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Human motion driven control (HMDC) is an effective approach for generating natural and compelling robot motions while preserving high-level semantics.
Madö king granzört, 1989
Shūji Iuchi · 1989
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Retargetting motion to new characters
Michael Gleicher · 1998
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A physically-based motion retargeting filter
Seyoon Tak and Hyeong-Seok Ko · 2005
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Pacific rim, 2013
Guillermo del Toro · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Interactive motion mapping for real-time character control
Helge Rhodin, James Tompkin, Kwang In Kim, Kiran Varanasi, Hans-Peter Seidel, and Christian Theobalt · 2014
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Karol Gregor, Danilo Jimenez Rezende, and Daan Wierstra · 2016
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Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 2016
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Learning dense correspondence via 3d-guided cycle consistency
Tinghui Zhou, Philipp Krahenbuhl, Mathieu Aubry, Qixing Huang, and Alexei A Efros · 2016
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Transfer learning of shared latent spaces between robots with similar kinematic structure
Brian Delhaisse, Domingo Esteban, Leonel Rozo, and Darwin Caldwell · 2017
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Unsupervised image-to-image translation networks
Ming-Yu Liu, Thomas Breuel, and Jan Kautz · 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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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
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Recycle-gan: Unsupervised video retargeting
Aayush Bansal, Shugao Ma, Deva Ramanan, and Yaser Sheikh · 2018
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Using simulation and domain adaptation to improve efficiency of deep robotic grasping
Konstantinos Bousmalis, Alex Irpan, Paul Wohlhart, Yunfei Bai, Matthew Kelcey, Mrinal Kalakrishnan, Laura Downs, Julian Ibarz, Peter Pastor, Kurt Konolige, et al · 2018
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Diversity is all you need: Learning skills without a reward function
Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, and Sergey Levine · 2018
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Learning to walk via deep reinforcement learning
Tuomas Haarnoja, Sehoon Ha, Aurick Zhou, Jie Tan, George Tucker, and Sergey Levine · 2018
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Cycada: Cycle-consistent adversarial domain adaptation
Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei Efros, and Trevor Darrell · 2018
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A variational u-net for motion retargeting
Hanyoung Jang, Byungjun Kwon, Moonwon Yu, Seong Uk Kim, and Jongmin Kim · 2018
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Time-contrastive networks: Self-supervised learning from video
Pierre Sermanet, Corey Lynch, Yevgen Chebotar, Jasmine Hsu, Eric Jang, Stefan Schaal, Sergey Levine, and Google Brain · 2018
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Ready player one, 2018
Steven Spielberg · 2018
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Genesis-rt: Generating synthetic images for training secondary real-world tasks
Gregory J Stein and Nicholas Roy · 2018
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Sim-to-real: Learning agile locomotion for quadruped robots
Jie Tan, Tingnan Zhang, Erwin Coumans, Atil Iscen, Yunfei Bai, Danijar Hafner, Steven Bohez, and Vincent Vanhoucke · 2018
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Neural kinematic networks for unsupervised motion retargetting
Learned motion matching
Daniel Holden, Oussama Kanoun, Maksym Perepichka, and Tiberiu Popa · 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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Character controllers using motion vaes
Hung Yu Ling, Fabio Zinno, George Cheng, and Michiel Van De Panne · 2020
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Learning agile robotic locomotion skills by imitating animals
Xue Bin Peng, Erwin Coumans, Tingnan Zhang, Tsang-Wei Edward Lee, Jie Tan, and Sergey Levine · 2020
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Rl-cyclegan: Reinforcement learning aware simulation-to-real
Kanishka Rao, Chris Harris, Alex Irpan, Sergey Levine, Julian Ibarz, and Mohi Khansari · 2020
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Learning cross-domain correspondence for control with dynamics cycle-consistency
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Ruben Villegas, Jimei Yang, Duygu Ceylan, and Honglak Lee · 2018
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Feedback control for cassie with deep reinforcement learning
Zhaoming Xie, Glen Berseth, Patrick Clary, Jonathan Hurst, and Michiel van de Panne · 2018
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Drecon: data-driven responsive control of physics-based characters
Kevin Bergamin, Simon Clavet, Daniel Holden, and James Richard Forbes · 2019
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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
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Using deep reinforcement learning to learn high-level policies on the atrias biped
Tianyu Li, Hartmut Geyer, Christopher G Atkeson, and Akshara Rai · 2019
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Amass: Archive of motion capture as surface shapes
Naureen Mahmood, Nima Ghorbani, Nikolaus F Troje, Gerard Pons-Moll, and Michael J Black · 2019
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Third-person visual imitation learning via decoupled hierarchical controller
Pratyusha Sharma, Deepak Pathak, and Abhinav Gupta · 2019
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Qiang Zhang, Tete Xiao, Alexei A Efros, Lerrel Pinto, and Xiaolong Wang · 2020
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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
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Human-to-robot imitation in the wild
Shikhar Bahl, Abhinav Gupta, and Deepak Pathak · 2022
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Ase: Large-scale reusable adversarial skill embeddings for physically simulated characters
Xue Bin Peng, Yunrong Guo, Lina Halper, Sergey Levine, and Sanja Fidler · 2022
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Learning to walk in minutes using massively parallel deep reinforcement learning
Nikita Rudin, David Hoeller, Philipp Reist, and Marco Hutter · 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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Doc: Differentiable optimal control for retargeting motions onto legged robots
Ruben Grandia, Farbod Farshidian, Espen Knoop, Christian Schumacher, Marco Hutter, and Moritz Bächer · 2023
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