What Is My Robot Thinking? Design Considerations for Transparent and Trustworthy Shared Autonomy
For designers of assistive robots, this work provides guidelines on how to design transparent interfaces that enhance coordination and trust, showing that effective transparency improves goal legibility rather than requiring maximal disclosure.
This paper investigates how interface-level transparency (feedback modality and information richness) affects coordination and trust in shared autonomy systems. A user study with 25 participants showed that providing feedback significantly improves intent alignment and reduces corrective intervention, with visual feedback preferred over auditory, and preferences for information richness depending on task complexity.
Assistive robots operating under shared autonomy must balance user control with autonomous assistance. Because robot actions depend on internal intent inference that is not directly observable, mismatches between inferred and intended goals can undermine coordination and trust. We investigate how interface-level transparency, including feedback modality (visual vs. auditory) and information richness (sparse vs. rich), shapes interaction in a vision-based shared autonomy system. In a user study with N=25 participants across two assistive manipulation tasks, we evaluate how these designs influence coordination and trust. Providing feedback significantly improves intent alignment and reduces corrective intervention, indicating that making the inferred goal legible accelerates convergence in shared control. Participants preferred visual over auditory feedback, while preferences for sparse versus rich information depended on task complexity. We also found that revealing the full belief distribution did not consistently improve alignment or trust. Together, these findings indicate that effective transparency enhances coordination primarily through goal legibility, while trust depends on task-appropriate information exposure rather than maximal disclosure. Based on these results, we outline guidelines for designing transparent shared autonomy systems.