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Manipulating deformable objects, such as ropes and clothing, is a long-standing challenge in robotics, because of their large degrees of freedom, complex non-linear dynamics, and self-occlusion in visual perception.
Vision-guided robotic fabric manipulation for apparel manufacturing
Eric Torgerson and Frank W Paul · 1988
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Robust manipulation of deformable objects by a simple pid feedback
Takahiro Wada, Shinichi Hirai, Sadao Kawamura, and Norimasa Kamiji · 2001
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Path planning for deformable linear objects
Mark Moll and Lydia E Kavraki · 2006
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Manipulation planning for deformable linear objects
Mitul Saha and Pekka Isto · 2007
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Contrastive variational model-based reinforcement learning for complex observations
Xiao Ma, Siwei Chen, David Hsu, and Wee Sun Lee · 2008
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Jerzy Smolen and Alexandru Patriciu · 2009
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Yuji Yamakawa, Akio Namiki, and Masatoshi Ishikawa · 2011
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A geometric approach to robotic laundry folding
Stephen Miller, Jur Van Den Berg, Mario Fritz, Trevor Darrell, Ken Goldberg, and Pieter Abbeel · 2012
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Shared autonomy via hindsight optimization
Shervin Javdani, Siddhartha S Srinivasa, and J Andrew Bagnell · 2015
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Folding deformable objects using predictive simulation and trajectory optimization
Yinxiao Li, Yonghao Yue, Danfei Xu, Eitan Grinspun, and Peter K Allen · 2015
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Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Sim-to-real reinforcement learning for deformable object manipulation
Jan Matas, Stephen James, and Andrew J Davison · 2018
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Particle-based fluid simulation with nvidia flex
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Danijar Hafner, Timothy P. Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson · 2019
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Softgym: Benchmarking deep reinforcement learning for deformable object manipulation
Xingyu Lin, Yufei Wang, Jake Olkin, and David Held · 2020
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Particle filter recurrent neural networks
Xiao Ma, Péter Karkus, David Hsu, and Wee Sun Lee · 2020
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Learning to simulate complex physics with graph networks
Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, and Peter Battaglia · 2020
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Daniel Seita, Pete Florence, Jonathan Tompson, Erwin Coumans, Vikas Sindhwani, Ken Goldberg, and Andy Zeng · 2020
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A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip · 2020
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Differentiable algorithm networks for composable robot learning
Peter Karkus, Xiao Ma, David Hsu, Leslie Pack Kaelbling, Wee Sun Lee, and Tomás Lozano-Pérez · 2019
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Unsupervised learning of object keypoints for perception and control
Tejas D Kulkarni, Ankush Gupta, Catalin Ionescu, Sebastian Borgeaud, Malcolm Reynolds, Andrew Zisserman, and Volodymyr Mnih · 2019
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Learning particle dynamics for manipulating rigid bodies, deformable objects, and fluids
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Deep imitation learning of sequential fabric smoothing policies
Daniel Seita, Aditya Ganapathi, Ryan Hoque, Minho Hwang, Edward Cen, Ajay Kumar Tanwani, Ashwin Balakrishna, Brijen Thananjeyan, Jeffrey Ichnowski, Nawid Jamali, et al · 2019
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Learning to manipulate deformable objects without demonstrations
Yilin Wu, Wilson Yan, Thanard Kurutach, Lerrel Pinto, and Pieter Abbeel · 2019
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Contrastive learning of structured world models
Thomas N. Kipf, Elise van der Pol, and Max Welling · 2020
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Learning arbitrary-goal fabric folding with one hour of real robot experience
Robert Lee, Daniel Ward, Akansel Cosgun, Vibhavari Dasagi, Peter Corke, and Jurgen Leitner · 2020
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Learning predictive representations for deformable objects using contrastive estimation
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Spatio-temporal graph transformer networks for pedestrian trajectory prediction
Cunjun Yu, Xiao Ma, Jiawei Ren, Haiyu Zhao, and Shuai Yi · 2020
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Transporter networks: Rearranging the visual world for robotic manipulation
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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Ab initio particle-based object manipulation
Siwei Chen, Xiao Ma, Yunfan Lu, and David Hsu · 2021
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Learning dense visual correspondences in simulation to smooth and fold real fabrics
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Learning visible connectivity dynamics for cloth smoothing
Xingyu Lin, Yufei Wang, and David Held · 2021
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