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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.
Linear representations of finite groups , volume 42
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End-to-end training of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel · 2016
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Discriminatively-guided deliberative perception for pose estimation of multiple 3d object instances
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Ahmed Hussein, Mohamed Medhat Gaber, Eyad Elyan, and Chrisina Jayne · 2017
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Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards
Mel Vecerik, Todd Hester, Jonathan Scholz, Fumin Wang, Olivier Pietquin, Bilal Piot, Nicolas Heess, Thomas Rothörl, Thomas Lampe, and Martin Riedmiller · 2017
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Deep q-learning from demonstrations
Todd Hester, Matej Vecerik, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Dan Horgan, John Quan, Andrew Sendonaris, Ian Osband, et al · 2018
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Closing the loop for robotic grasping: A real-time, generative grasp synthesis approach
Douglas Morrison, Peter Corke, and Jürgen Leitner · 2018
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Pcn: Point completion network
Wentao Yuan, Tejas Khot, David Held, Christoph Mertz, and Martial Hebert · 2018
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Learning synergies between pushing and grasping with self-supervised deep reinforcement learning
Andy Zeng, Shuran Song, Stefan Welker, Johnny Lee, Alberto Rodriguez, and Thomas Funkhouser · 2018
Self-supervised goal-conditioned pick and place
Coline Devin, Payam Rowghanian, Chris Vigorito, Will Richards, and Khashayar Rohanimanesh · 2020
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Action image representation: Learning scalable deep grasping policies with zero real world data
Mohi Khansari, Daniel Kappler, Jianlan Luo, Jeff Bingham, and Mrinal Kalakrishnan · 2020
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Keypose: Multi-view 3d labeling and keypoint estimation for transparent objects
Xingyu Liu, Rico Jonschkowski, Anelia Angelova, and Kurt Konolige · 2020
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Deep dynamics models for learning dexterous manipulation
Anusha Nagabandi, Kurt Konolige, Sergey Levine, and Vikash Kumar · 2020
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Form2fit: Learning shape priors for generalizable assembly from disassembly
Kevin Zakka, Andy Zeng, Johnny Lee, and Shuran Song · 2020
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Robotic pick-and-place of novel objects in clutter with multi-affordance grasping and cross-domain image matching
Andy Zeng, Shuran Song, Kuan-Ting Yu, Elliott Donlon, Francois R Hogan, Maria Bauza, Daolin Ma, Orion Taylor, Melody Liu, Eudald Romo, et al · 2018
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Grip: Generative robust inference and perception for semantic robot manipulation in adversarial environments
Xiaotong Chen, Rui Chen, Zhiqiang Sui, Zhefan Ye, Yanqi Liu, R Iris Bahar, and Odest Chadwicke Jenkins · 2019
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General e ( 2 ) e(2) -equivariant steerable cnns
Maurice Weiler and Gabriele Cesa · 2019
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Self-supervised learning for precise pick-and-place without object model
Lars Berscheid, Pascal Meißner, and Torsten Kröger · 2020
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Self-supervised 6d object pose estimation for robot manipulation
Xinke Deng, Yu Xiang, Arsalan Mousavian, Clemens Eppner, Timothy Bretl, and Dieter Fox · 2020
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Group equivariant convolutional networks
Taco Cohen and Max Welling
Cited in the paper.
Taco S Cohen and Max Welling
Cited in the paper.
Andy Zeng, Pete Florence, Jonathan Tompson, Stefan Welker, Jonathan Chien, Maria Attarian, Travis Armstrong, Ivan Krasin, Dan Duong, Vikas Sindhwani, et al · 2020
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Long-horizon manipulation of unknown objects via task and motion planning with estimated affordances
Aidan Curtis, Xiaolin Fang, Leslie Pack Kaelbling, Tomás Lozano-Pérez, and Caelan Reed Garrett · 2021
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Robotic pick-and-place with uncertain object instance segmentation and shape completion
Marcus Gualtieri and Robert Platt · 2021
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Gascn: Graph attention shape completion network
Haojie Huang, Ziyi Yang, and Robert Platt · 2021
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Nerp: Neural rearrangement planning for unknown objects
Ahmed H Qureshi, Arsalan Mousavian, Chris Paxton, Michael C Yip, and Dieter Fox · 2021
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