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

Engineering notes

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为 VLA、具身智能、人形机器人控制、机器人操作等高价值论文补充中文说明。

Original abstract

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.

Links and sources

• Official / arXiv page
• PDF from original source

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