Equivariant Transporter Network
This work addresses sample efficiency in robotic manipulation, offering incremental improvements over existing methods.
The paper tackles the problem of improving sample efficiency and success rates in robotic pick-and-place tasks by extending the Transporter Net framework to be equivariant to both pick and place orientations, resulting in better performance than the baseline.
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. A key reason why Transporter Net is so sample efficient is that the model incorporates rotational equivariance into the pick module, i.e. the model immediately generalizes learned pick knowledge to objects presented in different 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 place knowledge to different place orientations in addition to generalizing pick knowledge as before. Ultimately, our new model is more sample efficient and achieves better pick and place success rates than the baseline Transporter Net model.