Fetching the paper…
Reading the bibliography…
In this paper, we propose a real-world benchmark for studying robotic learning in the context of functional manipulation: a robot needs to accomplish complex long-horizon behaviors by composing individual manipulation skills in functionally relevant ways.
Task-oriented optimal grasping by multifingered robot hands
Z. Li and S.S. Sastry · 1988
Earlier work this paper cites.
A case study of flexible object manipulation
John E. Hopcroft, Joseph K. Kearney, and Dean B. Krafft · 1991
Earlier work this paper cites.
Peg-on-hole: a model based solution to peg and hole alignment
H. Bruyninckx, S. Dutre, and J. De Schutter · 1995
Earlier work this paper cites.
An application of reinforcement learning to aerobatic helicopter flight
Pieter Abbeel, Adam Coates, Morgan Quigley, and Andrew Y. Ng · 2006
Earlier work this paper cites.
Grasp programming by demonstration: A task-based quality measure
Jacopo Aleotti and Stefano Caselli · 2008
Earlier work this paper cites.
Compliant manipulation for peg-in-hole: Is passive compliance a key to learn contact motion?
Seung kook Yun · 2008
Earlier work this paper cites.
Learning to control in operational space
Jan Peters and Stefan Schaal · 2008
Earlier work this paper cites.
Learning variable impedance control
Jonas Buchli, Freek Stulp, Evangelos Theodorou, and Stefan Schaal · 2011
Earlier work this paper cites.
A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey Gordon, and Drew Bagnell · 2011
Earlier work this paper cites.
Benchmarking in manipulation research: Using the yale-CMU-berkeley object and model set
Berk Calli, Aaron Walsman, Arjun Singh, Siddhartha Srinivasa, Pieter Abbeel, and Aaron M. Dollar · 2015
Earlier work this paper cites.
Benchmarking robot force control capabilities: Experimental results, 2016-01-07 2016
Joseph Falco, Jeremy Marvel, Richard Norcross, and Karl Van · 2016
Earlier work this paper cites.
Unsupervised learning for physical interaction through video prediction
Chelsea Finn, Ian Goodfellow, and Sergey Levine · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
End-to-end training of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel · 2016
Earlier work this paper cites.
Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours
Lerrel Pinto and Abhinav Gupta · 2016
Earlier work this paper cites.
Autonomous alignment of peg and hole by force/torque measurement for robotic assembly
Te Tang, Hsien-Chung Lin, Yu Zhao, Wenjie Chen, and Masayoshi Tomizuka · 2016
Earlier work this paper cites.
Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
Shixiang Gu, Ethan Holly, Timothy Lillicrap, and Sergey Levine · 2017
Earlier work this paper cites.
Dex-net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics
Jeffrey Mahler, Jacky Liang, Sherdil Niyaz, Michael Laskey, Richard Doan, Xinyu Liu, Juan Aparicio Ojea, and Ken Goldberg · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Experimental force-torque dataset for robot learning of multi-shape insertion
Giovanni De Magistris, Asim Munawar, Tu-Hoa Pham, Tadanobu Inoue, Phongtharin Vinayavekhin, and Ryuki Tachibana · 2018
Cited alongside, same era.
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, and Sergey Levine · 2018
Cited alongside, same era.
Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection
Sergey Levine, Peter Pastor, Alex Krizhevsky, Julian Ibarz, and Deirdre Quillen · 2018
Cited alongside, same era.
Film: Visual reasoning with a general conditioning layer
Ethan Perez, Florian Strub, Harm De Vries, Vincent Dumoulin, and Aaron Courville · 2018
Cited alongside, same era.
A dynamical-system-based approach for controlling robotic manipulators during noncontact/contact transitions
S. S. M. Salehian and A. Billard · 2018
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 · 2021
Later among the works it cites.
Towards robotic assembly by predicting robust, precise and task-oriented grasps
Jialiang Zhao, Daniel Troniak, and Oliver Kroemer · 2021
Later among the works it cites.
Bridge data: Boosting generalization of robotic skills with cross-domain datasets
Frederik Ebert, Yanlai Yang, Karl Schmeckpeper, Bernadette Bucher, Georgios Georgakis, Kostas Daniilidis, Chelsea Finn, and Sergey Levine · 2022
Later among the works it cites.
Beyond pick-and-place: Tackling robotic stacking of diverse shapes
Alex X. Lee, Coline Manon Devin, Yuxiang Zhou, Thomas Lampe, Konstantinos Bousmalis, Jost Tobias Springenberg, Arunkumar Byravan, Abbas Abdolmaleki, Nimrod Gileadi, David Khosid, Claudio Fantacci, Jose Enrique Chen, Akhil Raju, Rae Jeong, Michael Neunert, Antoine Laurens, Stefano Saliceti, Federico Casarini, Martin Riedmiller, raia hadsell, and Francesco Nori · 2022
Later among the works it cites.
Deep transfer learning of pick points on fabric for robot bed-making
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Graspnet: a large-scale clustered and densely annotated dataset for object grasping
Hao-Shu Fang, Chenxi Wang, Minghao Gou, and Cewu Lu · 2019
Cited alongside, same era.
Sim-to-real via sim-to-sim: Data-efficient robotic grasping via randomized-to-canonical adaptation networks
Stephen James, Paul Wohlhart, Mrinal Kalakrishnan, Dmitry Kalashnikov, Alex Irpan, Julian Ibarz, Sergey Levine, Raia Hadsell, and Konstantinos Bousmalis · 2019
Cited alongside, same era.
Reinforcement learning on variable impedance controller for high-precision robotic assembly
Jianlan Luo, Eugen Solowjow, Chengtao Wen, Juan Aparicio Ojea, Alice M Agogino, Aviv Tamar, and Pieter Abbeel · 2019
Cited alongside, same era.
Scaling robot supervision to hundreds of hours with roboturk: Robotic manipulation dataset through human reasoning and dexterity
Ajay Mandlekar, Jonathan Booher, Max Spero, Albert Tung, Anchit Gupta, Yuke Zhu, Animesh Garg, Silvio Savarese, and Li Fei-Fei · 2019
Cited alongside, same era.
Solving rubik’s cube with a robot hand
OpenAI, Ilge Akkaya, Marcin Andrychowicz, Maciek Chociej, Mateusz Litwin, Bob McGrew, Arthur Petron, Alex Paino, Matthias Plappert, Glenn Powell, Raphael Ribas, Jonas Schneider, Nikolas Tezak, Jerry Tworek, Peter Welinder, Lilian Weng, Qiming Yuan, Wojciech Zaremba, and Lei Zhang · 2019
Cited alongside, same era.
A practical approach to insertion with variable socket position using deep reinforcement learning
Mel Vecerik, Oleg Sushkov, David Barker, Thomas Rothörl, Todd Hester, and Jon Scholz · 2019
Cited alongside, same era.
Replab: A reproducible low-cost arm benchmark for robotic learning
Brian Yang, Dinesh Jayaraman, Jesse Zhang, and Sergey Levine · 2019
Cited alongside, same era.
Daniel Seita, Nawid Jamali, Michael Laskey, Ajay Kumar Tanwani, Ron Berenstein, Prakash Baskaran, Soshi Iba, John Canny, and Ken Goldberg · 2022
Later among the works it cites.
6-dof pose estimation of household objects for robotic manipulation: An accessible dataset and benchmark
Stephen Tyree, Jonathan Tremblay, Thang To, Jia Cheng, Terry Mosier, Jeffrey Smith, and Stan Birchfield · 2022
Later among the works it cites.
Dextairity: Deformable manipulation can be a breeze
Zhenjia Xu, Cheng Chi, Benjamin Burchfiel, Eric Cousineau, Siyuan Feng, and Shuran Song · 2022
Later among the works it cites.
Offline meta-reinforcement learning for industrial insertion
Tony Z. Zhao, Jianlan Luo, Oleg Sushkov, Rugile Pevceviciute, Nicolas Heess, Jon Scholz, Stefan Schaal, and Sergey Levine · 2022
Later among the works it cites.
Roboagent: Generalization and efficiency in robot manipulation via semantic augmentations and action chunking
Homanga Bharadhwaj, Jay Vakil, Mohit Sharma, Abhinav Gupta, Shubham Tulsiani, and Vikash Kumar · 2023
Later among the works it cites.
Rt-1: Robotics transformer for real-world control at scale
Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Jasmine Hsu, et al · 2023
Later among the works it cites.
Diffusion policy: Visuomotor policy learning via action diffusion
Cheng Chi, Siyuan Feng, Yilun Du, Zhenjia Xu, Eric Cousineau, Benjamin Burchfiel, and Shuran Song · 2023
Later among the works it cites.
Open x-embodiment: Robotic learning datasets and rt-x models
Embodiment Collaboration, Abhishek Padalkar, Acorn Pooley, Ajay Mandlekar, Ajinkya Jain, Albert Tung, Alex Bewley, Alex Herzog, Alex Irpan, Alexander Khazatsky, Anant Rai, Anikait Singh, Animesh Garg, Anthony Brohan, Antonin Raffin, Ayzaan Wahid, Ben Burgess-Limerick, Beomjoon Kim, Bernhard Schölkopf, Brian Ichter, Cewu Lu, Charles Xu, Chelsea Finn, Chenfeng Xu, Cheng Chi, Chenguang Huang, Christine Chan, Chuer Pan, Chuyuan Fu, Coline Devin, Danny Driess, Deepak Pathak, Dhruv Shah, Dieter Büchler, Dmitry Kalashnikov, Dorsa Sadigh, Edward Johns, Federico Ceola, Fei Xia, Freek Stulp, Gaoyue Zhou, Gaurav S. Sukhatme, Gautam Salhotra, Ge Yan, Giulio Schiavi, Gregory Kahn, Hao Su, Hao-Shu Fang, Haochen Shi, Heni Ben Amor, Henrik I Christensen, Hiroki Furuta, Homer Walke, Hongjie Fang, Igor Mordatch, Ilija Radosavovic, Isabel Leal, Jacky Liang, Jad Abou-Chakra, Jaehyung Kim, Jan Peters, Jan Schneider, Jasmine Hsu, Jeannette Bohg, Jeffrey Bingham, Jiajun Wu, Jialin Wu, Jianlan Luo, Jiayuan Gu, Jie Tan, Jihoon Oh, Jitendra Malik, Jonathan Booher, Jonathan Tompson, Jonathan Yang, Joseph J. Lim, João Silvério, Junhyek Han, Kanishka Rao, Karl Pertsch, Karol Hausman, Keegan Go, Keerthana Gopalakrishnan, Ken Goldberg, Kendra Byrne, Kenneth Oslund, Kento Kawaharazuka, Kevin Zhang, Krishan Rana, Krishnan Srinivasan, Lawrence Yunliang Chen, Lerrel Pinto, Li Fei-Fei, Liam Tan, Lionel Ott, Lisa Lee, Masayoshi Tomizuka, Max Spero, Maximilian Du, Michael Ahn, Mingtong Zhang, Mingyu Ding, Mohan Kumar Srirama, Mohit Sharma, Moo Jin Kim, Naoaki Kanazawa, Nicklas Hansen, Nicolas Heess, Nikhil J Joshi, Niko Suenderhauf, Norman Di Palo, Nur Muhammad Mahi Shafiullah, Oier Mees, Oliver Kroemer, Pannag R Sanketi, Paul Wohlhart, Peng Xu, Pierre Sermanet, Priya Sundaresan, Quan Vuong, Rafael Rafailov, Ran Tian, Ria Doshi, Roberto Martín-Martín, Russell Mendonca, Rutav Shah, Ryan Hoque, Ryan Julian, Samuel Bustamante, Sean Kirmani, Sergey Levine, Sherry Moore, Shikhar Bahl, Shivin Dass, Shubham Sonawani, Shuran Song, Sichun Xu, Siddhant Haldar, Simeon Adebola, Simon Guist, Soroush Nasiriany, Stefan Schaal, Stefan Welker, Stephen Tian, Sudeep Dasari, Suneel Belkhale, Takayuki Osa, Tatsuya Harada, Tatsuya Matsushima, Ted Xiao, Tianhe Yu, Tianli Ding, Todor Davchev, Tony Z. Zhao, Travis Armstrong, Trevor Darrell, Vidhi Jain, Vincent Vanhoucke, Wei Zhan, Wenxuan Zhou, Wolfram Burgard, Xi Chen, Xiaolong Wang, Xinghao Zhu, Xuanlin Li, Yao Lu, Yevgen Chebotar, Yifan Zhou, Yifeng Zhu, Ying Xu, Yixuan Wang, Yonatan Bisk, Yoonyoung Cho, Youngwoon Lee, Yuchen Cui, Yueh-Hua Wu, Yujin Tang, Yuke Zhu, Yunzhu Li, Yusuke Iwasawa, Yutaka Matsuo, Zhuo Xu, and Zichen Jeff Cui · 2023
Later among the works it cites.
Rh20t: A robotic dataset for learning diverse skills in one-shot
Hao-Shu Fang, Hongjie Fang, Zhenyu Tang, Jirong Liu, Junbo Wang, Haoyi Zhu, and Cewu Lu · 2023
Later among the works it cites.
Furniturebench: Reproducible real-world benchmark for long-horizon complex manipulation
Minho Heo, Youngwoon Lee, Doohyun Lee, and Joseph J Lim · 2023
Later among the works it cites.
Reboot: Reuse data for bootstrapping efficient real-world dexterous manipulation
Zheyuan Hu, Aaron Rovinsky, Jianlan Luo, Vikash Kumar, Abhishek Gupta, and Sergey Levine · 2023
Later among the works it cites.
Fewsol: A dataset for few-shot object learning in robotic environments
Jishnu Jaykumar Padalunkal, Yu-Wei Chao, and Yu Xiang · 2023
Later among the works it cites.
Bridgedata v2: A dataset for robot learning at scale
Homer Rich Walke, Kevin Black, Tony Z. Zhao, Quan Vuong, Chongyi Zheng, Philippe Hansen-Estruch, Andre Wang He, Vivek Myers, Moo Jin Kim, Max Du, Abraham Lee, Kuan Fang, Chelsea Finn, and Sergey Levine · 2023
Later among the works it cites.
Learning fine-grained bimanual manipulation with low-cost hardware
Tony Z. Zhao, Vikash Kumar, Sergey Levine, and Chelsea Finn · 2023
Later among the works it cites.
Rt-2: Vision-language-action models transfer web knowledge to robotic control
Brianna Zitkovich, Tianhe Yu, Sichun Xu, Peng Xu, Ted Xiao, Fei Xia, Jialin Wu, Paul Wohlhart, Stefan Welker, Ayzaan Wahid, Quan Vuong, Vincent Vanhoucke, Huong Tran, Radu Soricut, Anikait Singh, Jaspiar Singh, Pierre Sermanet, Pannag R. Sanketi, Grecia Salazar, Michael S. Ryoo, Krista Reymann, Kanishka Rao, Karl Pertsch, Igor Mordatch, Henryk Michalewski, Yao Lu, Sergey Levine, Lisa Lee, Tsang-Wei Edward Lee, Isabel Leal, Yuheng Kuang, Dmitry Kalashnikov, Ryan Julian, Nikhil J. Joshi, Alex Irpan, Brian Ichter, Jasmine Hsu, Alexander Herzog, Karol Hausman, Keerthana Gopalakrishnan, Chuyuan Fu, Pete Florence, Chelsea Finn, Kumar Avinava Dubey, Danny Driess, Tianli Ding, Krzysztof Marcin Choromanski, Xi Chen, Yevgen Chebotar, Justice Carbajal, Noah Brown, Anthony Brohan, Montserrat Gonzalez Arenas, and Kehang Han · 2023
Later among the works it cites.