907 citations · 1.3k across the 17 of their papers we have counts for
4 papers · 1 filter
Terrain RL Simulator
Glen Berseth, Xue Bin Peng, Michiel van de Panne
We provide challenging simulation environments that range in difficulty. The difficulty of solving a task is linked not only to the number of dimensions in the action space bu…
DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills
Xue Bin Peng, Pieter Abbeel, Sergey Levine +1
A longstanding goal in character animation is to combine data-driven specification of behavior with a system that can execute a similar behavior in a physical simulation, thus enab…
Feedback Control For Cassie With Deep Reinforcement Learning
Zhaoming Xie, Glen Berseth, Patrick Clary +2
Bipedal locomotion skills are challenging to develop. Control strategies often use local linearization of the dynamics in conjunction with reduced-order abstractions to yield tract…
Progressive Reinforcement Learning with Distillation for Multi-Skilled Motion Control
Glen Berseth, Cheng Xie, Paul Cernek +1
Deep reinforcement learning has demonstrated increasing capabilities for continuous control problems, including agents that can move with skill and agility through their environmen…