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Humanoid control is an important research challenge offering avenues for integration into human-centric infrastructures and enabling physics-driven humanoid animations.
Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2001
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URL http://mocap.cs.cmu.edu/
CMU Graphics Lab Motion Capture Database, 2003 · 2003
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Tree-based batch mode reinforcement learning
Damien Ernst, Pierre Geurts, and Louis Wehenkel · 2005
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Scaling laws for autoregressive generative modeling
Tom Henighan, Jared Kaplan, Mor Katz, Mark Chen, Christopher Hesse, Jacob Jackson, Heewoo Jun, Tom B. Brown, Prafulla Dhariwal, Scott Gray, Chris Hallacy, Benjamin Mann, Alec Radford, Aditya Ramesh, Nick Ryder, Daniel M. Ziegler, John Schulman, Dario Amodei, and Sam McCandlish · 2010
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Batch reinforcement learning
Sascha Lange, Thomas Gabel, and Martin A. Riedmiller · 2012
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Human3.6M: Large scale datasets and predictive methods for 3D human sensing in natural environments
Catalin Ionescu, Dragos Papava, Vlad Olaru, and Cristian Sminchisescu · 2013
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Emergence of locomotion behaviours in rich environments
Nicolas Heess, Dhruva Tb, Srinivasan Sriram, Jay Lemmon, Josh Merel, Greg Wayne, Yuval Tassa, Tom Erez, Ziyu Wang, SM Eslami, et al · 2017
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Imitation learning: A survey of learning methods
Ahmed Hussein, Mohamed Medhat Gaber, Eyad Elyan, and Chrisina Jayne · 2017
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Learning human behaviors from motion capture by adversarial imitation
Josh Merel, Yuval Tassa, Dhruva TB, Sriram Srinivasan, Jay Lemmon, Ziyu Wang, Greg Wayne, and Nicolas Heess · 2017
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Model predictive path integral control: From theory to parallel computation
Grady Williams, Andrew Aldrich, and Evangelos A Theodorou · 2017
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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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Recurrent world models facilitate policy evolution
David Ha and Jürgen Schmidhuber · 2018
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DeepMimic: Example-guided deep reinforcement learning of physics-based character skills
Xue Bin Peng, Pieter Abbeel, Sergey Levine, and Michiel Van de Panne · 2018
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Temporal difference models: Model-free deep RL for model-based control
Vitchyr Pong, Shixiang Gu, Murtaza Dalal, and Sergey Levine · 2018
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Language2Pose: Natural language grounded pose forecasting
Chaitanya Ahuja and Louis-Philippe Morency · 2019
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Learning latent dynamics for planning from pixels
Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson · 2019
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When to trust your model: Model-based policy optimization
Michael Janner, Justin Fu, Marvin Zhang, and Sergey Levine · 2019
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Neural probabilistic motor primitives for humanoid control
Josh Merel, Leonard Hasenclever, Alexandre Galashov, Arun Ahuja, Vu Pham, Greg Wayne, Yee Whye Teh, and Nicolas Heess · 2019
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V-MPO: On-policy maximum a posteriori policy optimization for discrete and continuous control
H Francis Song, Abbas Abdolmaleki, Jost Tobias Springenberg, Aidan Clark, Hubert Soyer, Jack W Rae, Seb Noury, Arun Ahuja, Siqi Liu, Dhruva Tirumala, et al · 2019
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SOLAR: Deep structured representations for model-based reinforcement learning
Marvin Zhang, Sharad Vikram, Laura Smith, Pieter Abbeel, Matthew Johnson, and Sergey Levine · 2019
Cited alongside, same era.
Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
Cited alongside, same era.
Dream to control: Learning behaviors by latent imagination
Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi · 2020
Cited alongside, same era.
CoMic: Complementary task learning & mimicry for reusable skills
Training compute-optimal large language models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Jack W. Rae, Oriol Vinyals, and Laurent Sifre · 2022
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Planning with diffusion for flexible behavior synthesis
Michael Janner, Yilun Du, Joshua Tenenbaum, and Sergey Levine · 2022
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Offline reinforcement learning with implicit Q-learning
Ilya Kostrikov, Ashvin Nair, and Sergey Levine · 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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A generalist agent
Scott Reed, Konrad Zolna, Emilio Parisotto, Sergio Gomez Colmenarejo, Alexander Novikov, Gabriel Barth-Maron, Mai Gimenez, 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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Leonard Hasenclever, Fabio Pardo, Raia Hadsell, Nicolas Heess, and Josh Merel · 2020
Cited alongside, same era.
Offline reinforcement learning: Tutorial, review, and perspectives on open problems
Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu · 2020
Cited alongside, same era.
Mastering Atari, Go, chess and shogi by planning with a learned model
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, Timothy Lillicrap, and David Silver · 2020
Cited alongside, same era.
Discretizing continuous action space for on-policy optimization
Yunhao Tang and Shipra Agrawal · 2020
Cited alongside, same era.
Critic regularized regression
Ziyu Wang, Alexander Novikov, Konrad Żołna, Jost Tobias Springenberg, Scott Reed, Bobak Shahriari, Noah Siegel, Josh Merel, Caglar Gulcehre, Nicolas Heess, and Nando de Freitas · 2020
Cited alongside, same era.
Decision transformer: Reinforcement learning via sequence modeling
Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, and Igor Mordatch · 2021
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A minimalist approach to offline reinforcement learning
Scott Fujimoto and Shixiang Gu · 2021
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Mastering Atari with discrete world models
Danijar Hafner, Timothy Lillicrap, Mohammad Norouzi, and Jimmy Ba · 2021
Cited alongside, same era.
Later among the works it cites.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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MoCapAct: A multi-task dataset for simulated humanoid control
Nolan Wagener, Andrey Kolobov, Felipe Vieira Frujeri, Ricky Loynd, Ching-An Cheng, and Matthew Hausknecht · 2022
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Td-mpc2: Scalable, robust world models for continuous control
Anonymous Authors · 2023
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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, Alexander Herzog, Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Tomas Jackson, Sally Jesmonth, Nikhil J. Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kuang, Isabel Leal, Kuang-Huei Lee, Sergey Levine, Yao Lu, Utsav Malla, Deeksha Manjunath, Igor Mordatch, Ofir Nachum, Carolina Parada, Jodilyn Peralta, Emily Perez, Karl Pertsch, Jornell Quiambao, Kanishka Rao, Michael S. Ryoo, Grecia Salazar, Pannag R. Sanketi, Kevin Sayed, Jaspiar Singh, Sumedh Sontakke, Austin Stone, Clayton Tan, Huong T. Tran, Vincent Vanhoucke, Steve Vega, Quan Vuong, Fei Xia, Ted Xiao, Peng Xu, Sichun Xu, Tianhe Yu, and Brianna Zitkovich · 2023
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IQL-TD-MPC: Implicit Q-learning for hierarchical model predictive control
Rohan Chitnis, Yingchen Xu, Bobak Hashemi, Lucas Lehnert, Urun Dogan, Zheqing Zhu, and Olivier Delalleau · 2023
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C ⋅ \cdot ASE: Learning conditional adversarial skill embeddings for physics-based characters
Zhiyang Dou, Xuelin Chen, Qingnan Fan, Taku Komura, and Wenping Wang · 2023
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Mastering diverse domains through world models
Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, and Timothy Lillicrap · 2023
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Action-quantized offline reinforcement learning for robotic skill learning
Jianlan Luo, Perry Dong, Jeffrey Wu, Aviral Kumar, Xinyang Geng, and Sergey Levine · 2023
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SDXL: Improving latent diffusion models for high-resolution image synthesis
Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach · 2023
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A survey on offline reinforcement learning: Taxonomy, review, and open problems
Rafael Figueiredo Prudencio, Marcos ROA Maximo, and Esther Luna Colombini · 2023
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A generalist dynamics model for control
Ingmar Schubert, Jingwei Zhang, Jake Bruce, Sarah Bechtle, Emilio Parisotto, Martin A. Riedmiller, Jost Tobias Springenberg, Arunkumar Byravan, Leonard Hasenclever, and Nicolas Heess · 2023
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Human motion diffusion model
Guy Tevet, Sigal Raab, Brian Gordon, Yoni Shafir, Daniel Cohen-or, and Amit Haim Bermano · 2023
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