Equivariant Transporter Network. (arXiv:2202.09400v5 [cs.RO] CROSS LISTED)

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 …

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