3 papers
cs.RO2026
Revisiting Replanning from Scratch: Real-Time Incremental Planning with Fast Almost-Surely Asymptotically Optimal Planners
Mitchell E. C. Sabbadini, Andrew H. Liu, Joseph Ruan +3
Robots operating in changing environments either predict obstacle changes and/or plan quickly enough to react to them. Predictive approaches require a strong prior about the positi…
cs.RO2026
CDE: Concept-Driven Exploration for Reinforcement Learning
Le Mao, Andrew H. Liu, Renos Zabounidis +3
Intelligent exploration remains a critical challenge in reinforcement learning (RL), especially in visual control tasks. Unlike low-dimensional state-based RL, visual RL must extra…
cs.RO2025
Parallel Heuristic Search as Inference for Actor-Critic Reinforcement Learning Models
Hanlan Yang, Itamar Mishani, Luca Pivetti +2
Actor-Critic models are a class of model-free deep reinforcement learning (RL) algorithms that have demonstrated effectiveness across various robot learning tasks. While considerab…