基于大语言模型的双向自然语言对话增强的社会感知机器人导航
Socially-Aware Robot Navigation Enhanced by Bidirectional Natural Language Conversations Using Large Language Models
AI总结：
针对传统社会感知导航缺乏真正人机交互的问题，提出融合大语言模型的HSAC-LLM框架，通过双向自然语言交互实现主动避障，在多场景实验中性能优于现有最优DRL方法，推动动态场景人机交互发展。
摘要：
机器人导航在众多领域都至关重要，但传统方法聚焦于效率与避障，往往忽视了共享空间中的人类行为。随着服务机器人的兴起，社会感知导航日益受到关注。然而现有方法主要预测行人移动或发出警示，缺乏真正的人机交互。我们提出了融合大语言模型的混合软演员-评论家（Hybrid Soft Actor-Critic with Large Language Model, HSAC-LLM），这是一种新型社会感知导航框架。HSAC-LLM将深度强化学习与大语言模型相结合，支持双向自然语言交互，可同时预测连续与离散导航动作。当存在潜在碰撞风险时，机器人会主动与行人沟通以确定避障策略。在2D仿真、Gazebo以及真实环境中的实验表明，HSAC-LLM在交互、导航和避障方面均优于当前最优的深度强化学习（DRL）方法。该范式推动了动态场景下高效人机交互的发展。相关视频可访问https://hsacllm.github.io/查看。
英文摘要：
Robot navigation is crucial across various domains, yet traditional methods focus on efficiency and obstacle avoidance, often overlooking human behavior in shared spaces. With the rise of service robots, socially aware navigation has gained prominence. However, existing approaches primarily predict pedestrian movements or issue alerts, lacking true human-robot interaction. We introduce Hybrid Soft Actor-Critic with Large Language Model (HSAC-LLM), a novel framework for socially aware navigation. By integrating deep reinforcement learning with large language models, HSAC-LLM enables bidirectional natural language interactions, predicting both continuous and discrete navigation actions. When potential collisions arise, the robot proactively communicates with pedestrians to determine avoidance strategies. Experiments in 2D simulation, Gazebo, and real-world environments demonstrate that HSAC-LLM outperforms state-of-the-art DRL methods in interaction, navigation, and obstacle avoidance. This paradigm advances effective human-robot interactions in dynamic settings. Videos are available at https://hsacllm.github.io/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
