Sim-to-Real Control of Trifinger Robot by Deep Reinforcement Learning

Currently, deep reinforcement learning primarily focuses on simulated environments in the field of robot control. Algorithms deployed on real robots have high platform requirements, leading to practical implementation difficulties. This paper presents an easily implementable algorithm transfer framework deployed to a trifinger robot. Firstly, we obtain well-performing policy models by various deep reinforcement learning algorithms trained on a simulated environment. Through multimodal information fusion, domain randomization and observation-action space pruning, the models are successfully transferred to the real robots. The presented framework is capable of controlling a real trifinger robot to move a randomly placed target to a specified position with the success rate of 90.74%, demonstrating the feasibility of our framework and the effectiveness of our methods.