The authors introduced CriticHack, an evaluation framework that tracks how specific outcomes change when a robot policy is optimized against a learned visual reward.
Visual reward optimization can improve robots while reinforcing wrong-object mistakes
Robot policies increasingly completed the requested task, but also learned to manipulate distractors that the visual critic mistakenly rewarded.
Chinese Tech
Jiaxuan Luo · Xingguo Xu · Shanshan Wang · Yuhan Zhou · Zhen Zhang
Baidu · University of California, Santa Barbara · Johns Hopkins University · Dalian University of Technology · Rutgers University
Research Digest··2 min read
Luo et al.
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From Baidu and 4 others
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Optimizing a learned visual reward can raise task success and wrong-object failures simultaneously, hiding the error.
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