Back to the Manifold: Recovering from Out-of-Distribution States
Alfredo Reichlin,Giovanni Luca Marchetti,Hang Yin,Ali Ghadirzadeh,Danica Kragic,Alfredo Reichlin,Giovanni Luca Marchetti,Hang Yin,Ali Ghadirzadeh,Danica Kragic
Learning from previously collected datasets of expert data offers the promise of acquiring robotic policies without unsafe and costly online explorations. However, a major challenge is a distributional shift between the states in the training dataset and the ones visited by the learned policy at the test time. While prior works mainly studied the distribution shift caused by the policy during the ...
