让机器人通过对话理解行人意图，主动协商避让路径。
Socially-Aware Robot Navigation Enhanced by Bidirectional Natural Language Conversations Using Large Language Models
- 用大模型+强化学习实现双向语言交互导航
- 实测在仿真、模拟和真实环境均优于现有方法
- 适合服务机器人、人机共处场景研究者
机器人导航在多个领域至关重要，但传统方法仅关注效率与避障，常忽视共享空间中的人类行为。随着服务机器人的发展，社交感知导航日益重要。然而，现有方法多为预测行人运动或发出警告，缺乏真正的交互。本文提出混合软演员-评论家结合大语言模型（HSAC-LLM）的新框架，融合深度强化学习与大语言模型，支持双向自然语言交互，可同时预测连续与离散的导航动作。当存在潜在碰撞时，机器人会主动与行人沟通以确定避让策略。在2D仿真、Gazebo及真实环境中的实验表明，HSAC-LLM在交互性、导航性能和避障能力上均优于当前最优的深度强化学习方法。该范式推动了动态环境中有效人机交互的发展。视频演示见 https://hsacllm.github.io/。
原文摘要 · Abstract (English)
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/.
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