用事件相机+强化学习实现人机共处的实时避障导航
Human-Robot Navigation using Event-based Cameras and Reinforcement Learning
- 通过事件相机异步捕捉视觉信息，灵活处理时间间隔
- 在仿真中实现稳定跟人与障碍物避让，响应延迟更低
- 适合需要低延迟感知的机器人交互场景
本文提出一种结合事件相机与其他传感器的机器人导航控制器，利用强化学习实现实时人机协同导航与避障。与传统基于图像的控制器（固定采样率、易受运动模糊和延迟影响）不同，该方法利用事件相机的异步特性，在可变时间间隔内处理视觉信息，支持自适应推理与控制。框架整合了事件感知、额外测距传感及基于深度确定性策略梯度的策略优化，并引入初始模仿学习以提升样本效率。在仿真环境中取得良好效果，验证了导航稳定性、行人跟随能力与避障性能。演示视频见项目网站。
原文摘要 · Abstract (English)
This work introduces a robot navigation controller that combines event cameras and other sensors with reinforcement learning to enable real-time human-centered navigation and obstacle avoidance. Unlike conventional image-based controllers, which operate at fixed rates and suffer from motion blur and latency, this approach leverages the asynchronous nature of event cameras to process visual information over flexible time intervals, enabling adaptive inference and control. The framework integrates event-based perception, additional range sensing, and policy optimization via Deep Deterministic Policy Gradient, with an initial imitation learning phase to improve sample efficiency. Promising results are achieved in simulated environments, demonstrating robust navigation, pedestrian following, and obstacle avoidance. A demo video is available at the project website.
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