Publication Articles

Feedback Control for Cassie with Deep Reinforcement Learning

      IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018) Zhaoming Xie (1), Glen Berseth (1), Patrick Clary (2), Jonathan Hurst (2), Michiel van de Panne (1) (1) University of British Columbia (2) Oregon State University Abstract Bipedal locomotion skills are challenging to develop. Control strategies often use local …

Model-Based Action Exploration for Learning Dynamic Motion Skills

      Abstract Deep reinforcement learning has achieved great strides in solving challenging motion control tasks. Recently, there has been significant work on methods for exploiting the data gathered during training, but there has been less work on how to best generate the data to learn from. For continuous action domains, the …

TerrainRL Sim

Abstract We provide \(88\) challenging simulation environments that range in difficulty. The difficulty in these \environments is linked not only to the number of dimensions in the action space but also to the task complexity. Using more complex and accurate simulations will help push the field closer to creating human-level …if (!document.getElementById('mathjaxscript_pelican_#%@#$@#')) { var align = "center", indent = "0em", linebreak = "false"; if (false) { align = (screen.width