Grasping Unknown Objects by Coupling Deep Reinforcement Learning, Generative Adversarial Networks, and Visual Servoing
Ole-Magnus Pedersen,Ekrem Misimi,François Chaumette,Ole-Magnus Pedersen,Ekrem Misimi,François Chaumette
In this paper, we propose a novel approach for transferring a deep reinforcement learning (DRL) grasping agent from simulation to a real robot, without fine tuning in the real world. The approach utilises a CycleGAN to close the reality gap between the simulated and real environments, in a reverse real-to-sim manner, effectively "tricking" the agent into believing it is still in the simulator. Fur...
