Human-Robot Navigation using Event-based Cameras and Reinforcement Learning

TL;DR

This paper presents a novel robot navigation system that uses event-based cameras combined with reinforcement learning to achieve real-time, adaptive human-centered navigation and obstacle avoidance, overcoming limitations of traditional image-based methods.

Contribution

It introduces an integrated framework utilizing event cameras and reinforcement learning, including imitation learning, for improved real-time robot navigation in dynamic environments.

Findings

Successful navigation and obstacle avoidance in simulation

Robust pedestrian following demonstrated

Enhanced sample efficiency through imitation learning

Abstract

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.