4 papers
FARE: Fast-Slow Agentic Robotic Exploration
Shuhao Liao, Xuxin Lv, Jeric Lew +6
This work advances autonomous robot exploration by integrating agent-level semantic reasoning with fast local control. We introduce FARE, a hierarchical autonomous exploration fram…
GRATE: a Graph transformer-based deep Reinforcement learning Approach for Time-efficient autonomous robot Exploration
Haozhan Ni, Jingsong Liang, Chenyu He +2
Autonomous robot exploration (ARE) is the process of a robot autonomously navigating and mapping an unknown environment. Recent Reinforcement Learning (RL)-based approaches typical…
HDPlanner: Advancing Autonomous Deployments in Unknown Environments through Hierarchical Decision Networks
Jingsong Liang, Yuhong Cao, Yixiao Ma +2
In this paper, we introduce HDPlanner, a deep reinforcement learning (DRL) based framework designed to tackle two core and challenging tasks for mobile robots: autonomous explorati…
Privileged Reinforcement and Communication Learning for Distributed, Bandwidth-limited Multi-robot Exploration
Yixiao Ma, Jingsong Liang, Yuhong Cao +2
Communication bandwidth is an important consideration in multi-robot exploration, where information exchange among robots is critical. While existing methods typically aim to reduc…