Capability-Aware Arbitration for Semantic Intent-Based Shared Control

2026-09-21

AI Summary

该研究提出一种能力感知的共享控制框架，通过结合视觉-语言模型和视觉-语言-动作策略来调整机器人权限，以解决过度辅助问题，提高任务成功率。

Abstract

Shared control often allocates robot authority based on confidence in inferred human intent, assuming reliable autonomous execution. When this assumption fails, high intent confidence can cause over-helping. We present a capability-aware shared-control framework in which a vision-language model (VLM) infers human intent and provides semantic-intent confidence, while a vision-language-action (VLA) policy generates autonomous actions. VLA capability confidence is estimated online from the dispersion and local instability of stochastic action trajectories. We design a nonlinear arbitration policy that combines Bayesian-filtered semantic-intent confidence with VLA capability confidence through a sigmoid mapping to adapt robot authority. Our evaluation combined VLM/VLA confidence assessment with a study involving 12 participants performing pick-and-place and bidirectional stacking under in-distribution and out-of-distribution conditions. The proposed method achieved the highest task success rate (92%), compared with manual teleoperation (83%), intent-only arbitration (44%), and fixed equal-weight blending (10%). It also achieved higher control friendliness and lower authority-weighted disagreement than both shared-control baselines. These results demonstrate the benefit of incorporating VLA capability into authority allocation to mitigate over-helping and improve shared-control performance.

Problem

• shared control
• human intent
• autonomous execution
• over-helping
• capability-aware

Innovation

• capability-aware shared control
• visual-language model
• visual-language-action policy
• nonlinear arbitration policy
• Bayesian-filtered semantic-intent confidence