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Visuomotor policies have shown great promise in robotic manipulation but often require substantial amounts of human-collected data for effective performance.
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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End-to-end training of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel · 2016
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Decoupled weight decay regularization
I Loshchilov · 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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Sim-to-real transfer of robotic control with dynamics randomization
Xue Bin Peng, Marcin Andrychowicz, Wojciech Zaremba, and Pieter Abbeel · 2018
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Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Karol Hausman, Chelsea Finn, and Sergey Levine · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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Coarse-to-fine imitation learning: Robot manipulation from a single demonstration
Edward Johns · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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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 · 2021
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A system for general in-hand object re-orientation
Tao Chen, Jie Xu, and Pulkit Agrawal · 2022
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Learning multi-stage tasks with one demonstration via self-replay
Norman Di Palo and Edward Johns · 2022
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Implicit behavioral cloning
Pete Florence, Corey Lynch, Andy Zeng, Oscar A Ramirez, Ayzaan Wahid, Laura Downs, Adrian Wong, Johnny Lee, Igor Mordatch, and Jonathan Tompson · 2022
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Neural descriptor fields: Se (3)-equivariant object representations for manipulation
Anthony Simeonov, Yilun Du, Andrea Tagliasacchi, Joshua B Tenenbaum, Alberto Rodriguez, Pulkit Agrawal, and Vincent Sitzmann · 2022
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Demonstrate once, imitate immediately (dome): Learning visual servoing for one-shot imitation learning
Eugene Valassakis, Georgios Papagiannis, Norman Di Palo, and Edward Johns · 2022
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You only demonstrate once: Category-level manipulation from single visual demonstration
Bowen Wen, Wenzhao Lian, Kostas Bekris, and Stefan Schaal · 2022
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3d-aware image synthesis via learning structural and textural representations
Yinghao Xu, Sida Peng, Ceyuan Yang, Yujun Shen, and Bolei Zhou · 2022
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What makes pre-trained visual representations successful for robust manipulation?
Kaylee Burns, Zach Witzel, Jubayer Ibn Hamid, Tianhe Yu, Chelsea Finn, and Karol Hausman · 2023
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Genaug: Retargeting behaviors to unseen situations via generative augmentation
Zoey Chen, Sho Kiami, Abhishek Gupta, and Vikash Kumar · 2023
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Nod-tamp: Multi-step manipulation planning with neural object descriptors
Shuo Cheng, Caelan Reed Garrett, Ajay Mandlekar, and Danfei Xu · 2023
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Diffusion policy: Visuomotor policy learning via action diffusion
Cheng Chi, Siyuan Feng, Yilun Du, Zhenjia Xu, Eric Cousineau, Benjamin Burchfiel, and Shuran Song · 2023
Gensim2: Scaling robot data generation with multi-modal and reasoning llms
Pu Hua, Minghuan Liu, Annabella Macaluso, Yunfeng Lin, Weinan Zhang, Huazhe Xu, and Lirui Wang · 2024
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Dexmimicgen: Automated data generation for bimanual dexterous manipulation via imitation learning
Zhenyu Jiang, Yuqi Xie, Kevin Lin, Zhenjia Xu, Weikang Wan, Ajay Mandlekar, Linxi Fan, and Yuke Zhu · 2024
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3d diffuser actor: Policy diffusion with 3d scene representations
Tsung-Wei Ke, Nikolaos Gkanatsios, and Katerina Fragkiadaki · 2024
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Droid: A large-scale in-the-wild robot manipulation dataset
Alexander Khazatsky, Karl Pertsch, Suraj Nair, Ashwin Balakrishna, Sudeep Dasari, Siddharth Karamcheti, Soroush Nasiriany, Mohan Kumar Srirama, Lawrence Yunliang Chen, Kirsty Ellis, et al · 2024
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Cited alongside, same era.
Imitating task and motion planning with visuomotor transformers
Murtaza Dalal, Ajay Mandlekar, Caelan Reed Garrett, Ankur Handa, Ruslan Salakhutdinov, and Dieter Fox · 2023
Cited alongside, same era.
R3m: A universal visual representation for robot manipulation
Suraj Nair, Aravind Rajeswaran, Vikash Kumar, Chelsea Finn, and Abhinav Gupta · 2023
Cited alongside, same era.
Dinov2: Learning robust visual features without supervision
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al · 2023
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Useek: Unsupervised se (3)-equivariant 3d keypoints for generalizable manipulation
Zhengrong Xue, Zhecheng Yuan, Jiashun Wang, Xueqian Wang, Yang Gao, and Huazhe Xu · 2023
Cited alongside, same era.
Scaling robot learning with semantically imagined experience
Tianhe Yu, Ted Xiao, Austin Stone, Jonathan Tompson, Anthony Brohan, Su Wang, Jaspiar Singh, Clayton Tan, Jodilyn Peralta, Brian Ichter, et al · 2023
Cited alongside, same era.
Learning fine-grained bimanual manipulation with low-cost hardware
Tony Z Zhao, Vikash Kumar, Sergey Levine, and Chelsea Finn · 2023
Cited alongside, same era.
Mirage: Cross-embodiment zero-shot policy transfer with cross-painting
Lawrence Yunliang Chen, Kush Hari, Karthik Dharmarajan, Chenfeng Xu, Quan Vuong, and Ken Goldberg · 2024
Cited alongside, same era.
Okami: Teaching humanoid robots manipulation skills through single video imitation
Jinhan Li, Yifeng Zhu, Yuqi Xie, Zhenyu Jiang, Mingyo Seo, Georgios Pavlakos, and Yuke Zhu · 2024
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Data scaling laws in imitation learning for robotic manipulation
Fanqi Lin, Yingdong Hu, Pingyue Sheng, Chuan Wen, Jiacheng You, and Yang Gao · 2024
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Open x-embodiment: Robotic learning datasets and rt-x models: Open x-embodiment collaboration 0
Abby O’Neill, Abdul Rehman, Abhiram Maddukuri, Abhishek Gupta, Abhishek Padalkar, Abraham Lee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, Ajinkya Jain, et al · 2024
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Consistency policy: Accelerated visuomotor policies via consistency distillation
Aaditya Prasad, Kevin Lin, Jimmy Wu, Linqi Zhou, and Jeannette Bohg · 2024
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Grounded sam: Assembling open-world models for diverse visual tasks
Tianhe Ren, Shilong Liu, Ailing Zeng, Jing Lin, Kunchang Li, He Cao, Jiayu Chen, Xinyu Huang, Yukang Chen, Feng Yan, et al · 2024
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What matters in learning from large-scale datasets for robot manipulation
Vaibhav Saxena, Matthew Bronars, Nadun Ranawaka Arachchige, Kuancheng Wang, Woo Chul Shin, Soroush Nasiriany, Ajay Mandlekar, and Danfei Xu · 2024
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Manibox: Enhancing spatial grasping generalization via scalable simulation data generation
Hengkai Tan, Xuezhou Xu, Chengyang Ying, Xinyi Mao, Songming Liu, Xingxing Zhang, Hang Su, and Jun Zhu · 2024
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Reconciling reality through simulation: A real-to-sim-to-real approach for robust manipulation
Marcel Torne, Anthony Simeonov, Zechu Li, April Chan, Tao Chen, Abhishek Gupta, and Pulkit Agrawal · 2024
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Instant policy: In-context imitation learning via graph diffusion
Vitalis Vosylius and Edward Johns · 2024
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Learning to manipulate anywhere: A visual generalizable framework for reinforcement learning
Zhecheng Yuan, Tianming Wei, Shuiqi Cheng, Gu Zhang, Yuanpei Chen, and Huazhe Xu · 2024
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Mink: Python inverse kinematics based on MuJoCo, July 2024
Kevin Zakka · 2024
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Aloha unleashed: A simple recipe for robot dexterity
Tony Z Zhao, Jonathan Tompson, Danny Driess, Pete Florence, Kamyar Ghasemipour, Chelsea Finn, and Ayzaan Wahid · 2024
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Point cloud matters: Rethinking the impact of different observation spaces on robot learning
Haoyi Zhu, Yating Wang, Di Huang, Weicai Ye, Wanli Ouyang, and Tong He · 2024
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