让机器人学会排队等人的社交规范，提升人机共处自然度。
Learning Social Heuristics for Human-Aware Path Planning
- 用学习到的社会价值函数作为启发式，指导路径规划
- 在排队场景中实现符合社会规范的导航行为
- 适合研究人机交互与智能体社会性行为的学者
近年来，社交机器人导航成为研究热点。多数研究聚焦于避开障碍、保持人际距离并预测人类运动以优化路径。然而，真正被人类接纳的机器人还需掌握无法通过传统导航获得的社会规范，需专门的学习过程。本文提出基于学习社会价值的启发式规划（HPLSV），通过学习一个封装社交导航代价的价值函数，并将其作为启发式信息融入启发式搜索路径规划。本初步工作将方法应用于常见的排队场景，旨在未来扩展至更多人类活动。
原文摘要 · Abstract (English)
Social robotic navigation has been at the center of numerous studies in recent years. Most of the research has focused on driving the robotic agent along obstacle-free trajectories, respecting social distances from humans, and predicting their movements to optimize navigation. However, in order to really be socially accepted, the robots must be able to attain certain social norms that cannot arise from conventional navigation, but require a dedicated learning process. We propose Heuristic Planning with Learned Social Value (HPLSV), a method to learn a value function encapsulating the cost of social navigation, and use it as an additional heuristic in heuristic-search path planning. In this preliminary work, we apply the methodology to the common social scenario of joining a queue of people, with the intention of generalizing to further human activities.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。