2 papers
cs.RO2025
MapExRL: Human-Inspired Indoor Exploration with Predicted Environment Context and Reinforcement Learning
Narek Harutyunyan, Brady Moon, Seungchan Kim +3
Path planning for robotic exploration is challenging, requiring reasoning over unknown spaces and anticipating future observations. Efficient exploration requires selecting budget-…
cs.RO2025
PIPE Planner: Pathwise Information Gain with Map Predictions for Indoor Robot Exploration
Seungjae Baek, Brady Moon, Seungchan Kim +4
Autonomous exploration in unknown environments requires estimating the information gain of an action to guide planning decisions. While prior approaches often compute information g…