“Terrain-adaptive locomotion skills using deep reinforcement learning”

  • ©Xuebin (Jason) Peng, Glen Berseth, and Michiel van de Panne

Conference:


Type:


Title:

    Terrain-adaptive locomotion skills using deep reinforcement learning

Session/Category Title: MOTION CONTROL


Presenter(s)/Author(s):


Moderator(s):



Abstract:


    Reinforcement learning offers a promising methodology for developing skills for simulated characters, but typically requires working with sparse hand-crafted features. Building on recent progress in deep reinforcement learning (DeepRL), we introduce a mixture of actor-critic experts (MACE) approach that learns terrain-adaptive dynamic locomotion skills using high-dimensional state and terrain descriptions as input, and parameterized leaps or steps as output actions. MACE learns more quickly than a single actor-critic approach and results in actor-critic experts that exhibit specialization. Additional elements of our solution that contribute towards efficient learning include Boltzmann exploration and the use of initial actor biases to encourage specialization. Results are demonstrated for multiple planar characters and terrain classes.

References:


    1. Assael, J.-A. M., Wahlström, N., Schön, T. B., and Deisenroth, M. P. 2015. Data-efficient learning of feedback policies from image pixels using deep dynamical models. arXiv preprint arXiv:1510.02173.Google Scholar
    2. Bullet, 2015. Bullet physics library, Dec. http://bulletphysics.org.Google Scholar
    3. Calinon, S., Kormushev, P., and Caldwell, D. G. 2013. Compliant skills acquisition and multi-optima policy search with em-based reinforcement learning. Robotics and Autonomous Systems 61, 4, 369–379. Google ScholarDigital Library
    4. Coros, S., Beaudoin, P., Yin, K. K., and van de Panne, M. 2008. Synthesis of constrained walking skills. ACM Trans. Graph. 27, 5, Article 113. Google ScholarDigital Library
    5. Coros, S., Beaudoin, P., and van de Panne, M. 2009. Robust task-based control policies for physics-based characters. ACM Transctions on Graphics 28, 5, Article 170. Google ScholarDigital Library
    6. Coros, S., Beaudoin, P., and van de Panne, M. 2010. Generalized biped walking control. ACM Transctions on Graphics 29, 4, Article 130. Google ScholarDigital Library
    7. Coros, S., Karpathy, A., Jones, B., Reveret, L., and van de Panne, M. 2011. Locomotion skills for simulated quadrupeds. ACM Transactions on Graphics 30, 4, Article 59. Google ScholarDigital Library
    8. da Silva, M., Abe, Y., and Popović, J. 2008. Interactive simulation of stylized human locomotion. ACM Trans. Graph. 27, 3, Article 82. Google ScholarDigital Library
    9. da Silva, M., Durand, F., and Popović, J. 2009. Linear bellman combination for control of character animation. ACM Trans. Graph. 28, 3, Article 82. Google ScholarDigital Library
    10. Doya, K., Samejima, K., Katagiri, K.-i., and Kawato, M. 2002. Multiple model-based reinforcement learning. Neural computation 14, 6, 1347–1369. Google ScholarDigital Library
    11. Faloutsos, P., van de Panne, M., and Terzopoulos, D. 2001. Composable controllers for physics-based character animation. In Proceedings of SIGGRAPH 2001, 251–260. Google ScholarDigital Library
    12. Featherstone, R. 2014. Rigid body dynamics algorithms. Springer. Google ScholarDigital Library
    13. Geijtenbeek, T., and Pronost, N. 2012. Interactive character animation using simulated physics: A state-of-the-art review. In Computer Graphics Forum, vol. 31, Wiley Online Library, 2492–2515. Google ScholarDigital Library
    14. Grzeszczuk, R., Terzopoulos, D., and Hinton, G. 1998. Neuroanimator: Fast neural network emulation and control of physics-based models. In Proc. ACM SIGGRAPH, ACM, 9–20. Google ScholarDigital Library
    15. Hansen, N. 2006. The cma evolution strategy: A comparing review. In Towards a New Evolutionary Computation, 75–102.Google Scholar
    16. Haruno, M., Wolpert, D. H., and Kawato, M. 2001. Mosaic model for sensorimotor learning and control. Neural computation 13, 10, 2201–2220. Google ScholarDigital Library
    17. Hausknecht, M., and Stone, P. 2015. Deep reinforcement learning in parameterized action space. arXiv preprint arXiv:1511.04143.Google Scholar
    18. Heess, N., Wayne, G., Silver, D., Lillicrap, T., Erez, T., and Tassa, Y. 2015. Learning continuous control policies by stochastic value gradients. In Advances in Neural Information Processing Systems, 2926–2934. Google ScholarDigital Library
    19. Hester, T., and Stone, P. 2013. Texplore: real-time sample-efficient reinforcement learning for robots. Machine Learning 90, 3, 385–429. Google ScholarDigital Library
    20. Hodgins, J. K., Wooten, W. L., Brogan, D. C., and O’Brien, J. F. 1995. Animating human athletics. In Proceedings of SIGGRAPH 1995, 71–78. Google ScholarDigital Library
    21. Jacobs, R. A., Jordan, M. I., Nowlan, S. J., and Hinton, G. E. 1991. Adaptive mixtures of local experts. Neural computation 3, 1, 79–87.Google Scholar
    22. Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T. 2014. Caffe: Convolutional architecture for fast feature embedding. In Proceedings of the ACM International Conference on Multimedia, ACM, New York, NY, USA, MM ’14, 675–678. Google ScholarDigital Library
    23. Laszlo, J., van de Panne, M., and Fiume, E. 1996. Limit cycle control and its application to the animation of balancing and walking. In Proc. ACM SIGGRAPH, 155–162. Google ScholarDigital Library
    24. Lee, J., and Lee, K. H. 2006. Precomputing avatar behavior from human motion data. Graphical Models 68, 2, 158–174. Google ScholarDigital Library
    25. Lee, Y., Lee, S. J., and Popović, Z. 2009. Compact character controllers. ACM Transctions on Graphics 28, 5, Article 169. Google ScholarDigital Library
    26. Lee, Y., Wampler, K., Bernstein, G., Popović, J., and Popović, Z. 2010. Motion fields for interactive character locomotion. ACM Transctions on Graphics 29, 6, Article 138. Google ScholarDigital Library
    27. Lee, Y., Kim, S., and Lee, J. 2010. Data-driven biped control. ACM Transctions on Graphics 29, 4, Article 129. Google ScholarDigital Library
    28. Levine, S., and Abbeel, P. 2014. Learning neural network policies with guided policy search under unknown dynamics. In Advances in Neural Information Processing Systems 27. 1071–1079. Google ScholarDigital Library
    29. Levine, S., and Koltun, V. 2014. Learning complex neural network policies with trajectory optimization. In Proceedings of the 31st International Conference on Machine Learning (ICML-14), 829–837.Google Scholar
    30. Levine, S., Wang, J. M., Haraux, A., Popović, Z., and Koltun, V. 2012. Continuous character control with low-dimensional embeddings. ACM Transactions on Graphics (TOG) 31, 4, 28. Google ScholarDigital Library
    31. Levine, S., Finn, C., Darrell, T., and Abbeel, P. 2015. End-to-end training of deep visuomotor policies. arXiv preprint arXiv:1504.00702. Google ScholarDigital Library
    32. Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., and Wierstra, D. 2015. Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971.Google Scholar
    33. Liu, L., Yin, K., va n d e Panne, M., and Guo, B. 2012. Terrain runner: control, parameterization, composition, and planning for highly dynamic motions. ACM Trans. Graph. 31, 6, 154. Google ScholarDigital Library
    34. Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. 2015. Human-level control through deep reinforcement learning. Nature 518, 7540, 529–533.Google Scholar
    35. Mordatch, I., and Todorov, E. 2014. Combining the benefits of function approximation and trajectory optimization. In Robotics: Science and Systems (RSS).Google Scholar
    36. Mordatch, I., de Lasa, M., and Hertzmann, A. 2010. Robust physics-based locomotion using low-dimensional planning. ACM Trans. Graph. 29, 4, Article 71. Google ScholarDigital Library
    37. Mordatch, I., Lowrey, K., Andrew, G., Popovic, Z., and Todorov, E. V. 2015. Interactive control of diverse complex characters with neural networks. In Advances in Neural Information Processing Systems, 3114–3122. Google ScholarDigital Library
    38. Muico, U., Lee, Y., Popović, J., and Popović, Z. 2009. Contact-aware nonlinear control of dynamic characters. ACM Trans. Graph. 28, 3, Article 81. Google ScholarDigital Library
    39. Muico, U., Popović, J., and Popović, Z. 2011. Composite control of physically simulated characters. ACM Trans. Graph. 30, 3, Article 16. Google ScholarDigital Library
    40. Nair, A., Srinivasan, P., Blackwell, S., Alcicek, C., Fearon, R., De Maria, A., Panneershelvam, V., Suley-man, M., Beattie, C., Petersen, S., et al. 2015. Massively parallel methods for deep reinforcement learning. arXiv preprint arXiv:1507.04296.Google Scholar
    41. Parisotto, E., Ba, J. L., and Salakhutdinov, R. 2015. Actor-mimic: Deep multitask and transfer reinforcement learning. arXiv preprint arXiv:1511.06342.Google Scholar
    42. Pastor, P., Kalakrishnan, M., Righetti, L., and Schaal, S. 2012. Towards associative skill memories. In Humanoid Robots (Humanoids), 2012 12th IEEE-RAS International Conference on, IEEE, 309–315.Google Scholar
    43. Peng, X. B., Berseth, G., and van de Panne, M. 2015. Dynamic terrain traversal skills using reinforcement learning. ACM Transactions on Graphics 34, 4. Google ScholarDigital Library
    44. Rusu, A. A., Colmenarejo, S. G., Gulcehre, C., Desjardins, G., Kirkpatrick, J., Pascanu, R., Mnih, V., Kavukcuoglu, K., and Hadsell, R. 2015. Policy distillation. arXiv preprint arXiv:1511.06295.Google Scholar
    45. Schaul, T., Quan, J., Antonoglou, I., and Silver, D. 2015. Prioritized experience replay. arXiv preprint arXiv:1511.05952.Google Scholar
    46. Schulman, J., Levine, S., Moritz, P., Jordan, M. I., and Abbeel, P. 2015. Trust region policy optimization. CoRR abs/1502.05477.Google Scholar
    47. Silver, D., Lever, G., Heess, N., Degris, T., Wierstra, D., and Riedmiller, M. 2014. Deterministic policy gradient algorithms. In ICML.Google Scholar
    48. Sok, K. W., Kim, M., and Lee, J. 2007. Simulating biped behaviors from human motion data. ACM Trans. Graph. 26, 3, Article 107. Google ScholarDigital Library
    49. Stadie, B. C., Levine, S., and Abbeel, P. 2015. Incentiviz-ing exploration in reinforcement learning with deep predictive models. arXiv preprint arXiv:1507.00814.Google Scholar
    50. Tan, J., Liu, K., and Turk, G. 2011. Stable proportional-derivative controllers. Computer Graphics and Applications, IEEE 31, 4, 34–44. Google ScholarDigital Library
    51. Tan, J., Gu, Y., Liu, C. K., and Turk, G. 2014. Learning bicycle stunts. ACM Transactions on Graphics (TOG) 33, 4, 50. Google ScholarDigital Library
    52. Treuille, A., Lee, Y., and Popović, Z. 2007. Near-optimal character animation with continuous control. ACM Transactions on Graphics (TOG) 26, 3, Article 7. Google ScholarDigital Library
    53. Uchibe, E., and Doya, K. 2004. Competitive-cooperative-concurrent reinforcement learning with importance sampling. In Proc. of International Conference on Simulation of Adaptive Behavior: From Animals and Animats, 287–296.Google Scholar
    54. van der Maaten, L., and Hinton, G. E. 2008. Visualizing high-dimensional data using t-sne. Journal of Machine Learning Research 9, 2579–2605.Google Scholar
    55. Van Hasselt, H., and Wiering, M. A. 2007. Reinforcement learning in continuous action spaces. In Approximate Dynamic Programming and Reinforcement Learning, 2007. ADPRL 2007. IEEE International Symposium on, IEEE, 272–279.Google Scholar
    56. Van Hasselt, H., Guez, A., and Silver, D. 2015. Deep reinforcement learning with double q-learning. arXiv preprint arXiv:1509.06461.Google Scholar
    57. Van Hasselt, H. 2012. Reinforcement learning in continuous state and action spaces. In Reinforcement Learning. Springer, 207–251.Google Scholar
    58. Wang, J. M., Fleet, D. J., and Hertzmann, A. 2009. Optimizing walking controllers. ACM Transctions on Graphics 28, 5, Article 168. Google ScholarDigital Library
    59. Wiering, M., and Van Hasselt, H. 2008. Ensemble algorithms in reinforcement learning. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on 38, 4, 930–936. Google ScholarDigital Library
    60. Ye, Y., and Liu, C. K. 2010. Optimal feedback control for character animation using an abstract model. ACM Trans. Graph. 29, 4, Article 74. Google ScholarDigital Library
    61. Yin, K., Loken, K., and van de Panne, M. 2007. Simbicon: Simple biped locomotion control. ACM Transctions on Graphics 26, 3, Article 105. Google ScholarDigital Library
    62. Yin, K., Coros, S., Beaudoin, P., and van de Panne, M. 2008. Continuation methods for adapting simulated skills. ACM Transctions on Graphics 27, 3, Article 81. Google ScholarDigital Library


ACM Digital Library Publication:



Overview Page: