私のロボットは何を考えているのか？透明で信頼できる共有自律のための設計考察
What Is My Robot Thinking? Design Considerations for Transparent and Trustworthy Shared Autonomy
共有自律システムにおいて、ユーザーへのフィードバックの種類（視覚/聴覚）と情報量（簡潔/豊富）が協調と信頼に与える影響をユーザー研究で評価し、目標の明確化が調整を改善することを示した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Atharv Belsare, Zohre Karimi, Connor Mattson, Rushiil Nakka, Daniel S. Brown
分類: cs.RO
原文アブストラクト
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.
