Legible Shared Autonomy: Implicit Communication of Robot Belief through Motion

2026-06-29

AI Summary

This work addresses the limitations of traditional shared autonomy systems, where the robot’s internal belief about user intent remains opaque, often leading to ambiguous assistance, redundant control, or delayed error correction. To overcome these issues, the authors propose a novel framework that integrates legible motion planning with confidence-driven dynamic authority allocation. Specifically, the robot explicitly communicates its inferred goal through legible trajectories while adaptively adjusting control authority based on its confidence in the estimated intent. This approach uniquely combines legibility-aware motion generation with real-time, belief-confidence-based authority modulation, enhancing intent transparency without compromising task efficiency. User studies demonstrate that, compared to conventional methods, the proposed system significantly improves users’ understanding of the robot’s beliefs and effectively reduces their cognitive and physical control burden.

Abstract

Shared autonomy systems combine user input with autonomous assistance to help users with motor impairments control robot arms to perform everyday manipulation tasks, by inferring user goals and providing appropriate guidance. However, the robot's internal beliefs about user goals cannot be observed by users. Traditional shared autonomy systems provide assistance along efficient shortest paths toward inferred goals, but when multiple objects lie in similar directions, such assistive motion remains ambiguous and fails to reveal the specific goal identified by the robot. This creates two critical problems. First, when the robot correctly infers the goal, users continue controlling because they cannot perceive understanding from ambiguous assistive motion, wasting effort when autonomous completion would suffice. Second, when the robot misunderstands intent, users cannot quickly detect errors until assistive motion diverges significantly, requiring substantial corrective input. We address this by introducing legible motion into shared autonomy, where robot actions must both advance toward the goal and clearly reveal which goal has been inferred, enabling users to understand the robot's beliefs and adjust control accordingly. The robot modulates communication strength through confidence-aware adaptive authority allocation by providing assertive legible assistive actions when confident while increasing user authority when uncertain, transforming shared autonomy into transparent bidirectional collaboration. User studies including simulation and physical experiments with a six-degree-of-freedom robot arm demonstrate that legible shared autonomy significantly improves users' understanding of robot beliefs and reduces user control effort compared to standard shared autonomy.

Problem

• shared autonomy
• legible motion
• robot belief
• user intent
• assistive ambiguity

Innovation

• legible motion
• shared autonomy
• implicit communication
• adaptive authority allocation
• human-robot collaboration