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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 (1)
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Dian Wang, Colin Kohler (, International Symposium on Robotics Research)
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Xupeng Zhu, Dian Wang (, Robotics science and systems)
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Dian Wang, Robin Walters (, International Conference on Learning Representations)
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Haojie Huang; Dian Wang; Robin Walters; Robert Platt (, Robotics:Science and Systems)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 (Ed.)
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Dian Wang; Colin Kohler; Xupeng Zhu; Mingxi Jia; Robert Platt (, International Symposium on Robotics Research)
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Dian Wang; Mingxi Jia; Xupeng Zhu; Robin Walters; Robert Platt (, Conference on robot learning)
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Hai Nguyen; Andrea Baisero; Dian Wang; Christopher Amato; Robert Platt (, Conference on Robot Learning)
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