The repository contains the released checkpoint series, resolved configuration,
and dataset statistics. Use the BARX release scripts rather than loading the
checkpoint as a generic text-generation model: action tokenization,
normalization, image preprocessing, and representation prompting are part of
the public policy code.
The BARX MiniVLA policy combines Qwen2.5-0.5B with DINOv2 and SigLIP vision
features through MiniVLA/OpenVLA-derived policy code. This checkpoint was
trained in RoboCasa-X using the CC BY 4.0 dataset linked above. Source
embodiments are IIWA, Kinova3, and UR5e; Panda, Panda-OG, and Jaco are target
embodiments evaluated after adaptation.
Exact training aliases, selected paper checkpoints, adaptation commands, and
evaluation conditions are documented in the
BARX release repository.
This artifact is intended for simulation robotics research, BARX reproduction,
and cross-embodiment adaptation. It has not been validated for real-world robot
control or safety-critical use. Short public smoke tests establish
executability, not the paper's full success rates or guarantees on unseen
robots, scenes, or objects.
This BARX model artifact is released under
Apache 2.0. Preserve attribution
for Qwen2.5, DINOv2, SigLIP, MiniVLA/OpenVLA, RoboCasa/RoboCasa-X, the training
dataset, and BARX. Separately applicable upstream licenses are not replaced by
this artifact license.
1@inproceedings{sridhar2026barx,
2 title = {Cross-Embodiment Transfer via Behavior-Aligned Representations},
3 author = {Sridhar, Ajay and Gao, Jensen and Yang, Jonathan and Mercat, Jean and Belkhale, Suneel and Sadigh, Dorsa},
4 booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
5 year = {2026}
6}
