DTRT: 物理的人間-ロボット協調のための人間意図推定と役割分担の強化
DTRT: Enhancing Human Intent Estimation and Role Allocation for Physical Human-Robot Collaboration
人間の動作と力を用いて意図変化を捉え、軌道予測とロボットの役割分担を動的に調整する二重Transformerベースの手法を提案。
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著者: Haotian Liu, Yuchuang Tong, Zhengtao Zhang
分類: cs.RO, cs.AI
原文アブストラクト
In physical Human-Robot Collaboration (pHRC), accurate human intent estimation and rational human-robot role allocation are crucial for safe and efficient assistance. Existing methods that rely on short-term motion data for intention estimation lack multi-step prediction capabilities, hindering their ability to sense intent changes and adjust human-robot assignments autonomously, resulting in potential discrepancies. To address these issues, we propose a Dual Transformer-based Robot Trajectron (DTRT) featuring a hierarchical architecture, which harnesses human-guided motion and force data to rapidly capture human intent changes, enabling accurate trajectory predictions and dynamic robot behavior adjustments for effective collaboration. Specifically, human intent estimation in DTRT uses two Transformer-based Conditional Variational Autoencoders (CVAEs), incorporating robot motion data in obstacle-free case with human-guided trajectory and force for obstacle avoidance. Additionally, Differential Cooperative Game Theory (DCGT) is employed to synthesize predictions based on human-applied forces, ensuring robot behavior align with human intention. Compared to state-of-the-art (SOTA) methods, DTRT incorporates human dynamics into long-term prediction, providing an accurate understanding of intention and enabling rational role allocation, achieving robot autonomy and maneuverability. Experiments demonstrate DTRT's accurate intent estimation and superior collaboration performance.
関連論文
- フィッツのリスト再考：2エージェント物理的人間-ロボット協調位置/力タスクにおける機能配分の実証研究物理的人間-ロボット協調
- 骨格に基づく意図予測を用いた重量物の協調押し作業物理的人間-ロボット協調
