Glen Berseth

I am an assistant professor at the University de Montreal and Mila. My research explores how to use deep learning and reinforcement learning to develop generalist robots.

#Locomotion Articles


DeepLoco: Dynamic Locomotion Skills Using Hierarchical Deep Reinforcement Learning

Xue Bin Peng, Glen Berseth, KangKang Yin, Michiel van de Panne

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.


Modelling Dynamic Brachiation

Glen Berseth, Michiel van de Panne

Significant progress has been made with regard to motions such as walking, running, and other specific motions, such as falling and rolling. However, we still have difficulty simulating agile motions we see in nature, for example, brachiation by gibbons. Gibbons are one of the most agile primates and can leap remarkable distances. In this work we discuss the advantages of skill learning with explicit planning to create motion controllers for more complex and dynamic navigation tasks. Skill learning is complex and cannot be directly solved using only supervised learning because generating good data plays a key role in learning good skills. Here we construct a FSM controller to model the motion and capabilities of a gibbon, one of the most agile primates, shown in Figure 1. We endeavour to give this controller motion skills using reinforcement learning and use this dynamics model to intelligently sample good actions.