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Motion trajectories offer reliable references for physics-based motion learning but suffer from sparsity, particularly in regions that lack sufficient data coverage.
Maximum likelihood from incomplete data via the em algorithm
Arthur P Dempster, Nan M Laird, and Donald B Rubin · 1977
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Hierarchical spacetime control
Zicheng Liu, Steven J Gortler, and Michael F Cohen · 1994
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Motion signal processing
Armin Bruderlin and Lance Williams · 1995
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Fourier principles for emotion-based human figure animation
Munetoshi Unuma, Ken Anjyo, and Ryozo Takeuchi · 1995
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Interpolation synthesis of articulated figure motion
Douglas J Wiley and James K Hahn · 1997
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Verbs and adverbs: Multidimensional motion interpolation
Charles Rose, Michael F Cohen, and Bobby Bodenheimer · 1998
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The infinite gaussian mixture model
Carl Rasmussen · 1999
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The nonstochastic multiarmed bandit problem
Peter Auer, Nicolo Cesa-Bianchi, Yoav Freund, and Robert E Schapire · 2002
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Adapting wavelet compression to human motion capture clips
Philippe Beaudoin, Pierre Poulin, and Michiel van de Panne · 2007
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Central pattern generators for locomotion control in animals and robots: a review
Auke Jan Ijspeert · 2008
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Motion fields for interactive character locomotion
Yongjoon Lee, Kevin Wampler, Gilbert Bernstein, Jovan Popović, and Zoran Popović · 2010
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Continuous character control with low-dimensional embeddings
Sergey Levine, Jack M Wang, Alexis Haraux, Zoran Popović, and Vladlen Koltun · 2012
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The strategic student approach for life-long exploration and learning
Manuel Lopes and Pierre-Yves Oudeyer · 2012
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Motion graphs++ a compact generative model for semantic motion analysis and synthesis
Jianyuan Min and Jinxiang Chai · 2012
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Learning robot gait stability using neural networks as sensory feedback function for central pattern generators
Sébastien Gay, José Santos-Victor, and Auke Ijspeert · 2013
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Synchronization in complex networks of phase oscillators: A survey
Florian Dörfler and Francesco Bullo · 2014
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Embed to control: A locally linear latent dynamics model for control from raw images
Manuel Watter, Jost Springenberg, Joschka Boedecker, and Martin Riedmiller · 2015
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Deep spatial autoencoders for visuomotor learning
Chelsea Finn, Xin Yu Tan, Yan Duan, Trevor Darrell, Sergey Levine, and Pieter Abbeel · 2016
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Spectral style transfer for human motion between independent actions
M Ersin Yumer and Niloy J Mitra · 2016
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Phase-functioned neural networks for character control
Daniel Holden, Taku Komura, and Jun Saito · 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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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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Teacher algorithms for curriculum learning of deep rl in continuously parameterized environments
Rémy Portelas, Cédric Colas, Katja Hofmann, and Pierre-Yves Oudeyer · 2020
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Local motion phases for learning multi-contact character movements
Sebastian Starke, Yiwei Zhao, Taku Komura, and Kazi Zaman · 2020
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The mit humanoid robot: Design, motion planning, and control for acrobatic behaviors
Matthew Chignoli, Donghyun Kim, Elijah Stanger-Jones, and Sangbae Kim · 2021
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How to train your robot with deep reinforcement learning: lessons we have learned
Julian Ibarz, Jie Tan, Chelsea Finn, Mrinal Kalakrishnan, Peter Pastor, and Sergey Levine · 2021
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David Ha and Jürgen Schmidhuber · 2018
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Policies modulating trajectory generators
Atil Iscen, Ken Caluwaerts, Jie Tan, Tingnan Zhang, Erwin Coumans, Vikas Sindhwani, and Vincent Vanhoucke · 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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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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Drecon: data-driven responsive control of physics-based characters
Kevin Bergamin, Simon Clavet, Daniel Holden, and James Richard Forbes · 2019
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Towards learning to imitate from a single video demonstration
Glen Berseth, Florian Golemo, and Christopher Pal · 2019
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Dream to control: Learning behaviors by latent imagination
Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi · 2019
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Learning a family of motor skills from a single motion clip
Seyoung Lee, Sunmin Lee, Yongwoo Lee, and Jehee Lee · 2021
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Amp: Adversarial motion priors for stylized physics-based character control
Xue Bin Peng, Ze Ma, Pieter Abbeel, Sergey Levine, and Angjoo Kanazawa · 2021
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Learning robust perceptive locomotion for quadrupedal robots in the wild
Takahiro Miki, Joonho Lee, Jemin Hwangbo, Lorenz Wellhausen, Vladlen Koltun, and Marco Hutter · 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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Deepphase: Periodic autoencoders for learning motion phase manifolds
Sebastian Starke, Ian Mason, and Taku Komura · 2022
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Simper: Simple self-supervised learning of periodic targets
Yuzhe Yang, Xin Liu, Jiang Wu, Silviu Borac, Dina Katabi, Ming-Zher Poh, and Daniel McDuff · 2022
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Versatile skill control via self-supervised adversarial imitation of unlabeled mixed motions
Chenhao Li, Sebastian Blaes, Pavel Kolev, Marin Vlastelica, Jonas Frey, and Georg Martius · 2023
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Milad Shafiee, Guillaume Bellegarda, and Auke Ijspeert · 2023
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Motion in-betweening with phase manifolds
Paul Starke, Sebastian Starke, Taku Komura, and Frank Steinicke · 2023
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