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cs.RO2026
MAPL: Multi-Objective Preference Learning for Robot Locomotion
Xiyue Chen, Muhan Lin, Shuyang Shi +1
Reward design remains a major bottleneck in reinforcement learning for robot locomotion, where successful policies often depend on carefully tuned, task-specific reward functions.…
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…