Enhancing Socially-Aware Robot Navigation through Bidirectional Natural Language Conversation

Robot navigation is an important research field with applications in various domains. However, traditional approaches often prioritize efficiency and obstacle avoidance, neglecting a nuanced understanding of human behavior or intent in shared spaces. With the rise of service robots, there's an increasing emphasis on endowing robots with the capability to navigate and interact in complex real-world environments. Socially aware navigation has recently become a key research area. However, existing work either predicts pedestrian movements or simply emits alert signals to pedestrians, falling short of facilitating genuine interactions between humans and robots. In this paper, we introduce the Hybrid Soft Actor-Critic with Large Language Model (HSAC-LLM), an innovative model designed for socially-aware navigation in robots. This model seamlessly integrates deep reinforcement learning with large language models, enabling it to predict both continuous and discrete actions for navigation. Notably, HSAC-LLM facilitates bidirectional interaction based on natural language with pedestrian models. When a potential collision with pedestrians is detected, the robot can initiate or respond to communications with pedestrians, obtaining and executing subsequent avoidance strategies. Experimental results in 2D simulation, the Gazebo environment, and the real-world environment demonstrate that HSAC-LLM not only efficiently enables interaction with humans but also exhibits superior performance in navigation and obstacle avoidance compared to state-of-the-art DRL algorithms. We believe this innovative paradigm opens up new avenues for effective and socially aware human-robot interactions in dynamic environments. Videos are available at https://hsacllm.github.io/.