Unifying Large Language Model and Deep Reinforcement Learning for Human-in-Loop Interactive Socially-aware Navigation

Understand

Navigating human-filled spaces is crucial for the interactive social robots to support advanced services, such as cooperative carrying, which enables service provision in complex and crowded environments while adapting behavior based on real-time human language commands or feedback.

• However, existing social robot navigation planners face two major challenges: managing real-time user inputs and ensuring socially compliant behaviors in unfamiliar, zero-shot environments.
• In response, we introduce SALM, an interactive, human-in-loop Socially-Aware navigation Large Language Model framework that dynamically integrates deep reinforcement learning (DRL) with large language model (LLM) capabilities.
• SALM leverages contextual semantic understanding from real-time human-robot interactions to convert high-level user commands into precise, low-level control actions.