pipeline_tag: robotics
AnyPos: Automated Task-Agnostic Actions for Bimanual Manipulation
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.
- Paper: AnyPos: Automated Task-Agnostic Actions for Bimanual Manipulation
- Project Page: https://embodiedfoundation.github.io/vidar_anypos
- Repository: https://github.com/yaofeng1998/Vidar
Introduction
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.
Environment Setup
The code has been tested with CUDA 12.4.
1. Create and Activate Conda Environment
conda create -n vidar python==3.11.9
conda activate vidar
2. Install Dependencies
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
Usage
The AnyPos pipeline consists of a Video Diffusion Model and a Masked Inverse Dynamic Model.
Video Diffusion Model (Vidar) Inference
To generate a video based on an initial frame and a text instruction:
scripts/vm/sample.sh
Masked Inverse Dynamic Model Inference
To evaluate the inverse dynamic model:
scripts/idm/eval.sh
Citation
@article{anypos2025,
title={AnyPos: Automated Task-Agnostic Actions for Bimanual Manipulation},
author={Yao, Feng and others},
journal={arXiv preprint arXiv:2507.12768},
year={2025}
}