E2HiL: Entropy-Guided Sample Selection for Efficient Real-World Human-in-the-Loop Reinforcement Learning
Published in IEEE Robotics and Automation Letters (RA-L), 2026
A human-in-the-loop reinforcement learning framework that actively selects informative samples via entropy guidance, sharply reducing the number of human interventions needed to learn real-world robotic manipulation.
Recommended citation: Haoyuan Deng, Yudong Lin, Yuanjiang Xue, Haoyang Du, Qianzhun Wang, Boyang Zhou, Zhenyu Wu, Ziwei Wang. (2026). "E2HiL: Entropy-Guided Sample Selection for Efficient Real-World Human-in-the-Loop Reinforcement Learning." IEEE Robotics and Automation Letters.
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