让机器人在安全约束下高效个性化，减少用户交互次数。
Coloring Between the Lines: Personalization in the Null Space of Planning Constraints
- 利用规划约束的零空间实现安全前提下的行为个性化。
- 仅需少量交互即可完成有效适应，无需重置环境。
- 适合长期使用、需持续优化的智能机器人系统。
通用机器人需在真实场景中持续个性化以满足长期用户的多样化需求与偏好。如何在不牺牲安全性和能力的前提下实现灵活个性化？本文提出「着色于线间」（CBTL）方法，通过挖掘规划约束问题（CSP）的零空间实现个性化。CBTL首先生成确保安全与能力的CSP，再通过在线交互逐步学习参数化约束。借助不确定性量化与约束的组合性，实现无需环境重置的样本高效适应。我们在三种仿真环境、一个基于网络的用户研究及一台真实机器人辅助进食系统中评估了CBTL，结果表明其在更少交互下始终优于基线，实现更有效的个性化。实验验证了CBTL在持续、灵活、主动且安全的机器人个性化方面具有统一而实用的优势。
原文摘要 · Abstract (English)
Generalist robots must personalize in-the-wild to meet the diverse needs and preferences of long-term users. How can we enable flexible personalization without sacrificing safety or competency? This paper proposes Coloring Between the Lines (CBTL), a method for personalization that exploits the null space of constraint satisfaction problems (CSPs) used in robot planning. CBTL begins with a CSP generator that ensures safe and competent behavior, then incrementally personalizes behavior by learning parameterized constraints from online interaction. By quantifying uncertainty and leveraging the compositionality of planning constraints, CBTL achieves sample-efficient adaptation without environment resets. We evaluate CBTL in (1) three diverse simulation environments; (2) a web-based user study; and (3) a real-robot assisted feeding system, finding that CBTL consistently achieves more effective personalization with fewer interactions than baselines. Our results demonstrate that CBTL provides a unified and practical approach for continual, flexible, active, and safe robot personalization. Website: https://emprise.cs.cornell.edu/cbtl/
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