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cs.LG2025
Learning a Pessimistic Reward Model in RLHF
Yinglun Xu, Hangoo Kang, Tarun Suresh +2
This work proposes `PET', a novel pessimistic reward fine-tuning method, to learn a pessimistic reward model robust against reward hacking in offline reinforcement learning from hu…
cs.RO2025
FetchBot: Learning Generalizable Object Fetching in Cluttered Scenes via Zero-Shot Sim2Real
Weiheng Liu, Yuxuan Wan, Jilong Wang +7
Generalizable object fetching in cluttered scenes remains a fundamental and application-critical challenge in embodied AI. Closely packed objects cause inevitable occlusions, makin…