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...