用深度逆强化学习让机器人隐式学会社交导航，自动避让人群不堵路。
Learning Implicit Social Navigation Behavior using Deep Inverse Reinforcement Learning
- 基于少量示范数据，通过改进的逆强化学习算法推断社交成本图
- 在逼真仿真中实现机器人自动避让、不堵死，表现优于传统规则和ORCA模型
- 适合做智能机器人导航的团队，尤其关注人机共融场景
本文提出一种平滑最大熵深度逆强化学习（S-MEDIRL）算法，用于在动态环境中学习社交导航的奖励地图。该方法利用人类轨迹与场景几何信息，使机器人能实时推理路径。传统规则方法难以覆盖所有交互情况，而本方法仅需少量示范即可外推，有效编码场景可通行性。机器人通过预测成本图生成轨迹，并由局部人群导航控制器执行。在高保真仿真中，机器人在狭窄通道场景下展现出自然避让对向人流、避免死锁等社交行为。实验对比显示，该方法优于主流模型基方法ORCA及基于规则的避让代理。
原文摘要 · Abstract (English)
This paper reports on learning a reward map for social navigation in dynamic environments where the robot can reason about its path at any time, given agents' trajectories and scene geometry. Humans navigating in dense and dynamic indoor environments often work with several implied social rules. A rule-based approach fails to model all possible interactions between humans, robots, and scenes. We propose a novel Smooth Maximum Entropy Deep Inverse Reinforcement Learning (S-MEDIRL) algorithm that can extrapolate beyond expert demos to better encode scene navigability from few-shot demonstrations. The agent learns to predict the cost maps reasoning on trajectory data and scene geometry. The agent samples a trajectory that is then executed using a local crowd navigation controller. We present results in a photo-realistic simulation environment, with a robot and a human navigating a narrow crossing scenario. The robot implicitly learns to exhibit social behaviors such as yielding to oncoming traffic and avoiding deadlocks. We compare the proposed approach to the popular model-based crowd navigation algorithm ORCA and a rule-based agent that exhibits yielding.
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