基于能力感知的语义意图共享控制仲裁
Capability-Aware Arbitration for Semantic Intent-Based Shared Control
- Colorado School of Mines（科罗拉多矿业大学）
- University of Pennsylvania（宾夕法尼亚大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出一种能力感知共享控制框架，利用VLM意图置信度与VLA能力置信度通过Sigmoid仲裁分配机器人权限，实验表明该方法在任务成功率（92%）和用户体验上优于基线，有效缓解过度帮助。
AI中文摘要：
共享控制通常根据对推断的人类意图的置信度来分配机器人权限，并假设自主执行是可靠的。当这一假设不成立时，高意图置信度可能导致过度帮助。我们提出了一种能力感知的共享控制框架，其中视觉语言模型（VLM）推断人类意图并提供语义意图置信度，而视觉语言动作（VLA）策略生成自主动作。VLA能力置信度通过随机动作轨迹的离散性和局部不稳定性在线估计。我们设计了一种非线性仲裁策略，通过Sigmoid映射将贝叶斯滤波后的语义意图置信度与VLA能力置信度相结合，以自适应调整机器人权限。我们的评估结合了VLM/VLA置信度评估，并开展了一项涉及12名参与者的研究，他们在分布内和分布外条件下执行拾取放置和双向堆叠任务。所提出的方法实现了最高的任务成功率（92%），相比之下，手动遥操作（83%）、仅意图仲裁（44%）和固定等权混合（10%）的成功率较低。该方法还比两种共享控制基线实现了更高的控制友好性和更低的权限加权不一致性。这些结果表明，将VLA能力纳入权限分配有助于缓解过度帮助并提升共享控制性能。
英文摘要：
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.
