Adaptive Interactive Navigation of Quadruped Robots using Large Language Models
This addresses navigation challenges in complex settings like disaster zones or warehouses, offering an incremental improvement over traditional methods.
The paper tackles robotic navigation in environments without clear paths by proposing an adaptive interactive approach that uses large language models for task planning and reinforcement learning for skill execution, demonstrating effectiveness in diverse scenarios.
Robotic navigation in complex environments remains a critical research challenge. Traditional navigation methods focus on optimal trajectory generation within free space, struggling in environments lacking viable paths to the goal, such as disaster zones or cluttered warehouses. To address this gap, we propose an adaptive interactive navigation approach that proactively interacts with environments to create feasible paths to reach originally unavailable goals. Specifically, we present a primitive tree for task planning with large language models (LLMs), facilitating effective reasoning to determine interaction objects and sequences. To ensure robust subtask execution, we adopt reinforcement learning to pre-train a comprehensive skill library containing versatile locomotion and interaction behaviors for motion planning. Furthermore, we introduce an adaptive replanning method featuring two LLM-based modules: an advisor serving as a flexible replanning trigger and an arborist for autonomous plan adjustment. Integrated with the tree structure, the replanning mechanism allows for convenient node addition and pruning, enabling rapid plan modification in unknown environments. Comprehensive simulations and experiments have demonstrated our method's effectiveness and adaptivity in diverse scenarios. The supplementary video is available at page: https://youtu.be/W5ttPnSap2g.