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The ability to reuse collected data and transfer trained policies between robots could alleviate the burden of additional data collection and training.
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Design and use paradigms for gazebo, an open-source multi-robot simulator
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An image inpainting technique based on the fast marching method
Alexandru Telea · 2004
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Autonomous shaping: Knowledge transfer in reinforcement learning
George Konidaris and Andrew Barto · 2006
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Transfer learning for reinforcement learning domains: A survey
Matthew E Taylor and Peter Stone · 2009
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Transfer learning across heterogeneous robots with action sequence mapping
Balaji Lakshmanan and Ravindran Balaraman · 2010
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Alignment-based transfer learning for robot models
Botond Bocsi, Lehel Csató, and Jan Peters · 2013
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Rapid transfer of controllers between uavs using learning-based adaptive control
Girish Chowdhary, Tongbin Wu, Mark Cutler, and Jonathan P How · 2013
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Unsupervised cross-domain transfer in policy gradient reinforcement learning via manifold alignment
Haitham Bou Ammar, Eric Eaton, Paul Ruvolo, and Matthew Taylor · 2015
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Knowledge transfer for learning robot models via local procrustes analysis
Ndivhuwo Makondo, Benjamin Rosman, and Osamu Hasegawa · 2015
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A preliminary study of transfer learning between unicycle robots
Kaizad V Raimalwala, Bruce A Francis, and Angela P Schoellig · 2016
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Learning modular neural network policies for multi-task and multi-robot transfer
Coline Devin, Abhishek Gupta, Trevor Darrell, Pieter Abbeel, and Sergey Levine · 2017
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Deep visual foresight for planning robot motion
Chelsea Finn and Sergey Levine · 2017
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Learning invariant feature spaces to transfer skills with reinforcement learning
Abhishek Gupta, Coline Devin, YuXuan Liu, Pieter Abbeel, and Sergey Levine · 2017
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Multi-robot transfer learning: A dynamical system perspective
Mohamed K Helwa and Angela P Schoellig · 2017
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Sim-to-real robot learning from pixels with progressive nets
Andrei A Rusu, Matej Večerík, Thomas Rothörl, Nicolas Heess, Razvan Pascanu, and Raia Hadsell · 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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Domain randomization for transferring deep neural networks from simulation to the real world
Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel · 2017
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Hardware conditioned policies for multi-robot transfer learning
Tao Chen, Adithyavairavan Murali, and Abhinav Gupta · 2018
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Jacquard: A large scale dataset for robotic grasp detection
Amaury Depierre, Emmanuel Dellandréa, and Liming Chen · 2018
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Visual foresight: Model-based deep reinforcement learning for vision-based robotic control
Frederik Ebert, Chelsea Finn, Sudeep Dasari, Annie Xie, Alex Lee, and Sergey Levine · 2018
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QT-Opt: 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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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
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Imitation from observation: Learning to imitate behaviors from raw video via context translation
YuXuan Liu, Abhishek Gupta, Pieter Abbeel, and Sergey Levine · 2018
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Accelerating model learning with inter-robot knowledge transfer
Ndivhuwo Makondo, Benjamin Rosman, and Osamu Hasegawa · 2018
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Graph networks as learnable physics engines for inference and control
Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller, Raia Hadsell, and Peter Battaglia · 2018
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Nervenet: Learning structured policy with graph neural networks
Tingwu Wang, Renjie Liao, Jimmy Ba, and Sanja Fidler · 2018
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One-shot imitation from observing humans via domain-adaptive meta-learning
Tianhe Yu, Chelsea Finn, Sudeep Dasari, Annie Xie, Tianhao Zhang, Pieter Abbeel, and Sergey Levine · 2018
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Robonet: Large-scale multi-robot learning
Sudeep Dasari, Frederik Ebert, Stephen Tian, Suraj Nair, Bernadette Bucher, Karl Schmeckpeper, Siddharth Singh, Sergey Levine, and Chelsea Finn · 2019
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Skill transfer in deep reinforcement learning under morphological heterogeneity
Yang Hu and Giovanni Montana · 2019
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Copy-and-paste networks for deep video inpainting
Sungho Lee, Seoung Wug Oh, DaeYeun Won, and Seon Joo Kim · 2019
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Zero-shot generalization using cascaded system-representations
Ashish Malik · 2019
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Learning to control self-assembling morphologies: a study of generalization via modularity
Deepak Pathak, Christopher Lu, Trevor Darrell, Phillip Isola, and Alexei A Efros · 2019
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Avid: Learning multi-stage tasks via pixel-level translation of human videos
Laura Smith, Nikita Dhawan, Marvin Zhang, Pieter Abbeel, and Sergey Levine · 2019
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Learning one-shot imitation from humans without humans
Alessandro Bonardi, Stephen James, and Andrew J Davison · 2020
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ACRONYM: A large-scale grasp dataset based on simulation
Clemens Eppner, Arsalan Mousavian, and Dieter Fox · 2020
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Hierarchically decoupled imitation for morphological transfer
Donald Hejna, Lerrel Pinto, and Pieter Abbeel · 2020
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One policy to control them all: Shared modular policies for agent-agnostic control
Wenlong Huang, Igor Mordatch, and Deepak Pathak · 2020
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Rlbench: The robot learning benchmark & learning environment
Stephen James, Zicong Ma, David Rovick Arrojo, and Andrew J Davison · 2020
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Domain adaptive imitation learning
Kuno Kim, Yihong Gu, Jiaming Song, Shengjia Zhao, and Stefano Ermon · 2020
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My body is a cage: the role of morphology in graph-based incompatible control
A generalist agent
Scott Reed, Konrad Zolna, Emilio Parisotto, Sergio Gómez Colmenarejo, Alexander Novikov, Gabriel Barth-maron, Mai Giménez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, Tom Eccles, Jake Bruce, Ali Razavi, Ashley Edwards, Nicolas Heess, Yutian Chen, Raia Hadsell, Oriol Vinyals, Mahyar Bordbar, and Nando de Freitas · 2022
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Translating robot skills: Learning unsupervised skill correspondences across robots
Tanmay Shankar, Yixin Lin, Aravind Rajeswaran, Vikash Kumar, Stuart Anderson, and Jean Oh · 2022
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Robotic telekinesis: Learning a robotic hand imitator by watching humans on youtube
Aravind Sivakumar, Kenneth Shaw, and Deepak Pathak · 2022
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Transfer rl across observation feature spaces via model-based regularization
Yanchao Sun, Ruijie Zheng, Xiyao Wang, Andrew Cohen, and Furong Huang · 2022
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Vitaly Kurin, Maximilian Igl, Tim Rocktäschel, Wendelin Boehmer, and Shimon Whiteson · 2020
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Learning agile robotic locomotion skills by imitating animals
Xue Bin Peng, Erwin Coumans, Tingnan Zhang, Tsang-Wei Lee, Jie Tan, and Sergey Levine · 2020
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Unigrasp: Learning a unified model to grasp with multifingered robotic hands
Lin Shao, Fabio Ferreira, Mikael Jorda, Varun Nambiar, Jianlan Luo, Eugen Solowjow, Juan Aparicio Ojea, Oussama Khatib, and Jeannette Bohg · 2020
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Learning cross-domain correspondence for control with dynamics cycle-consistency
Qiang Zhang, Tete Xiao, Alexei A Efros, Lerrel Pinto, and Xiaolong Wang · 2020
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robosuite: A modular simulation framework and benchmark for robot learning
Yuke Zhu, Josiah Wong, Ajay Mandlekar, Roberto Martín-Martín, Abhishek Joshi, Soroush Nasiriany, and Yifeng Zhu · 2020
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Learning generalizable robotic reward functions from “in-the-wild" human videos
Annie S Chen, Suraj Nair, and Chelsea Finn · 2021
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Policy transfer via kinematic domain randomization and adaptation
Ioannis Exarchos, Yifeng Jiang, Wenhao Yu, and C Karen Liu · 2021
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Tete Xiao, Ilija Radosavovic, Trevor Darrell, and Jitendra Malik · 2022
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Cross domain robot imitation with invariant representation
Zhao-Heng Yin, Lingfeng Sun, Hengbo Ma, Masayoshi Tomizuka, and Wu-Jun Li · 2022
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Xirl: Cross-embodiment inverse reinforcement learning
Kevin Zakka, Andy Zeng, Pete Florence, Jonathan Tompson, Jeannette Bohg, and Debidatta Dwibedi · 2022
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RoboAgent: Towards sample efficient robot manipulation with semantic augmentations and action chunking
Homanga Bharadhwaj, Jay Vakil, Mohit Sharma, Abhinav Gupta, Shubham Tulsiani, and Vikash Kumar · 2023
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Zero-shot robotic manipulation with pretrained image-editing diffusion models
Kevin Black, Mitsuhiko Nakamoto, Pranav Atreya, Homer Walke, Chelsea Finn, Aviral Kumar, and Sergey Levine · 2023
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Robocat: A self-improving foundation agent for robotic manipulation
Konstantinos Bousmalis, Giulia Vezzani, Dushyant Rao, Coline Devin, Alex X Lee, Maria Bauza, Todor Davchev, Yuxiang Zhou, Agrim Gupta, Akhil Raju, et al · 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
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PaLM-E: An embodied multimodal language model, 2023
Danny Driess, Fei Xia, Mehdi S. M. Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, Wenlong Huang, Yevgen Chebotar, Pierre Sermanet, Daniel Duckworth, Sergey Levine, Vincent Vanhoucke, Karol Hausman, Marc Toussaint, Klaus Greff, Andy Zeng, Igor Mordatch, and Pete Florence · 2023
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Ar2-d2: Training a robot without a robot
Jiafei Duan, Yi Ru Wang, Mohit Shridhar, Dieter Fox, and Ranjay Krishna · 2023
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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
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Exaug: Robot-conditioned navigation policies via geometric experience augmentation
Noriaki Hirose, Dhruv Shah, Ajay Sridhar, and Sergey Levine · 2023
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Policy stitching: Learning transferable robot policies
Pingcheng Jian, Easop Lee, Zachary Bell, Michael M Zavlanos, and Boyuan Chen · 2023
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VIMA: General robot manipulation with multimodal prompts
Yunfan Jiang, Agrim Gupta, Zichen Zhang, Guanzhi Wang, Yongqiang Dou, Yanjun Chen, Li Fei-Fei, Anima Anandkumar, Yuke Zhu, and Linxi Fan · 2023
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Interactive language: Talking to robots in real time
Corey Lynch, Ayzaan Wahid, Jonathan Tompson, Tianli Ding, James Betker, Robert Baruch, Travis Armstrong, and Pete Florence · 2023
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Mimicgen: A data generation system for scalable robot learning using human demonstrations
Ajay Mandlekar, Soroush Nasiriany, Bowen Wen, Iretiayo Akinola, Yashraj Narang, Linxi Fan, Yuke Zhu, and Dieter Fox · 2023
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Orbit: A unified simulation framework for interactive robot learning environments
Mayank Mittal, Calvin Yu, Qinxi Yu, Jingzhou Liu, Nikita Rudin, David Hoeller, Jia Lin Yuan, Ritvik Singh, Yunrong Guo, Hammad Mazhar, Ajay Mandlekar, Buck Babich, Gavriel State, Marco Hutter, and Animesh Garg · 2023
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Octo: An open-source generalist robot policy
Octo Model Team, Dibya Ghosh, Homer Walke, Karl Pertsch, Kevin Black, Oier Mees, Sudeep Dasari, Joey Hejna, Charles Xu, Jianlan Luo, Tobias Kreiman, You Liang Tan, Dorsa Sadigh, Chelsea Finn, and Sergey Levine · 2023
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Robot learning with sensorimotor pre-training
Ilija Radosavovic, Baifeng Shi, Letian Fu, Ken Goldberg, Trevor Darrell, and Jitendra Malik · 2023
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Bridging action space mismatch in learning from demonstrations
Gautam Salhotra, I Liu, Chun Arthur, and Gaurav Sukhatme · 2023
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A generalist dynamics model for control, 2023
Ingmar Schubert, Jingwei Zhang, Jake Bruce, Sarah Bechtle, Emilio Parisotto, Martin Riedmiller, Jost Tobias Springenberg, Arunkumar Byravan, Leonard Hasenclever, and Nicolas Heess · 2023
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On bringing robots home, 2023
Nur Muhammad Mahi Shafiullah, Anant Rai, Haritheja Etukuru, Yiqian Liu, Ishan Misra, Soumith Chintala, and Lerrel Pinto · 2023
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GNM: A general navigation model to drive any robot
Dhruv Shah, Ajay Sridhar, Arjun Bhorkar, Noriaki Hirose, and Sergey Levine · 2023
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Open-world object manipulation using pre-trained vision-language models
Austin Stone, Ted Xiao, Yao Lu, Keerthana Gopalakrishnan, Kuang-Huei Lee, Quan Vuong, Paul Wohlhart, Brianna Zitkovich, Fei Xia, Chelsea Finn, et al · 2023
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Bridgedata v2: A dataset for robot learning at scale, 2023
Homer Walke, Kevin Black, Abraham Lee, Moo Jin Kim, Max Du, Chongyi Zheng, Tony Zhao, Philippe Hansen-Estruch, Quan Vuong, Andre He, Vivek Myers, Kuan Fang, Chelsea Finn, and Sergey Levine · 2023
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Any-point trajectory modeling for policy learning
Chuan Wen, Xingyu Lin, John So, Kai Chen, Qi Dou, Yang Gao, and Pieter Abbeel · 2023
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Universal morphology control via contextual modulation
Zheng Xiong, Jacob Beck, and Shimon Whiteson · 2023
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XSkill: Cross embodiment skill discovery
Mengda Xu, Zhenjia Xu, Cheng Chi, Manuela Veloso, and Shuran Song · 2023
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Polybot: Training one policy across robots while embracing variability
Jonathan Yang, Dorsa Sadigh, and Chelsea Finn · 2023
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Multi-embodiment legged robot control as a sequence modeling problem
Chen Yu, Weinan Zhang, Hang Lai, Zheng Tian, Laurent Kneip, and Jun Wang · 2023
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Modularity through attention: Efficient training and transfer of language-conditioned policies for robot manipulation
Yifan Zhou, Shubham Sonawani, Mariano Phielipp, Simon Stepputtis, and Heni Amor · 2023
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Transfer learning in deep reinforcement learning: A survey
Zhuangdi Zhu, Kaixiang Lin, Anil K Jain, and Jiayu Zhou · 2023
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Meta-evolve: Continuous robot evolution for one-to-many policy transfer
Xingyu Liu, Deepak Pathak, and Ding Zhao · 2024
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