Proposes a multi-phase imitation learning framework using supplementary demonstrations with a discriminator offline and self-supervised adaptation online to mitigate distribution shift in robotic control, claiming superior robustness in MuJoCo evaluations.
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How to Mitigate the Distribution Shift Problem in Robotics Control: A Robust and Adaptive Approach Based on Offline to Online Imitation Learning
Proposes a multi-phase imitation learning framework using supplementary demonstrations with a discriminator offline and self-supervised adaptation online to mitigate distribution shift in robotic control, claiming superior robustness in MuJoCo evaluations.