84 citations · 100 across the 4 of their papers we have counts for
4 papers
How does the structure embedded in learning policy affect learning quadruped locomotion?
Kuangen Zhang, Jongwoo Lee, Zhimin Hou +3
Reinforcement learning (RL) is a popular data-driven method that has demonstrated great success in robotics. Previous works usually focus on learning an end-to-end (direct) policy…
Off-policy Maximum Entropy Reinforcement Learning : Soft Actor-Critic with Advantage Weighted Mixture Policy(SAC-AWMP)
Zhimin Hou, Kuangen Zhang, Yi Wan +3
The optimal policy of a reinforcement learning problem is often discontinuous and non-smooth. I.e., for two states with similar representations, their optimal policies can be signi…
Teach Biped Robots to Walk via Gait Principles and Reinforcement Learning with Adversarial Critics
Kuangen Zhang, Zhimin Hou, Clarence W. de Silva +2
Controlling a biped robot to walk stably is a challenging task considering its nonlinearity and hybrid dynamics. Reinforcement learning can address these issues by directly mapping…
Compare Contact Model-based Control and Contact Model-free Learning: A Survey of Robotic Peg-in-hole Assembly Strategies
Jing Xu, Zhimin Hou, Zhi Liu +1
In this paper, we present an overview of robotic peg-in-hole assembly and analyze two main strategies: contact model-based and contact model-free strategies. More specifically, we…