让机器人通过分析人类合作意愿，智能判断何时沟通以安全通过迎面场景
Assessing Human Cooperation for Enhancing Social Robot Navigation
- 基于几何分析判断人类合作程度，决定是否沟通
- 提出可区分合作与非合作行为的评估指标
- 用几何推理生成适时的语音或动作响应，适合人机交互研究者
社交感知机器人导航是一种在人类环境中规划路径的范式，要求机器人在互动中遵循社会规范。现有策略虽借助人类轨迹预测提升表现，但在人类行为意外时仍易失效，因机器人难以理解人类意图与合作倾向，且人类也不清楚机器人的规划。本文针对这一问题，在迎面相遇场景中，基于上下文几何分析与人类合作性，提出一种适时有效沟通的方法。我们构建了评估框架与量化指标，可区分合作型与非合作型人类。进一步展示了如何利用几何推理生成恰当的语音提示或机器人动作，实现更自然的人机协作。
原文摘要 · Abstract (English)
Socially aware robot navigation is a planning paradigm where the robot navigates in human environments and tries to adhere to social constraints while interacting with the humans in the scene. These navigation strategies were further improved using human prediction models, where the robot takes the potential future trajectory of humans while computing its own. Though these strategies significantly improve the robot's behavior, it faces difficulties from time to time when the human behaves in an unexpected manner. This happens as the robot fails to understand human intentions and cooperativeness, and the human does not have a clear idea of what the robot is planning to do. In this paper, we aim to address this gap through effective communication at an appropriate time based on a geometric analysis of the context and human cooperativeness in head-on crossing scenarios. We provide an assessment methodology and propose some evaluation metrics that could distinguish a cooperative human from a non-cooperative one. Further, we also show how geometric reasoning can be used to generate appropriate verbal responses or robot actions.
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