SimGAN: Hybrid Simulator Identification for Domain Adaptation via Adversarial Reinforcement Learning
Yifeng Jiang,Tingnan Zhang,Daniel Ho,Yunfei Bai,C. Karen Liu,Sergey Levine,Jie Tan,Yifeng Jiang,Tingnan Zhang,Daniel Ho,Yunfei Bai,C. Karen Liu,Sergey Levine,Jie Tan
As learning-based approaches progress towards automating robot controllers design, transferring learned policies to new domains with different dynamics (e.g. sim-to-real transfer) still demands manual effort. This paper introduces SimGAN, a framework to tackle domain adaptation by identifying a hybrid physics simulator to match the simulated trajectories to the ones from the target domain, using a...
