Breathing Life Into Robots Through Simulators

Simulation enables engineers to prototype rapidly and with minimal human effort. In robotics, physics simulations provide a secure and low-cost virtual playground for robots to gain physical skills through Deep Reinforcement Learning (DRL). However, simulations use hand-derived physics that will have difficulty adapting when tested on real hardware. This challenge is termed the "sim-to-real gap" or the domain adaptation problem. Reinforcement-based approaches(  RL-CycleGAN and RetinaGAN) have been utilised to bridge the simulation-to-reality gap for purely perceptual tasks, such as grasping. However, the gap is still present because of the dynamic characteristics of robotic systems. In this case, researchers are prompted to ask whether or not they can find a more accurate physics simu