yet safe trajectories at scale, then learns embodiment representations by decoupling arm and end-effector motions. It employs a direction-aware decoder to stabilize predictions under distribution shift, allowing it to be coupled with diverse high-level policy models. The system has demonstrated significant success rate improvements across tasks such as operating a microwave, folding clothes, and watering plants. |
|
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| ## Environment Setup |
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| The code has been tested with CUDA 12.4. |
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|
| ### 1. Create and Activate Conda Environment |
| ```bash |
| conda create -n vidar python==3.11.9 |
| conda activate vidar |
| ``` |
|
|
| ### 2. Install Dependencies |
| ```bash |
| conda install pytorch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 pytorch-cuda=12.4 -c pytorch -c nvidia |
| python -m pip install -r requirements.txt |
| python -m pip install ninja |
| python -m pip install git+https://github.com/Dao-AILab/flash-attention.git@v2.6.3 |
| python -m pip install xfuser==0.4.0 |
| ``` |
|
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| ## Usage |
|
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| The AnyPos pipeline consists of a Video Diffusion Model and a Masked Inverse Dynamic Model. |
|
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| ### Video Diffusion Model (Vidar) Inference |
| To generate a video based on an initial frame and a text instruction: |
| ```bash |
| scripts/vm/sample.sh |
| ``` |
|
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| ### Masked Inverse Dynamic Model Inference |
| To evaluate the inverse dynamic model: |
| ```bash |
| scripts/idm/eval.sh |
| ``` |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{anypos2025, |
| title={AnyPos: Automated Task-Agnostic Actions for Bimanual Manipulation}, |
| author={Yao, Feng and others}, |
| journal={arXiv preprint arXiv:2507.12768}, |
| year={2025} |
| } |
| ``` |
