SPIRIT: 深層学習の不確実性下での堅牢なロボット操作のための知覚共有自律
SPIRIT: Perceptive Shared Autonomy for Robust Robotic Manipulation under Deep Learning Uncertainty
深層学習による知覚の不確実性に応じて自律度を調整する「知覚共有自律」を提案し、不確実な時は遠隔操作に切り替えることで、信頼性の高い操作を実現した。
詳しい要約
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著者: Jongseok Lee, Ribin Balachandran, Harsimran Singh, Jianxiang Feng, Hrishik Mishra, Marco De Stefano, Rudolph Triebel, Alin Albu-Schaeffer, Konstantin Kondak
分類: cs.RO, cs.AI
原文アブストラクト
Deep learning (DL) has enabled impressive advances in robotic perception, yet its limited robustness and lack of interpretability hinder reliable deployment in safety critical applications. We propose a concept termed perceptive shared autonomy, in which uncertainty estimates from DL based perception are used to regulate the level of autonomy. Specifically, when the robot's perception is confident, semi-autonomous manipulation is enabled to improve performance; when uncertainty increases, control transitions to haptic teleoperation for maintaining robustness. In this way, high-performing but uninterpretable DL methods can be integrated safely into robotic systems. A key technical enabler is an uncertainty aware DL based point cloud registration approach based on the so called Neural Tangent Kernels (NTK). We evaluate perceptive shared autonomy on challenging aerial manipulation tasks through a user study of 15 participants and realization of mock-up industrial scenarios, demonstrating reliable robotic manipulation despite failures in DL based perception. The resulting system, named SPIRIT, improves both manipulation performance and system reliability. SPIRIT was selected as a finalist of a major industrial innovation award.
関連論文
- 意図推定に基づくモデル予測制御による遠隔操作の共有自律制御共有自律/遠隔操作
- インピーダンス駆動の異方性誘導場による共有自律支援共有自律/遠隔操作
