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Simulated humanoids are an appealing research domain due to their physical capabilities.
Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning
X. B. Peng, A. Kumar, G. Zhang, and S. Levine · 1910
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Carnegie Mellon University Graphics Lab Motion Capture Database
CMU · 2003
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Reinforcement Learning by Reward-Weighted Regression for Operational Space Control
J. Peters and S. Schaal · 2007
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MuJoCo: A Physics Engine for Model-Based Control
E. Todorov, T. Erez, and Y. Tassa · 2012
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Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments
C. Ionescu, D. Papava, V. Olaru, and C. Sminchisescu · 2013
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Adam: A Method for Stochastic Optimization
D. P. Kingma and J. Ba · 2015
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Deep Variational Information Bottleneck
A. A. Alemi, I. Fischer, J. V. Dillon, and K. Murphy · 2017
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Emergence of Locomotion Behaviours in Rich Environments
N. Heess, D. TB, S. Sriram, J. Lemmon, J. Merel, G. Wayne, Y. Tassa, T. Erez, Z. Wang, S. M. A. Eslami, M. Riedmiller, and D. Silver · 2017
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DART: Noise Injection for Robust Imitation Learning
M. Laskey, J. Lee, R. Fox, A. Dragan, and K. Goldberg · 2017
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Learning Human Behaviors from Motion Capture by Adversarial Imitation
J. Merel, Y. Tassa, D. TB, S. Srinivasan, J. Lemmon, Z. Wang, G. Wayne, and N. Heess · 2017
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Proximal Policy Optimization Algorithms
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
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Robust Imitation of Diverse Behaviors
Z. Wang, J. S. Merel, S. E. Reed, N. de Freitas, G. Wayne, and N. Heess · 2017
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Physics-Based Motion Capture Imitation With Deep Reinforcement Learning
N. Chentanez, M. Müller, M. Macklin, V. Makoviychuk, and S. Jeschke · 2018
Cited alongside, same era.
DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills
X. B. Peng, P. Abbeel, S. Levine, and M. van de Panne · 2018
Cited alongside, same era.
PyTorch Lightning
W. Falcon · 2019
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Learning Trajectory Dependencies for Human Motion Prediction
W. Mao, M. Liu, M. Salzmann, and H. Li · 2019
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Language Models Are Unsupervised Multitask Learners
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever · 2019
Cited alongside, same era.
Imitation Learning for Human Pose Prediction
B. Wang, E. Adeli, H.-k. Chiu, D.-A. Huang, and J. C. Niebles · 2019
Cited alongside, same era.
Residual Force Control for Agile Human Behavior Imitation and Extended Motion Synthesis
Y. Yuan and K. Kitani · 2020
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A Spatio-Temporal Transformer for 3D Human Motion Prediction
E. Aksan, M. Kaufmann, P. Cao, and O. Hilliges · 2021
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Decision Transformer: Reinforcement Learning via Sequence Modeling
L. Chen, K. Lu, A. Rajeswaran, K. Lee, A. Grover, M. Laskin, P. Abbeel, A. Srinivas, and I. Mordatch · 2021
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Offline Reinforcement Learning as One Big Sequence Modeling Problem
M. Janner, Q. Li, and S. Levine · 2021
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TrajeVAE: Controllable Human Motion Generation from Trajectories
K. Kania, M. Kowalski, and T. Trzciński · 2021
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D4RL: Datasets for Deep Data-Driven Reinforcement Learning
J. Fu, A. Kumar, O. Nachum, G. Tucker, and S. Levine · 2020
Cited alongside, same era.
Robust Motion In-Betweening
F. G. Harvey, M. Yurick, D. Nowrouzezahrai, and C. Pal · 2020
Cited alongside, same era.
CoMic: Complementary Task Learning & Mimicry for Reusable Skills
L. Hasenclever, F. Pardo, R. Hadsell, N. Heess, and J. Merel · 2020
Cited alongside, same era.
Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
S. Levine, A. Kumar, G. Tucker, and J. Fu · 2020
Cited alongside, same era.
Catch & Carry: Reusable Neural Controllers for Vision-Guided Whole-Body Tasks
J. Merel, S. Tunyasuvunakool, A. Ahuja, Y. Tassa, L. Hasenclever, V. Pham, T. Erez, G. Wayne, and N. Heess · 2020
Cited alongside, same era.
dm_control
S. Tunyasuvunakool, A. Muldal, Y. Doron, S. Liu, S. Bohez, J. Merel, T. Erez, T. Lillicrap, N. Heess, and Y. Tassa · 2020
Cited alongside, same era.
V. Makoviychuk, L. Wawrzyniak, Y. Guo, M. Lu, K. Storey, M. Macklin, D. Hoeller, N. Rudin, A. Allshire, A. Handa, and G. State · 2021
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Stable-Baselines3: Reliable Reinforcement Learning Implementations
A. Raffin, A. Hill, A. Gleave, A. Kanervisto, M. Ernestus, and N. Dormann · 2021
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Imitate and Repurpose: Learning Reusable Robot Movement Skills From Human and Animal Behaviors
S. Bohez, S. Tunyasuvunakool, P. Brakel, F. Sadeghi, L. Hasenclever, Y. Tassa, E. Parisotto, J. Humplik, T. Haarnoja, R. Hafner, M. Wulfmeier, M. Neunert, B. Moran, N. Siegel, A. Huber, F. Romano, N. Batchelor, F. Casarini, J. Merel, R. Hadsell, and N. Heess · 2022
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From Motor Control to Team Play in Simulated Humanoid Football
S. Liu, G. Lever, Z. Wang, J. Merel, S. M. A. Eslami, D. Hennes, W. M. Czarnecki, Y. Tassa, S. Omidshafiei, A. Abdolmaleki, N. Y. Siegel, L. Hasenclever, L. Marris, S. Tunyasuvunakool, H. F. Song, M. Wulfmeier, P. Muller, T. Haarnoja, B. D. Tracey, K. Tuyls, T. Graepel, and N. Heess · 2022
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A Survey on Deep Learning for Skeleton-Based Human Animation
L. Mourot, L. Hoyet, F. Le Clerc, F. Schnitzler, and P. Hellier · 2022
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ASE: Large-Scale Reusable Adversarial Skill Embeddings for Physically Simulated Characters
X. B. Peng, Y. Guo, L. Halper, S. Levine, and S. Fidler · 2022
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MotionCLIP: Exposing Human Motion Generation to CLIP Space
G. Tevet, B. Gordon, A. Hertz, A. H. Bermano, and D. Cohen-Or · 2022
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