通过示范学习人类对物体摆放的空间规则，提升机器人理解能力。
Inference of Human-derived Specifications of Object Placement via Demonstration
- 基于区域连接演算构建位置增强的逻辑框架PARCC
- 从人类示范中推断出符合意图的物体布局规范
- 实验证明示范学习优于人工提供规则
随着机器人在抓取与放置任务（如物品打包、分类和套件组装）中操作能力的提升，如何有效捕捉人类感知中重要的空间关系仍缺乏充分表达的方法。为推进机器人对人类物体摆放规则的理解，本文提出位置增强的区域连接演算（PARCC），一种基于区域连接演算（RCC）的形式化逻辑框架，用于描述物体间的相对空间位置。同时，设计了一种通过示范学习PARCC规范的推断算法。最后，通过一项人机实验验证了该框架能准确捕捉人类意图，并表明基于示范的学习方法在效果上优于直接获取人工提供的规则。
原文摘要 · Abstract (English)
As robots' manipulation capabilities improve for pick-and-place tasks (e.g., object packing, sorting, and kitting), methods focused on understanding human-acceptable object configurations remain limited expressively with regard to capturing spatial relationships important to humans. To advance robotic understanding of human rules for object arrangement, we introduce positionally-augmented RCC (PARCC), a formal logic framework based on region connection calculus (RCC) for describing the relative position of objects in space. Additionally, we introduce an inference algorithm for learning PARCC specifications via demonstrations. Finally, we present the results from a human study, which demonstrate our framework's ability to capture a human's intended specification and the benefits of learning from demonstration approaches over human-provided specifications.
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