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
Intent-Informed Model Predictive Control for Shared Autonomy in Remote Manipulation Tasks
視線とロボットアーム動作から動的ベイズネットワークで人間の意図を推定し、その信念分布のエントロピーに応じて自律度を連続的に調整するMPCベースの共有自律手法を提案した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Lorena Maria Genua, Camilla Tabasso, Mya Schroder, Joshua Migdal, Tye Brady, Taskin Padir
分類: cs.RO
原文アブストラクト
Shared autonomy is a control paradigm that combines human cognitive abilities with robot precision to enable effective remote manipulation in complex environments. A key challenge is how to adapt the level of autonomy using information extracted from the human, task, and/or environment. In this paper, we propose a novel arbitration strategy that dynamically adjusts the level of autonomy in the system using the entropy of a belief distribution derived from a multimodal intent inference. Human intent is modeled with a Dynamic Bayesian Network (DBN), which updates beliefs over goals given real-time operator gaze data and robot arm motion during teleoperation. We then formulate the blending of human input and autonomous control as a Model Predictive Control (MPC) problem, where the system's autonomous behavior is guided by the belief distribution. This approach enables continuous, gradual transitions across the full spectrum of autonomy levels, from direct teleoperation to full autonomy. To validate our approach, we conducted experiments to compare the performance of our proposed algorithm against two MPC-based shared autonomy methods. Compared to the baselines, our approach achieved reductions of at least 61% in object retrieval time, 44% in trajectory length, and 60% in human control input. Participants reported high perceived control, predictability, intent recognition, and responsiveness, along with low workload.
関連論文
- 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
- 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共有自律/遠隔操作
