Sep 15, 2021 · In this work, we aim to solve multi-task learning through the lens of sequence-conditioning and weighted sampling.
Our results show that the new framework significantly outperforms original Transporter networks [12], [13] on 10 multi-task benchmark problems in MultiRavens, ...
Enabling robots to solve multiple manipulation tasks has a wide range of industrial applications. While learning-based approaches enjoy flexibility and ...
Sep 15, 2021 · This work proposes a new suite of benchmark specifically aimed at compositional tasks, MultiRavens, and proposes a vision-based end-to-end ...
SCTN is trained through multi-task weighted sampling, and can reason with demonstration sequence images with the trained networks to output pick-and-place ...
Oct 28, 2020 · We propose the Transporter Network, a simple model architecture that rearranges deep features to infer spatial displacements from visual input.
Aleksandra Faust is a Director of Research at Google DeepMind. Her research is centered around safe and scalable autonomous systems for social good, ...
Multi-Task Learning with Sequence-Conditioned Transporter Networks. Michael ... In this work, we aim to solve multi-task learning through the lens of sequence- ...
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Multi-Task Learning with Sequence-Conditioned Transporter Networks ... Enabling robots to solve multiple manipulation tasks has a wide range of industrial ...
We propose embedding goal-conditioning into Transporter Networks, a recently proposed model architecture for learning robotic manipulation that rearranges deep ...