[Submitted on 21 Sep 2026]
Title:Capability-Aware Arbitration for Semantic Intent-Based Shared Control
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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.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2609.25369 [cs.RO]
(or arXiv:2609.25369v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.25369
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Zhaoda Du [view email] [v1] Mon, 21 Sep 2026 20:08:39 UTC (7,357 KB)
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