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Robots will be expected to manipulate a wide variety of objects in complex and arbitrary ways as they become more widely used in human environments.
Integrated task and motion planning
Caelan Reed Garrett, Rohan Chitnis, Rachel Holladay, Beomjoon Kim, Tom Silver, Leslie Pack Kaelbling, and Tomás Lozano-Pérez · 2010
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Constrained motion planning networks x
Ahmed H Qureshi, Jiangeng Dong, Asfiya Baig, and Michael C Yip · 2010
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Push planning for object placement on cluttered table surfaces
Akansel Cosgun, Tucker Hermans, Victor Emeli, and Mike Stilman · 2011
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Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer · 2011
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Fcl: A general purpose library for collision and proximity queries
J. Pan, S. Chitta, and D. Manocha · 2012
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Qmdp-net: Deep learning for planning under partial observability
Peter Karkus, David Hsu, and Wee Sun Lee · 2017
Earlier work this paper cites.
Unobservable monte carlo planning for nonprehensile rearrangement tasks
Jennifer E King, Vinitha Ranganeni, and Siddhartha S Srinivasa · 2017
Earlier work this paper cites.
Jeffrey Mahler, Matthew Matl, Xinyu Liu, Albert Li, David Gealy, and Ken Goldberg · 2017
Earlier work this paper cites.
Pointnet++: Deep hierarchical feature learning on point sets in a metric space
Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas · 2017
Earlier work this paper cites.
Universal planning networks: Learning generalizable representations for visuomotor control
Aravind Srinivas, Allan Jabri, Pieter Abbeel, Sergey Levine, and Chelsea Finn · 2018
Earlier work this paper cites.
Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
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Learning latent dynamics for planning from pixels
Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson · 2019
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Neural task graphs: Generalizing to unseen tasks from a single video demonstration
De-An Huang, Suraj Nair, Danfei Xu, Yuke Zhu, Animesh Garg, Li Fei-Fei, Silvio Savarese, and Juan Carlos Niebles · 2019
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Learning to guide task and motion planning using score-space representation
Beomjoon Kim, Zi Wang, Leslie Pack Kaelbling, and Tomás Lozano-Pérez · 2019
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Weisfeiler and leman go neural: Higher-order graph neural networks
Christopher Morris, Martin Ritzert, Matthias Fey, William L Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe · 2019
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Broadly-exploring, local-policy trees for long-horizon task planning
Brian Ichter, Pierre Sermanet, and Corey Lynch · 2020
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Monte-carlo tree search for efficient visually guided rearrangement planning
Yann Labbé, Sergey Zagoruyko, Igor Kalevatykh, Ivan Laptev, Justin Carpentier, Mathieu Aubry, and Josef Sivic · 2020
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6-dof grasping for target-driven object manipulation in clutter
Adithyavairavan Murali, Arsalan Mousavian, Clemens Eppner, Chris Paxton, and Dieter Fox · 2020
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A long horizon planning framework for manipulating rigid pointcloud objects
Anthony Simeonov, Yilun Du, Beomjoon Kim, Francois R Hogan, Joshua Tenenbaum, Pulkit Agrawal, and Alberto Rodriguez · 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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Arsalan Mousavian, Clemens Eppner, and Dieter Fox · 2019
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Planning for target retrieval using a robotic manipulator in cluttered and occluded environments
Changjoo Nam, Jinhwi Lee, Younggil Cho, Jeongho Lee, Dong Hwan Kim, and ChangHwan Kim · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Visual robot task planning
Chris Paxton, Yotam Barnoy, Kapil Katyal, Raman Arora, and Gregory D Hager · 2019
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Rearrangement: A challenge for embodied ai
Dhruv Batra, Angel X Chang, Sonia Chernova, Andrew J Davison, Jia Deng, Vladlen Koltun, Sergey Levine, Jitendra Malik, Igor Mordatch, Roozbeh Mottaghi, et al · 2020
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Learning physical graph representations from visual scenes
Daniel M Bear, Chaofei Fan, Damian Mrowca, Yunzhu Li, Seth Alter, Aran Nayebi, Jeremy Schwartz, Li Fei-Fei, Jiajun Wu, Joshua B Tenenbaum, et al · 2020
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Online replanning in belief space for partially observable task and motion problems
Caelan Reed Garrett, Chris Paxton, Tomás Lozano-Pérez, Leslie Pack Kaelbling, and Dieter Fox · 2020
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Learning rgb-d feature embeddings for unseen object instance segmentation
Yu Xiang, Christopher Xie, Arsalan Mousavian, and Dieter Fox · 2020
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The best of both modes: Separately leveraging rgb and depth for unseen object instance segmentation
Christopher Xie, Yu Xiang, Arsalan Mousavian, and Dieter Fox · 2020
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Deep affordance foresight: Planning through what can be done in the future
Danfei Xu, Ajay Mandlekar, Roberto Martín-Martín, Yuke Zhu, Silvio Savarese, and Li Fei-Fei · 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, and Johnny Lee · 2020
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Object rearrangement using learned implicit collision functions
Michael Danielczuk, Arsalan Mousavian, Clemens Eppner, and Dieter Fox · 2021
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Contact-graspnet: Efficient 6-dof grasp generation in cluttered scenes
Martin Sundermeyer, Arsalan Mousavian, Rudolph Triebel, and Dieter Fox · 2021
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