動的な人とロボットの物体協調操作のための制約を考慮した意図推定
Constraint-Aware Intent Estimation for Dynamic Human-Robot Object Co-Manipulation
人間の意図を動的システムモデルとパーティクルフィルタでリアルタイム推定し、可変インピーダンス制御で協調操作を支援する枠組みを提案した。
著者: Yifei Simon Shao, Tianyu Li, Shafagh Keyvanian, Pratik Chaudhari, Vijay Kumar, Nadia Figueroa
分類: cs.RO
原文アブストラクト
Constraint-aware estimation of human intent is essential for robots to physically collaborate and interact with humans. Further, to achieve fluid collaboration in dynamic tasks intent estimation should be achieved in real-time. In this paper, we present a framework that combines online estimation and control to facilitate robots in interpreting human intentions, and dynamically adjust their actions to assist in dynamic object co-manipulation tasks while considering both robot and human constraints. Central to our approach is the adoption of a Dynamic Systems (DS) model to represent human intent. Such a low-dimensional parameterized model, along with human manipulability and robot kinematic constraints, enables us to predict intent using a particle filter solely based on past motion data and tracking errors. For safe assistive control, we propose a variable impedance controller that adapts the robot's impedance to offer assistance based on the intent estimation confidence from the DS particle filter. We validate our framework on a challenging real-world human-robot co-manipulation task and present promising results over baselines. Our framework represents a significant step forward in physical human-robot collaboration (pHRC), ensuring that robot cooperative interactions with humans are both feasible and effective.
