RLDG: Robotic Generalist Policy Distillation via Reinforcement Learning
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\reportnumber \correspondingauthor Jianlan Luo( jianlanluo@berkeley.edu ), Charles Xu( xuc@berkeley.edu ) RLDG: Robotic Generalist Policy Distillation via Reinforcement Learning Charles Xu Qiyang Li Department of EECS, UC Berkeley Jianlan Luo Sergey Levine Department of EECS, UC Berkeley Abstract Recent advances in robotic foundation models have enabled the development of generalist policies that can adapt to diverse tasks. While these models show impressive flexibility, their performance heavily depends on the quality of their training data. In this work, we propose Reinforcement Learning Dis
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