DeepLoco: Dynamic Locomotion Skills
Using Hierarchical Deep Reinforcement Learning


Transactions on Graphics (Proc. ACM SIGGRAPH 2017)

Xue Bin Peng (1)     Glen Berseth (1)     KangKang Yin (2)     Michiel van de Panne (1)
(1)University of British Columbia
(2)National University of Singapore  

     

Abstract

Learning physics-based locomotion skills is a difficult problem, leading to solutions that typically exploit prior knowledge of various forms. In this paper we aim to learn a variety of environment-aware locomotion skills with a limited amount of prior knowledge. We adopt a two-level hierarchical control framework. First, low-level controllers are learned that operate at a fine timescale and which achieve robust walking gaits that satisfy stepping-target and style objectives. Second, high-level controllers are then learned which plan at the timescale of steps by invoking desired step targets for the low-level controller. The high-level controller makes decisions directly based on high-dimensional inputs, including terrain maps or other suitable representations of the surroundings. Both levels of the control policy are trained using deep reinforcement learning. Results are demonstrated on a simulated 3D biped. Low-level controllers are learned for a variety of motion styles and demonstrate robustness with respect to force-based disturbances, terrain variations, and style interpolation. High-level controllers are demonstrated that are capable of following trails through terrains, dribbling a soccer ball towards a target location, and navigating through static or dynamic obstacles.

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Errata
Algorithm 1, line 18: The last term should be removed, so that it implements eqn (5).
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Other Projects
Terrain-adaptive locomotion     Guided Learning     Muscle-based bipeds     more...
Bibtex
@article{2017-TOG-deepLoco,
  title={DeepLoco: Dynamic Locomotion Skills Using Hierarchical Deep Reinforcement Learning},
  author={Xue Bin Peng and Glen Berseth and KangKang Yin and Michiel van de Panne},
  journal = {ACM Transactions on Graphics (Proc. SIGGRAPH 2017)},
  volume = 36,
  number = 4,
  article = 41,
  year={2017}
}
Acknowledgements
We thank the anonymous reviewers for their helpful feedback. This research was funded in part by an NSERC Discovery Grant (RGPIN-2015-04843).