COOPERA: 継続的でオープンエンドな人間-ロボット協働
COOPERA: Continual Open-Ended Human-Robot Assistance
心理特性と長期意図を持つシミュレーション人間とロボットが複雑環境で相互作用する枠組みを提案し、人間の特性や文脈依存の意図を学習して協働行動を個人適応させるベンチマークと手法を導入した。
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: Chenyang Ma, Kai Lu, Ruta Desai, Xavier Puig, Andrew Markham, Niki Trigoni
分類: cs.RO
原文アブストラクト
To understand and collaborate with humans, robots must account for individual human traits, habits, and activities over time. However, most robotic assistants lack these abilities, as they primarily focus on predefined tasks in structured environments and lack a human model to learn from. This work introduces COOPERA, a novel framework for COntinual, OPen-Ended human-Robot Assistance, where simulated humans, driven by psychological traits and long-term intentions, interact with robots in complex environments. By integrating continuous human feedback, our framework, for the first time, enables the study of long-term, open-ended human-robot collaboration (HRC) in different collaborative tasks across various time-scales. Within COOPERA, we introduce a benchmark and an approach to personalize the robot's collaborative actions by learning human traits and context-dependent intents. Experiments validate the extent to which our simulated humans reflect realistic human behaviors and demonstrate the value of inferring and personalizing to human intents for open-ended and long-term HRC. Project Page: https://dannymcy.github.io/coopera/
