考虑视觉受限情况，让机器人更好理解人类的避障行为。
Understanding and Imitating Human-Robot Motion with Restricted Visual Fields
- 分离感知与运动策略，建模人类有限视野和视距
- 实验证明可降低碰撞率，提升导航成功率
- 适用于人机协同场景中的实时避障系统
当与人类等其他智能体协作时，需建模其感知能力以预测和理解其行为。本文研究感知能力受视野范围、观测距离限制，且可能遗漏目标的情况。通过将感知能力与运动策略独立建模，我们证明：通过推理他人感知局限性，能更准确解释其行为。在用户实验中，人类操作者在复杂环境中以有限视野扫描障碍物。结果显示，若机器人考虑人类的有限观测空间，能更准确学习其导航策略，实现与动态/静态障碍物的低碰撞交互。该模型还成功部署于真实硬件车辆，支持实时运行。代码已开源。
原文摘要 · Abstract (English)
When working around other agents such as humans, it is important to model their perception capabilities to predict and make sense of their behavior. In this work, we consider agents whose perception capabilities are determined by their limited field of view, viewing range, and the potential to miss objects within their viewing range. By considering the perception capabilities and observation model of agents independently from their motion policy, we show that we can better predict the agents' behavior; i.e., by reasoning about the perception capabilities of other agents, one can better make sense of their actions. We perform a user study where human operators navigate a cluttered scene while scanning the region for obstacles with a limited field of view and range. We show that by reasoning about the limited observation space of humans, a robot can better learn a human's strategy for navigating an environment and navigate with minimal collision with dynamic and static obstacles. We also show that this learned model helps it successfully navigate a physical hardware vehicle in real-time. Code available at https://github.com/labicon/HRMotion-RestrictedView.
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