NaviSTAR: Socially Aware Robot Navigation with Hybrid Spatio-Temporal Graph Transformer and Preference Learning

Abstract

Developing robotic technologies for use in human society requires ensuring the safety of robots' navigation behaviors while adhering to pedestrians' expectations and social norms. However, understanding complex human-robot interactions (HRI) to infer potential cooperation and response among robots and pedestrians for cooperative collision avoid-ance is challenging. To address these challenges, we propose a novel socially-aware navigation benchmark called NaviS Tar, which utilizes a hybrid Spatio- Temporal grAph tRansformer to understand interactions in human-rich environments fusing crowd multi-modal dynamic features. We leverage an off-policy reinforcement learning algorithm with preference learning to train a policy and a reward function network with supervi-sor guidance. Additionally, we design a social score function to evaluate the overall performance of social navigation. To compare, we train and test our algorithm with other state-of-the-art methods in both simulator and real-world scenarios independently. Our results show that NaviSTAR outperforms previous methods with outstanding performance <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> The source code and experiment videos of this work are available at: https://sites.google.com/view/san-navistar