让机器人持续学习人类习惯，实现长期协作。
COOPERA: Continual Open-Ended Human-Robot Assistance
- 用心理特质模拟人类，动态调整机器人行为
- 首次支持跨任务、多时长的开放协作研究
- 适合研究长期人机交互的学者与工程师
为理解并协同人类，机器人需随时间适应个体特征、习惯与行为。然而多数机器人助手仅关注预设任务，缺乏学习人类模型的能力。本文提出COOPERA框架，实现持续、开放式的人-机器人协作。通过引入具有心理特质和长期意图的模拟人类，在复杂环境中与机器人互动，首次支持在不同任务和时间尺度下开展长期、开放的人机协作研究。框架包含基准测试与个性化方法，通过学习人类特质与上下文意图，优化机器人协作行为。实验验证了模拟人类行为的真实性，并证明推断与个性化人类意图对开放、长期协作的重要价值。
原文摘要 · Abstract (English)
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/
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