SAPS: Shared Autonomy for Policy Steering by Blending Teleoperation with a Pretrained VLA

Practical Tips

Practical tips and how-to guidance will be added by our editorial team.

中文解读

中文解读待补充：本站将优先为睡眠改善、失眠治疗、助眠方法等高价值文章补充中文说明。

Article Summary

Recent advancements in Vision-Language-Action (VLA) models have demonstrated impressive generalist capabilities in robot manipulation, yet these policies can be brittle under out-of-distribution spatial and semantic perturbations. While human teleoperation offers reliable recovery, it can demand high cognitive load and precise manual control, and existing policy steering methods often require auxiliary models or sampler modifications. In this work, we introduce Shared Autonomy for Policy Steering (SAPS), a framework that blends real-time human teleoperation commands with pretrained policy actions at the action level. SAPS requires no policy retraining, auxiliary dynamics models, or architectural modifications. We propose and evaluate three arbitration strategies to balance human and VLA policy control, including a dynamic Cosine-similarity arbitration strategy that computes the geometric agreement between human and policy actions. Across evaluations in simulation (LIBERO, LIBERO-PRO, CALVIN) and on real-world robot hardware, SAPS improves task success rates over autonomous execution by up to 82% in both simulation and the real world. Furthermore, our approach drastically reduces human intervention compared to pure teleoperation, while simultaneously achieving faster task completion times than both autonomous execution and pure teleoperation. These results demonstrate that action-level shared autonomy is a practical, model-agnostic approach for reliably deploying generalist robot policies in real-world contexts involving a human operator,with promising applications in assistive teleoperation and scalable data collection.

Sources & References

• Original source
• Full article (PDF)

Comments