FARE：快速-缓慢代理机器人探索
FARE: Fast-Slow Agentic Robotic Exploration
AI总结：
FARE通过结合大型语言模型和强化学习策略，实现了高效且稳健的机器人探索，显著提升了探索效率并验证了其在大规模建筑环境中的有效性。
摘要：
本文通过将代理级语义推理与快速局部控制相结合，推动了自主机器人探索的发展。我们引入了FARE，一种分层自主探索框架，该框架整合了大型语言模型（LLM）用于全局推理和强化学习（RL）策略用于局部决策制定。FARE遵循快速-缓慢思考范式。缓慢思考的LLM模块解读对未知环境的简洁文本描述，并合成代理级探索策略，然后通过拓扑图转化为一系列全局航点。为了进一步提高推理效率，该模块采用基于模块化的修剪机制，以减少冗余的图结构。快速思考的RL模块通过响应局部观察并受LLM生成的全局航点引导来执行探索。此外，RL策略还通过一个奖励项来鼓励遵循全局航点，从而实现一致且稳健的闭环行为。这种架构将语义推理与几何决策解耦，使每个模块能够在其适当的时间和空间尺度上运行。在具有挑战性的模拟环境中，我们的结果表明，FARE在状态-of-the-art基线之上实现了显著的探索效率提升。我们进一步在硬件上部署FARE，并在复杂的大规模$200m×130m$建筑环境中验证了其有效性。
英文摘要：
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 local decision making. FARE follows a fast-slow thinking paradigm. The slow-thinking LLM module interprets a concise textual description of the unknown environment and synthesizes an agent-level exploration strategy, which is then grounded into a sequence of global waypoints through a topological graph. To further improve reasoning efficiency, this module employs a modularity-based pruning mechanism that reduces redundant graph structures. The fast-thinking RL module executes exploration by reacting to local observations while being guided by the LLM-generated global waypoints. The RL policy is additionally shaped by a reward term that encourages adherence to the global waypoints, enabling coherent and robust closed-loop behavior. This architecture decouples semantic reasoning from geometric decision, allowing each module to operate in its appropriate temporal and spatial scale. In challenging simulated environments, our results show that FARE achieves substantial improvements in exploration efficiency over state-of-the-art baselines. We further deploy FARE on hardware and validate it in complex, large scale $200m\times130m$ building environment.
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。
