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Learning a generalizable object manipulation policy is vital for an embodied agent to work in complex real-world scenes.
High-level control of a mobile manipulator for door opening
L Peterson, David Austin, and Danica Kragic · 2000
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Pulling open novel doors and drawers with equilibrium point control
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A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey Gordon, and Drew Bagnell · 2011
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Whole-body motion planning for manipulation of articulated objects
Felix Burget, Armin Hornung, and Maren Bennewitz · 2013
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Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2015
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High-dimensional continuous control using generalized advantage estimation
John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel · 2015
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Towards vision-based deep reinforcement learning for robotic motion control
Fangyi Zhang, Jürgen Leitner, Michael Milford, Ben Upcroft, and Peter Corke · 2015
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Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
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Submanifold sparse convolutional networks
Benjamin Graham and Laurens van der Maaten · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 2017
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Pointnet++: Deep hierarchical feature learning on point sets in a metric space
Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas · 2017
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Learning complex dexterous manipulation with deep reinforcement learning and demonstrations
Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, Giulia Vezzani, John Schulman, Emanuel Todorov, and Sergey Levine · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Scalable deep reinforcement learning for vision-based robotic manipulation
Dmitry Kalashnikov, Alex Irpan, Peter Pastor, Julian Ibarz, Alexander Herzog, Eric Jang, Deirdre Quillen, Ethan Holly, Mrinal Kalakrishnan, Vincent Vanhoucke, et al · 2018
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Domain generalization with adversarial feature learning
Haoliang Li, Sinno Jialin Pan, Shiqi Wang, and Alex C Kot · 2018
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Ipod: Intensive point-based object detector for point cloud, 2018
Zetong Yang, Yanan Sun, Shu Liu, Xiaoyong Shen, and Jiaya Jia · 2018
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Solving rubik’s cube with a robot hand
Ilge Akkaya, Marcin Andrychowicz, Maciek Chociej, Mateusz Litwin, Bob McGrew, Arthur Petron, Alex Paino, Matthias Plappert, Glenn Powell, Raphael Ribas, et al · 2019
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Doorgym: A scalable door opening environment and baseline agent
Yusuke Urakami, Alec Hodgkinson, Casey Carlin, Randall Leu, Luca Rigazio, and Pieter Abbeel · 2019
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Normalized object coordinate space for category-level 6d object pose and size estimation
He Wang, Srinath Sridhar, Jingwei Huang, Julien Valentin, Shuran Song, and Leonidas J Guibas · 2019
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Graspnet-1billion: A large-scale benchmark for general object grasping
Hao-Shu Fang, Chenxi Wang, Minghao Gou, and Cewu Lu · 2020
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Pointgroup: Dual-set point grouping for 3d instance segmentation
Li Jiang, Hengshuang Zhao, Shaoshuai Shi, Shu Liu, Chi-Wing Fu, and Jiaya Jia · 2020
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Curl: Contrastive unsupervised representations for reinforcement learning
Aravind Srinivas, Michael Laskin, and Pieter Abbeel · 2020
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kpam-sc: Generalizable manipulation planning using keypoint affordance and shape completion
Wei Gao and Russ Tedrake · 2021
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Why generalization in rl is difficult: Epistemic pomdps and implicit partial observability
Dibya Ghosh, Jad Rahme, Aviral Kumar, Amy Zhang, Ryan P Adams, and Sergey Levine · 2021
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Flowbot3d: Learning 3d articulation flow to manipulate articulated objects
Ben Eisner, Harry Zhang, and David Held · 2022
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Haoran Geng, Helin Xu, Chengyang Zhao, Chao Xu, Li Yi, Siyuan Huang, and He Wang · 2022
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End-to-end affordance learning for robotic manipulation
Yiran Geng, Boshi An, Haoran Geng, Yuanpei Chen, Yaodong Yang, and Hao Dong · 2022
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Silver-bullet-3d at maniskill 2021: Learning-from-demonstrations and heuristic rule-based methods for object manipulation, 2022
Yingwei Pan, Yehao Li, Yiheng Zhang, Qi Cai, Fuchen Long, Zhaofan Qiu, Ting Yao, and Tao Mei · 2022
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Knowledge distillation: A survey
Jianping Gou, Baosheng Yu, Stephen J Maybank, and Dacheng Tao · 2021
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Rgb matters: Learning 7-dof grasp poses on monocular rgbd images
Minghao Gou, Hao-Shu Fang, Zhanda Zhu, Sheng Xu, Chenxi Wang, and Cewu Lu · 2021
Cited alongside, same era.
A survey of generalisation in deep reinforcement learning
Robert Kirk, Amy Zhang, Edward Grefenstette, and Tim Rocktäschel · 2021
Cited alongside, same era.
Isaac gym: High performance gpu-based physics simulation for robot learning
Viktor Makoviychuk, Lukasz Wawrzyniak, Yunrong Guo, Michelle Lu, Kier Storey, Miles Macklin, David Hoeller, Nikita Rudin, Arthur Allshire, Ankur Handa, et al · 2021
Cited alongside, same era.
Where2act: From pixels to actions for articulated 3d objects
Kaichun Mo, Leonidas J Guibas, Mustafa Mukadam, Abhinav Gupta, and Shubham Tulsiani · 2021
Cited alongside, same era.
ManiSkill: Generalizable Manipulation Skill Benchmark with Large-Scale Demonstrations
Tongzhou Mu, Zhan Ling, Fanbo Xiang, Derek Yang, Xuanlin Li, Stone Tao, Zhiao Huang, Zhiwei Jia, and Hao Su · 2021
Cited alongside, same era.
Decoupling representation learning from reinforcement learning
Adam Stooke, Kimin Lee, Pieter Abbeel, and Michael Laskin · 2021
Cited alongside, same era.
Ilija Radosavovic, Tete Xiao, Stephen James, Pieter Abbeel, Jitendra Malik, and Trevor Darrell · 2022
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Scott Reed, Konrad Zolna, Emilio Parisotto, Sergio Gomez Colmenarejo, Alexander Novikov, Gabriel Barth-Maron, Mai Gimenez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, et al · 2022
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Masked world models for visual control
Younggyo Seo, Danijar Hafner, Hao Liu, Fangchen Liu, Stephen James, Kimin Lee, and Pieter Abbeel · 2022
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Reinforcement learning with action-free pre-training from videos
Younggyo Seo, Kimin Lee, Stephen L James, and Pieter Abbeel · 2022
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Hao Shen, Weikang Wan, and He Wang · 2022
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Softgroup for 3d instance segmentation on point clouds
Thang Vu, Kookhoi Kim, Tung M Luu, Thanh Nguyen, and Chang D Yoo · 2022
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Adaafford: Learning to adapt manipulation affordance for 3d articulated objects via few-shot interactions
Yian Wang, Ruihai Wu, Kaichun Mo, Jiaqi Ke, Qingnan Fan, Leonidas J Guibas, and Hao Dong · 2022
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A minimalist ensemble method for generalizable offline deep reinforcement learning
Kun Wu, Yinuo Zhao, Zhiyuan Xu, Zhen Zhao, Pei Ren, Zhengping Che, Chi Harold Liu, Feifei Feng, and Jian Tang · 2022
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VAT-mart: Learning visual action trajectory proposals for manipulating 3d ARTiculated objects
Ruihai Wu, Yan Zhao, Kaichun Mo, Zizheng Guo, Yian Wang, Tianhao Wu, Qingnan Fan, Xuelin Chen, Leonidas Guibas, and Hao Dong · 2022
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Learning generalizable dexterous manipulation from human grasp affordance
Yueh-Hua Wu, Jiashun Wang, and Xiaolong Wang · 2022
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Universal manipulation policy network for articulated objects
Zhenjia Xu, Zhanpeng He, and Shuran Song · 2022
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Dualafford: Learning collaborative visual affordance for dual-gripper object manipulation
Yan Zhao, Ruihai Wu, Zhehuan Chen, Yourong Zhang, Qingnan Fan, Kaichun Mo, and Hao Dong · 2022
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Maniskill2: A unified benchmark for generalizable manipulation skills
Jiayuan Gu, Fanbo Xiang, Xuanlin Li, Zhan Ling, Xiqiang Liu, Tongzhou Mu, Yihe Tang, Stone Tao, Xinyue Wei, Yunchao Yao, et al · 2023
Closest in time.
Yinzhen Xu, Weikang Wan, Jialiang Zhang, Haoran Liu, Zikang Shan, Hao Shen, Ruicheng Wang, Haoran Geng, Yijia Weng, Jiayi Chen, et al · 2023
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