Advanced Skills through Multiple Adversarial Motion Priors in Reinforcement Learning

Nvidia presented parts of this work at GTC 2022, revealing our humanoid-quadruped transformer! Title: Advanced Skills through Multiple Adversarial Motion Priors in Reinforcement Learning Authors: Eric Vollenweider, Marko Bjelonic, Victor Klemm, Nikita Rudin, Joonho Lee and Marco Hutter Paper submitted to IEEE/RSJ International Conference on Intelligent Robots and Systems in Kyoto. Preprint: Abstract: In recent years, reinforcement learning (RL) has shown outstanding performance for locomotion control of highly articulated robotic systems. Such approaches typically involve tedious reward function tuning to achieve the desired motion style. Imitation learning approaches such as adversarial motion priors aim to reduce this problem by encouraging a pre-defined motion style. In this work, we present an approach to augment the concept of adversarial motion prior-based RL to allow for multiple, discretely switchable styles. We show that multiple styles and