FARE: Fast-Slow Agentic Robotic Exploration
Shuhao Liao, Xuxin Lv, Jeric Lew +6 more
This work advances autonomous robot exploration by integrating agent-level semantic reasoning with fast local control. We introduce FARE, a hierarchical autonomous exploration framework that integrates a large language model (LLM) for global reasoning with a reinforcement learning (RL) policy for lo...
large language modelreinforcement learningglobal reasoninglocal decision makingtopological graph+5 more
Jan 21, 20265