Fetching the paper…
Reading the bibliography…
To interact with daily-life articulated objects of diverse structures and functionalities, understanding the object parts plays a central role in both user instruction comprehension and task execution.
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
Martin A Fischler and Robert C Bolles · 1981
Earlier work this paper cites.
Least-squares estimation of transformation parameters between two point patterns
Shinji Umeyama · 1991
Earlier work this paper cites.
High-level control of a mobile manipulator for door opening
L Peterson, David Austin, and Danica Kragic · 2000
Earlier work this paper cites.
Distributed cognition, representation, and affordance
Jiajie Zhang and Vimla L Patel · 2006
Earlier work this paper cites.
Pulling open novel doors and drawers with equilibrium point control
Advait Jain and Charles C Kemp · 2009
Earlier work this paper cites.
Whole-body motion planning for manipulation of articulated objects
Felix Burget, Armin Hornung, and Maren Bennewitz · 2013
Earlier work this paper cites.
Scannet: Richly-annotated 3d reconstructions of indoor scenes
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
End-to-end driving via conditional imitation learning
Felipe Codevilla, Matthias Müller, Antonio López, Vladlen Koltun, and Alexey Dosovitskiy · 2018
Earlier work this paper cites.
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
Earlier work this paper cites.
Hierarchical decision making by generating and following natural language instructions
Hengyuan Hu, Denis Yarats, Qucheng Gong, Yuandong Tian, and Mike Lewis · 2019
Earlier work this paper cites.
Doorgym: A scalable door opening environment and baseline agent
Yusuke Urakami, Alec Hodgkinson, Casey Carlin, Randall Leu, Luca Rigazio, and Pieter Abbeel · 2019
Earlier work this paper cites.
Graspnet-1billion: A large-scale benchmark for general object grasping
Hao-Shu Fang, Chenxi Wang, Minghao Gou, and Cewu Lu · 2020
Earlier work this paper cites.
Pointgroup: Dual-set point grouping for 3d instance segmentation
Li Jiang, Hengshuang Zhao, Shaoshuai Shi, Shu Liu, Chi-Wing Fu, and Jiaya Jia · 2020
Earlier work this paper cites.
Language-conditioned imitation learning for robot manipulation tasks
Simon Stepputtis, Joseph Campbell, Mariano Phielipp, Stefan Lee, Chitta Baral, and Heni Ben Amor · 2020
Earlier work this paper cites.
Sapien: A simulated part-based interactive environment
Fanbo Xiang, Yuzhe Qin, Kaichun Mo, Yikuan Xia, Hao Zhu, Fangchen Liu, Minghua Liu, Hanxiao Jiang, Yifu Yuan, He Wang, et al · 2020
Earlier work this paper cites.
Grounding language to autonomously-acquired skills via goal generation
Ahmed Akakzia, Cédric Colas, Pierre-Yves Oudeyer, Mohamed Chetouani, and Olivier Sigaud · 2021
Earlier work this paper cites.
Act the part: Learning interaction strategies for articulated object part discovery
Samir Yitzhak Gadre, Kiana Ehsani, and Shuran Song · 2021
Earlier work this paper cites.
kpam-sc: Generalizable manipulation planning using keypoint affordance and shape completion
Wei Gao and Russ Tedrake · 2021
Earlier work this paper cites.
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
Earlier work this paper cites.
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.
Zero-shot task adaptation using natural language
Prasoon Goyal, Raymond J. Mooney, and Scott Niekum · 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.
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
Softgroup for 3d instance segmentation on point clouds
Thang Vu, Kookhoi Kim, Tung M Luu, Thanh Nguyen, and Chang D Yoo · 2022
Later among the works it cites.
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
Later among the works it cites.
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
Later among the works it cites.
Universal manipulation policy network for articulated objects
Zhenjia Xu, Zhanpeng He, and Shuran Song · 2022
Later among the works it cites.
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
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Concept2robot: Learning manipulation concepts from instructions and human demonstrations
Lin Shao, Toki Migimatsu, Qiang Zhang, Karen Yang, and Jeannette Bohg · 2021
Cited alongside, same era.
Contact-graspnet: Efficient 6-dof grasp generation in cluttered scenes
Martin Sundermeyer, Arsalan Mousavian, Rudolph Triebel, and Dieter Fox · 2021
Cited alongside, same era.
Adagrasp: Learning an adaptive gripper-aware grasping policy
Zhenjia Xu, Beichun Qi, Shubham Agrawal, and Shuran Song · 2021
Cited alongside, same era.
Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning, 2021
Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Avnish Narayan, Hayden Shively, Adithya Bellathur, Karol Hausman, Chelsea Finn, and Sergey Levine · 2021
Cited alongside, same era.
Flowbot3d: Learning 3d articulation flow to manipulate articulated objects
Ben Eisner, Harry Zhang, and David Held · 2022
Cited alongside, same era.
An image is worth one word: Personalizing text-to-image generation using textual inversion, 2022
Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit H. Bermano, Gal Chechik, and Daniel Cohen-Or · 2022
Cited alongside, same era.
Bc-z: Zero-shot task generalization with robotic imitation learning
Eric Jang, Alex Irpan, Mohi Khansari, Daniel Kappler, Frederik Ebert, Corey Lynch, Sergey Levine, and Chelsea Finn · 2022
Cited alongside, same era.
Do as i can, not as i say: Grounding language in robotic affordances
Anthony Brohan, Yevgen Chebotar, Chelsea Finn, Karol Hausman, Alexander Herzog, Daniel Ho, Julian Ibarz, Alex Irpan, Eric Jang, Ryan Julian, et al · 2023
Closest in time.
Partmanip: Learning cross-category generalizable part manipulation policy from point cloud observations, 2023
Haoran Geng, Ziming Li, Yiran Geng, Jiayi Chen, Hao Dong, and He Wang · 2023
Closest in time.
Arnold: A benchmark for language-grounded task learning with continuous states in realistic 3d scenes
Ran Gong, Jiangyong Huang, Yizhou Zhao, Haoran Geng, Xiaofeng Gao, Qingyang Wu, Wensi Ai, Ziheng Zhou, Demetri Terzopoulos, Song-Chun Zhu, et al · 2023
Closest in time.
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.
Voxposer: Composable 3d value maps for robotic manipulation with language models
Wenlong Huang, Chen Wang, Ruohan Zhang, Yunzhu Li, Jiajun Wu, and Li Fei-Fei · 2023
Closest in time.
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollár, and Ross Girshick · 2023
Closest in time.
Text2motion: From natural language instructions to feasible plans
Kevin Lin, Christopher Agia, Toki Migimatsu, Marco Pavone, and Jeannette Bohg · 2023
Closest in time.
Gpt-4 technical report, 2023
OpenAI · 2023
Closest in time.
Dinov2: Learning robust visual features without supervision, 2023
Maxime Oquab, Timothée Darcet, Theo Moutakanni, Huy V. Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Russell Howes, Po-Yao Huang, Hu Xu, Vasu Sharma, Shang-Wen Li, Wojciech Galuba, Mike Rabbat, Mido Assran, Nicolas Ballas, Gabriel Synnaeve, Ishan Misra, Herve Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bojanowski · 2023
Closest in time.
Perceiver-actor: A multi-task transformer for robotic manipulation
Mohit Shridhar, Lucas Manuelli, and Dieter Fox · 2023
Closest in time.
Weikang Wan, Haoran Geng, Yun Liu, Zikang Shan, Yaodong Yang, Li Yi, and He Wang · 2023
Closest in time.
Chain-of-thought prompting elicits reasoning in large language models, 2023
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou · 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
Closest in time.
Make a donut: Language-guided hierarchical emd-space planning for zero-shot deformable object manipulation, 2023
Yang You, Bokui Shen, Congyue Deng, Haoran Geng, He Wang, and Leonidas Guibas · 2023
Closest in time.