“Discovering diverse athletic jumping strategies” by Yin, Yang, Panne and Yin

  • ©Zhiqi Yin, Zeshi Yang, Michiel van de Panne, and KangKang Yin

Conference:


Type(s):


Title:

    Discovering diverse athletic jumping strategies

Presenter(s)/Author(s):



Abstract:


    We present a framework that enables the discovery of diverse and natural-looking motion strategies for athletic skills such as the high jump. The strategies are realized as control policies for physics-based characters. Given a task objective and an initial character configuration, the combination of physics simulation and deep reinforcement learning (DRL) provides a suitable starting point for automatic control policy training. To facilitate the learning of realistic human motions, we propose a Pose Variational Autoencoder (P-VAE) to constrain the actions to a subspace of natural poses. In contrast to motion imitation methods, a rich variety of novel strategies can naturally emerge by exploring initial character states through a sample-efficient Bayesian diversity search (BDS) algorithm. A second stage of optimization that encourages novel policies can further enrich the unique strategies discovered. Our method allows for the discovery of diverse and novel strategies for athletic jumping motions such as high jumps and obstacle jumps with no motion examples and less reward engineering than prior work.

References:


    1. Joshua Achiam, Harrison Edwards, Dario Amodei, and Pieter Abbeel. 2018. Variational option discovery algorithms. arXiv preprint arXiv:1807.10299 (2018).Google Scholar
    2. V.M. Adashevskiy, S.S. Iermakov, and A.A. Marchenko. 2013. Biomechanics aspects of technique of high jump. Physical Education of Students 17, 2 (2013), 11–17.Google Scholar
    3. Shailen Agrawal, Shuo Shen, and Michiel van de Panne. 2013. Diverse Motion Variations for Physics-Based Character Animation. In Proceedings of the 12th ACM SIGGRAPH/Eurographics Symposium on Computer Animation. 37–44.Google Scholar
    4. Mazen Al Borno, Martin de Lasa, and Aaron Hertzmann. 2013. Trajectory Optimization for Full-Body Movements with Complex Contacts. TVCG 19, 8 (2013), 1405–1414.Google ScholarDigital Library
    5. Sheldon Andrews and Paul G. Kry. 2013. Goal directed multi-finger manipulation: Control policies and analysis. Computers Graphics 37, 7 (2013), 830 — 839.Google ScholarDigital Library
    6. Javad Azimi, Alan Fern, and Xiaoli Z Fern. 2010. Batch bayesian optimization via simulation matching. In Advances in Neural Information Processing Systems. Citeseer, 109–117.Google Scholar
    7. Kevin Bergamin, Simon Clavet, Daniel Holden, and James Forbes. 2019. DReCon: data-driven responsive control of physics-based characters. ACM Transctions on Graphics 38, 6, Article 206 (2019).Google Scholar
    8. Eric Brochu, Abhijeet Ghosh, and Nando de Freitas. 2007. Preference galleries for material design. SIGGRAPH Posters 105, 10.1145 (2007), 1280720–1280834.Google Scholar
    9. Jinxiang Chai and Jessica K Hodgins. 2005. Performance animation from low-dimensional control signals. In ACM SIGGRAPH 2005 Papers. 686–696.Google ScholarDigital Library
    10. Scott Shaobing Chen, David L Donoho, and Michael A Saunders. 2001. Atomic decomposition by basis pursuit. SIAM review 43, 1 (2001), 129–159.Google Scholar
    11. Matei Ciocarlie. 2010. Low-Dimensional Robotic Grasping: Eigengrasp Subspaces and Optimized Underactuation. Ph.D. Dissertation. Columbia University.Google Scholar
    12. Simon Clavet. 2016. Motion Matching and The Road to Next-Gen Animation. In GCD.Google Scholar
    13. Alexandra Coman and Hector Munoz-Avila. 2011. Generating diverse plans using quantitative and qualitative plan distance metrics. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 25.Google Scholar
    14. Edoardo Conti, Vashisht Madhavan, Felipe Petroski Such, Joel Lehman, Kenneth Stanley, and Jeff Clune. 2018. Improving exploration in evolution strategies for deep reinforcement learning via a population of novelty-seeking agents. In Advances in neural information processing systems. 5027–5038.Google ScholarDigital Library
    15. Stelian Coros, Philippe Beaudoin, and Michiel van de Panne. 2010. Generalized Biped Walking Control. ACM Transctions on Graphics 29, 4 (2010), Article 130.Google Scholar
    16. Erwin Coumans and Yunfei Bai. 2016–2019. PyBullet, a Python module for physics simulation for games, robotics and machine learning. http://pybullet.org.Google Scholar
    17. Thomas M Cover. 1999. Elements of information theory. John Wiley & Sons.Google Scholar
    18. Ana Lucia Cruz Ruiz, Charles Pontonnier, Jonathan Levy, and Georges Dumont. 2017. A synergy-based control solution for overactuated characters: Application to throwing. Computer Animation and Virtual Worlds 28, 6 (2017), e1743.Google ScholarCross Ref
    19. Marco da Silva, Yeuhi Abe, and Jovan Popović. 2008. Interactive simulation of stylized human locomotion. In ACM SIGGRAPH 2008 papers. 1–10.Google ScholarDigital Library
    20. Jesus Dapena. 2002. The evolution of high jumping technique: Biomechanical analysis.Google Scholar
    21. Martin de Lasa, Igor Mordatch, and Aaron Hertzmann. 2010. Feature-based locomotion controllers. In ACM Transactions on Graphics (TOG), Vol. 29. ACM, 131.Google ScholarDigital Library
    22. Sean Donnelly. 2014. An Introduction to the High Jump.Google Scholar
    23. Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, and Sergey Levine. 2019. Diversity is All You Need: Learning Skills without a Reward Function. In ICLR.Google Scholar
    24. Martin L Felis and Katja Mombaur. 2016. Synthesis of full-body 3D human gait using optimal control methods. In 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 1560–1566.Google ScholarDigital Library
    25. Peter Frazier, Warren Powell, and Savas Dayanik. 2009. The knowledge-gradient policy for correlated normal beliefs. INFORMS journal on Computing 21, 4 (2009), 599–613.Google ScholarCross Ref
    26. Thomas Geijtenbeek, Michiel Van De Panne, and A Frank Van Der Stappen. 2013. Flexible muscle-based locomotion for bipedal creatures. ACM Transactions on Graphics (TOG) 32, 6 (2013), 1–11.Google ScholarDigital Library
    27. F Sebastian Grassia. 1998. Practical parameterization of rotations using the exponential map. Journal of graphics tools 3, 3 (1998), 29–48.Google ScholarDigital Library
    28. Sehoon Ha and C Karen Liu. 2014. Iterative training of dynamic skills inspired by human coaching techniques. ACM Transactions on Graphics (TOG) 34, 1 (2014), 1–11.Google ScholarDigital Library
    29. Sehoon Ha, Yuting Ye, and C Karen Liu. 2012. Falling and landing motion control for character animation. ACM Transactions on Graphics (TOG) 31, 6 (2012), 1–9.Google ScholarDigital Library
    30. Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine. 2018. Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. arXiv preprint arXiv:1801.01290 (2018).Google Scholar
    31. Ikhsanul Habibie, Daniel Holden, Jonathan Schwarz, Joe Yearsley, and Taku Komura. 2017. A recurrent variational autoencoder for human motion synthesis. In 28th British Machine Vision Conference.Google ScholarCross Ref
    32. Emmanuel Hebrard, Brahim Hnich, Barry O’Sullivan, and Toby Walsh. 2005. Finding diverse and similar solutions in constraint programming. In AAAI, Vol. 5. 372–377.Google Scholar
    33. Nicolas Heess, Dhruva TB, Srinivasan Sriram, Jay Lemmon, Josh Merel, Greg Wayne, Yuval Tassa, Tom Erez, Ziyu Wang, SM Eslami, et al. 2017. Emergence of locomotion behaviours in rich environments. ArXiv abs/1707.02286 (2017).Google Scholar
    34. Nicolas Heess, Gregory Wayne, David Silver, Tim Lillicrap, Tom Erez, and Yuval Tassa. 2015. Learning continuous control policies by stochastic value gradients. In Advances in Neural Information Processing Systems. 2944–2952.Google Scholar
    35. Todd Hester and Peter Stone. 2017. Intrinsically motivated model learning for developing curious robots. Artificial Intelligence 247 (2017), 170–186.Google ScholarDigital Library
    36. Irina Higgins, Loïc Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner. 2017. beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net.Google Scholar
    37. J. K. Hodgins, W. L. Wooten, D. C. Brogan, and J. F. O’Brien. 1995. Animating Human Athletics. In Proceedings of SIGGRAPH 1995. 71–78.Google ScholarDigital Library
    38. Daniel Holden, Taku Komura, and Jun Saito. 2017. Phase-functioned Neural Networks for Character Control. ACM Transctions on Graphics 36, 4, Article 42 (2017).Google Scholar
    39. Daniel Holden, Jun Saito, and Taku Komura. 2016. A deep learning framework for character motion synthesis and editing. ACM Transctions on Graphics 35, 4 (2016), Article 138.Google Scholar
    40. Rein Houthooft, Xi Chen, Yan Duan, John Schulman, Filip De Turck, and Pieter Abbeel. 2016. Vime: Variational information maximizing exploration. Advances in neural information processing systems 29 (2016), 1109–1117.Google ScholarDigital Library
    41. Atil Iscen, Ken Caluwaerts, Jie Tan, Tingnan Zhang, Erwin Coumans, Vikas Sindhwani, and Vincent Vanhoucke. 2018. Policies Modulating Trajectory Generators. In Proceedings of The 2nd Conference on Robot Learning. PMLR 87:916–926.Google Scholar
    42. Sumit Jain, Yuting Ye, and C Karen Liu. 2009. Optimization-based interactive motion synthesis. ACM Transactions on Graphics (TOG) 28, 1 (2009), 1–12.Google ScholarDigital Library
    43. Donald R. Jones. 2001. Direct global optimization algorithmDirect Global Optimization Algorithm. Springer US, Boston, MA, 431–440.Google Scholar
    44. Donald R Jones, Matthias Schonlau, and William J Welch. 1998. Efficient global optimization of expensive black-box functions. Journal of Global optimization 13, 4 (1998), 455–492.Google ScholarDigital Library
    45. Teen Jumper. 2020. 7 Classic High Jump Styles. https://teenjumper.com/2020/01/04/7-classic-high-jump-styles-and-how-to-do-them/Google Scholar
    46. Kirthevasan Kandasamy, Willie Neiswanger, Jeff Schneider, Barnabas Poczos, and Eric P Xing. 2018. Neural architecture search with bayesian optimisation and optimal transport. In Advances in neural information processing systems. 2016–2025.Google Scholar
    47. Kirthevasan Kandasamy, Karun Raju Vysyaraju, Willie Neiswanger, Biswajit Paria, Christopher R Collins, Jeff Schneider, Barnabas Poczos, and Eric P Xing. 2020. Tuning hyperparameters without grad students: Scalable and robust bayesian optimisation with dragonfly. Journal of Machine Learning Research 21, 81 (2020), 1–27.Google Scholar
    48. Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).Google Scholar
    49. Aaron Klein, Stefan Falkner, Simon Bartels, Philipp Hennig, and Frank Hutter. 2017. Fast bayesian optimization of machine learning hyperparameters on large datasets. In Artificial Intelligence and Statistics. PMLR, 528–536.Google Scholar
    50. Ksenia Korovina, Sailun Xu, Kirthevasan Kandasamy, Willie Neiswanger, Barnabas Poczos, Jeff Schneider, and Eric Xing. 2020. Chembo: Bayesian optimization of small organic molecules with synthesizable recommendations. In International Conference on Artificial Intelligence and Statistics. PMLR, 3393–3403.Google Scholar
    51. Lucas Kovar, Michael Gleicher, and Frédéric Pighin. 2002. Motion Graphs. ACM Transctions on Graphics 21, 3 (2002), 473–482.Google ScholarDigital Library
    52. Yuki Koyama, Issei Sato, and Masataka Goto. 2020. Sequential gallery for interactive visual design optimization. ACM Transactions on Graphics 39, 4 (Jul 2020). Google ScholarDigital Library
    53. Yuki Koyama, Issei Sato, Daisuke Sakamoto, and Takeo Igarashi. 2017. Sequential line search for efficient visual design optimization by crowds. ACM Transactions on Graphics (TOG) 36, 4 (2017), 1–11.Google ScholarDigital Library
    54. Kyungho Lee, Seyoung Lee, and Jehee Lee. 2018. Interactive character animation by learning multi-objective control. ACM Transactions on Graphics (TOG) 37, 6 (2018), 1–10.Google ScholarDigital Library
    55. Yoonsang Lee, Sungeun Kim, and Jehee Lee. 2010. Data-driven biped control. In ACM SIGGRAPH 2010 papers. 1–8.Google ScholarDigital Library
    56. Yoonsang Lee, Moon Seok Park, Taesoo Kwon, and Jehee Lee. 2014. Locomotion control for many-muscle humanoids. ACM Transactions on Graphics (TOG) 33, 6 (2014), 1–11.Google ScholarDigital Library
    57. Joel Lehman and Kenneth O Stanley. 2011. Novelty search and the problem with objectives. In Genetic programming theory and practice IX. Springer, 37–56.Google Scholar
    58. Sergey Levine, Jack M Wang, Alexis Haraux, Zoran Popović, and Vladlen Koltun. 2012. Continuous character control with low-dimensional embeddings. ACM Transactions on Graphics (TOG) 31, 4 (2012), 1–10.Google ScholarDigital Library
    59. Hung Yu Ling, Fabio Zinno, George Cheng, and Michiel van de Panne. 2020. Character Controllers Using Motion VAEs. ACM Trans. Graph. 39, 4 (2020).Google ScholarDigital Library
    60. Dong C Liu and Jorge Nocedal. 1989. On the limited memory BFGS method for large scale optimization. Mathematical programming 45, 1-3 (1989), 503–528.Google ScholarDigital Library
    61. Libin Liu and Jessica Hodgins. August 2018. Learning Basketball Dribbling Skills Using Trajectory Optimization and Deep Reinforcement Learning. ACM Transactions on Graphics 37, 4 (August 2018).Google ScholarDigital Library
    62. Libin Liu, Michiel van de Panne, and KangKang Yin. 2016. Guided Learning of Control Graphs for Physics-based Characters. ACM Transctions on Graphics 35, 3 (2016), Article 29.Google Scholar
    63. Libin Liu, KangKang Yin, and Baining Guo. 2015. Improving Sampling-based Motion Control. Computer Graphics Forum 34, 2 (2015), 415–423.Google ScholarDigital Library
    64. Libin Liu, KangKang Yin, Michiel van de Panne, Tianjia Shao, and Weiwei Xu. 2010. Sampling-based contact-rich motion control. ACM Transctions on Graphics 29, 4, Article 128 (2010).Google Scholar
    65. Li-Ke Ma, Zeshi Yang, Baining Guo, and KangKang Yin. 2019. Towards Robust Direction Invariance in Character Animation. Computer Graphics Forum 38, 7 (2019), 1–8.Google ScholarDigital Library
    66. Li-ke Ma, Zeshi Yang, Xin Tong, Baining Guo, and Kangkang Yin. 2021. Learning and Exploring Motor Skills with Spacetime Bounds. Computer Graphics Forum (2021).Google Scholar
    67. B Matérn. 1960. Spatial variation: Meddelanden fran statens skogsforskningsinstitut. Lecture Notes in Statistics 36 (1960), 21.Google Scholar
    68. Paul Merrell, Eric Schkufza, Zeyang Li, Maneesh Agrawala, and Vladlen Koltun. 2011. Interactive furniture layout using interior design guidelines. ACM transactions on graphics (TOG) 30, 4 (2011), 1–10.Google Scholar
    69. Igor Mordatch, Kendall Lowrey, Galen Andrew, Zoran Popović, and Emanuel V Todorov. 2015. Interactive Control of Diverse Complex Characters with Neural Networks. In Advances in Neural Information Processing Systems. 3114–3122.Google Scholar
    70. Igor Mordatch, Emanuel Todorov, and Zoran Popović. 2012. Discovery of complex behaviors through contact-invariant optimization. ACM SIGGRAPH 31, 4 (2012), Article 43.Google Scholar
    71. Igor Mordatch, Jack M Wang, Emanuel Todorov, and Vladlen Koltun. 2013. Animating human lower limbs using contact-invariant optimization. ACM Transactions on Graphics (TOG) 32, 6 (2013), 1–8.Google ScholarDigital Library
    72. Uldarico Muico, Yongjoon Lee, Jovan Popović, and Zoran Popović. 2009. Contact-aware nonlinear control of dynamic characters. In ACM SIGGRAPH 2009 papers. 1–9.Google ScholarDigital Library
    73. K. Okuyama, M. Ae, and T. Yokozawa. 2003. Three dimensional joint torque of the takeoff leg in the Fosbury flop style. In Proceedings of the XIXth Congress of the International Society of the Biomechanics (CD-ROM).Google Scholar
    74. T. Osa, J. Peters, and G. Neumann. 2018. Hierarchical Reinforcement Learning of Multiple Grasping Strategies with Human Instructions. 18 (2018), 955–968.Google Scholar
    75. SA Overduin, A d’Avella, J. Roh, JM Carmena, and E. Bizzi. 2015. Representation of Muscle Synergies in the Primate Brain. Journal of Neuroscience 37 (2015). Issue 35.Google Scholar
    76. Soohwan Park, Hoseok Ryu, Seyoung Lee, Sunmin Lee, and Jehee Lee. 2019. Learning Predict-and-Simulate Policies From Unorganized Human Motion Data. ACM Transctions on Graphics 38, 6, Article 205 (2019).Google Scholar
    77. Xue Bin Peng, Pieter Abbeel, Sergey Levine, and Michiel van de Panne. 2018a. DeepMimic: Example-guided Deep Reinforcement Learning of Physics-based Character Skills. ACM Transctions on Graphics 37, 4, Article 143 (2018).Google Scholar
    78. Xue Bin Peng, Glen Berseth, KangKang Yin, and Michiel van de Panne. 2017. DeepLoco: Dynamic Locomotion Skills Using Hierarchical Deep Reinforcement Learning. ACM Transactions on Graphics (Proc. SIGGRAPH 2017) 36, 4 (2017).Google Scholar
    79. Xue Bin Peng, Michael Chang, Grace Zhang, Pieter Abbeel, and Sergey Levine. 2019. MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies. In Advances in Neural Information Processing Systems 32. Curran Associates, Inc., 3681–3692.Google Scholar
    80. Xue Bin Peng, Angjoo Kanazawa, Jitendra Malik, Pieter Abbeel, and Sergey Levine. 2018b. SFV: Reinforcement Learning of Physical Skills from Videos. ACM Transctions on Graphics 37, 6, Article 178 (2018).Google ScholarDigital Library
    81. Justin K Pugh, Lisa B Soros, and Kenneth O Stanley. 2016. Quality diversity: A new frontier for evolutionary computation. Frontiers in Robotics and AI 3 (2016), 40.Google Scholar
    82. PyTorch. 2018. PyTorch. https://pytorch.org/.Google Scholar
    83. Avinash Ranganath, Pei Xu, Ioannis Karamouzas, and Victor Zordan. 2019. Low Dimensional Motor Skill Learning Using Coactivation. In Motion, Interaction and Games. 1–10.Google Scholar
    84. Carl Edward Rasmussen. 2003. Gaussian processes in machine learning. In Summer School on Machine Learning. Springer, 63–71.Google Scholar
    85. Alla Safonova and Jessica K. Hodgins. 2007. Construction and Optimal Search of Interpolated Motion Graphs. ACM Transctions on Graphics 26, 3 (2007), 106–es.Google ScholarDigital Library
    86. Alla Safonova, Jessica K Hodgins, and Nancy S Pollard. 2004. Synthesizing physically realistic human motion in low-dimensional, behavior-specific spaces. ACM Transctions on Graphics 23, 3 (2004), 514–521.Google ScholarDigital Library
    87. Jürgen Schmidhuber. 1991. Curious model-building control systems. In Proc. international joint conference on neural networks. 1458–1463.Google ScholarCross Ref
    88. John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel. 2016. High-Dimensional Continuous Control Using Generalized Advantage Estimation. In Proceedings of the International Conference on Learning Representations (ICLR).Google Scholar
    89. John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017. Proximal Policy Optimization Algorithms. CoRR abs/1707.06347 (2017).Google Scholar
    90. Moonseok Park Seunghwan Lee, Kyoungmin Lee and Jehee Lee. 2019. Scalable Muscle-actuated Human Simulation and Control. ACM Transactions on Graphics (Proc. SIGGRAPH 2019) 38, 4 (2019).Google Scholar
    91. Archit Sharma, Shixiang Gu, Sergey Levine, Vikash Kumar, and Karol Hausman. 2019. Dynamics-aware unsupervised discovery of skills. arXiv preprint arXiv:1907.01657 (2019).Google Scholar
    92. Jasper Snoek, Hugo Larochelle, and Ryan P Adams. 2012. Practical bayesian optimization of machine learning algorithms. In Advances in neural information processing systems. 2951–2959.Google Scholar
    93. Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Mostofa Patwary, Mr Prabhat, and Ryan Adams. 2015. Scalable bayesian optimization using deep neural networks. In International conference on machine learning. 2171–2180.Google Scholar
    94. Kwang Won Sok, Manmyung Kim, and Jehee Lee. 2007. Simulating biped behaviors from human motion data. In ACM SIGGRAPH 2007 papers. 107–es.Google ScholarDigital Library
    95. Jialin Song, Yuxin Chen, and Yisong Yue. 2019. A general framework for multi-fidelity bayesian optimization with gaussian processes. In The 22nd International Conference on Artificial Intelligence and Statistics. PMLR, 3158–3167.Google Scholar
    96. Niranjan Srinivas, Andreas Krause, Sham Kakade, and Matthias Seeger. 2010. Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design (ICML’10). Omnipress, Madison, WI, USA, 1015–1022.Google Scholar
    97. Biplav Srivastava, Tuan Anh Nguyen, Alfonso Gerevini, Subbarao Kambhampati, Minh Binh Do, and Ivan Serina. 2007. Domain Independent Approaches for Finding Diverse Plans.. In IJCAI. 2016–2022.Google ScholarDigital Library
    98. Sebastian Starke, He Zhang, Taku Komura, and Jun Saito. 2019. Neural state machine for character-scene interactions. ACM Trans. Graph. 38, 6 (2019), 209–1.Google ScholarDigital Library
    99. Sebastian Starke, Yiwei Zhao, Taku Komura, and Kazi Zaman. 2020. Local motion phases for learning multi-contact character movements. ACM Transactions on Graphics (TOG) 39, 4 (2020), 54–1.Google ScholarDigital Library
    100. Hao Sun, Zhenghao Peng, Bo Dai, Jian Guo, Dahua Lin, and Bolei Zhou. 2020. Novel Policy Seeking with Constrained Optimization. arXiv:2005.10696Google Scholar
    101. Jie Tan, Karen Liu, and Greg Turk. 2011. Stable Proportional-Derivative Controllers. IEEE Computer Graphics and Applications 31, 4 (2011), 34–44.Google ScholarDigital Library
    102. Robert Tibshirani. 1996. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological) 58, 1 (1996), 267–288.Google ScholarCross Ref
    103. Rasmus K Ursem. 2002. Diversity-guided evolutionary algorithms. In International Conference on Parallel Problem Solving from Nature. Springer, 462–471.Google ScholarDigital Library
    104. Mark van der Wilk, Vincent Dutordoir, ST John, Artem Artemev, Vincent Adam, and James Hensman. 2020. A Framework for Interdomain and Multioutput Gaussian Processes. arXiv:2003.01115 (2020).Google Scholar
    105. Jack M Wang, David J Fleet, and Aaron Hertzmann. 2009. Optimizing walking controllers. In ACM SIGGRAPH Asia 2009 papers. 1–8.Google Scholar
    106. Jack M Wang, Samuel R Hamner, Scott L Delp, and Vladlen Koltun. 2012. Optimizing locomotion controllers using biologically-based actuators and objectives. ACM Transactions on Graphics (TOG) 31, 4 (2012), 1–11.Google ScholarDigital Library
    107. Jungdam Won, Deepak Gopinath, and Jessica Hodgins. 2020. A scalable approach to control diverse behaviors for physically simulated characters. ACM Transactions on Graphics (TOG) 39, 4 (2020), Article 33.Google ScholarDigital Library
    108. Wayne Lewis Wooten. 1998. Simulation of Leaping, Tumbling, Landing, and Balancing Humans. Ph.D. Dissertation. USA. Advisor(s) Hodgins, Jessica K. AAI9827367.Google ScholarDigital Library
    109. Zhaoming Xie, Hung Yu Ling, Nam Hee Kim, and Michiel van de Panne. 2020. ALLSTEPS: Curriculum-driven Learning of Stepping Stone Skills. In Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation.Google Scholar
    110. Yuting Ye and C Karen Liu. 2010a. Optimal feedback control for character animation using an abstract model. In ACM SIGGRAPH 2010 papers. 1–9.Google ScholarDigital Library
    111. Yuting Ye and C Karen Liu. 2010b. Synthesis of responsive motion using a dynamic model. In Computer Graphics Forum, Vol. 29. Wiley Online Library, 555–562.Google Scholar
    112. KangKang Yin, Kevin Loken, and Michiel van de Panne. 2007. SIMBICON: Simple Biped Locomotion Control. ACM Transctions on Graphics 26, 3 (2007), Article 105.Google ScholarDigital Library
    113. Wenhao Yu, Greg Turk, and C Karen Liu. 2018. Learning symmetric and low-energy locomotion. ACM Transactions on Graphics 37, 4, Article 144 (2018).Google ScholarDigital Library
    114. He Zhang, Sebastian Starke, Taku Komura, and Jun Saito. 2018. Mode-adaptive neural networks for quadruped motion control. ACM Transctions on Graphics 37, 4, Article 145 (2018).Google Scholar
    115. Yunbo Zhang, Wenhao Yu, and Greg Turk. 2019. Learning novel policies for tasks. arXiv preprint arXiv:1905.05252 (2019).Google Scholar
    116. K. Zhao, Z. Zhang, H. Wen, Z. Wang, and J. Wu. 2019. Modular Organization of Muscle Synergies to Achieve Movement Behaviors. Journal of Healthcare Engineering (2019).Google Scholar
    117. Peng Zhao and Michiel van de Panne. 2005. User Interfaces for Interactive Control of Physics-based 3D Characters. In I3D: ACM SIGGRAPH 2005 Symposium on Interactive 3D Graphics and Games.Google Scholar
    118. Yi Zhou, Connelly Barnes, Jingwan Lu, Jimei Yang, and Hao Li. 2019. On the continuity of rotation representations in neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 5745–5753.Google ScholarCross Ref


ACM Digital Library Publication:



Overview Page: