Human-Following and -guiding in Crowded Environments using Semantic Deep-Reinforcement-Learning for Mobile Service Robots

TL;DR

This paper introduces a deep reinforcement learning approach for mobile service robots to follow and guide humans in crowded environments, leveraging semantic information to improve safety, robustness, and interaction quality.

Contribution

The novel integration of semantic information into deep reinforcement learning enables robots to navigate safely and interact effectively with humans in dynamic, crowded settings.

Findings

Enhanced navigational safety and robustness over benchmark methods

Improved human-robot interaction through adaptive behaviors

Successful learning of social and safety-aware navigation strategies

Abstract

Assistance robots have gained widespread attention in various industries such as logistics and human assistance. The tasks of guiding or following a human in a crowded environment such as airports or train stations to carry weight or goods is still an open problem. In these use cases, the robot is not only required to intelligently interact with humans, but also to navigate safely among crowds. Thus, especially highly dynamic environments pose a grand challenge due to the volatile behavior patterns and unpredictable movements of humans. In this paper, we propose a Deep-Reinforcement-Learning-based agent for human-guiding and -following tasks in crowded environments. Therefore, we incorporate semantic information to provide the agent with high-level information like the social states of humans, safety models, and class types. We evaluate our proposed approach against a benchmark approach without semantic information and demonstrated enhanced navigational safety and robustness. Moreover, we demonstrate that the agent could learn to adapt its behavior to humans, which improves the human-robot interaction significantly.