Equivariant Transporter Network

Abstract

Transporter Net is a recently proposed framework for pick and place that is able to learn good manipulation policies from a very few expert demonstrations [35]. A key reason why Transporter Net is so sample efficient is that the model incorporates rotational equivariance into the pick-conditioned place module, i.e. the model immediately generalizes learned pick-place knowledge to objects presented in different pick orientations. This paper proposes a novel version of Transporter Net that is equivariant to both pick and place orientation. As a result, our model immediately generalizes pick-place knowledge to different place orientations in addition to generalizing the pick orientation as before. Ultimately, our new model is more sample efficient and achieves better pick and place success rates than the baseline Transporter Net model.