Deep Learning Approach for Linear Locomotion Control of Spherical Robot

Spherical robot is a typical nonlinear underactuated nonholonomic system that is difficult to control accurate path trajectory and uniform and stable posture during transfer. In this paper, we propose a method called controller guided model learning (CGML) to control its posture and trajectory path simultaneously. This model-based control algorithm is a combination of traditional classical control techniques and model learning through deep neural networks. CGML implemented with artificial neural network improves data efficiency in learning by using reliability advantage of classical controller. The proposed method can reduce the learning time required for convergence, which is suitable for direct online learning. The simulation environment experiment of the spherical robot showed that CGML is superior to other algorithms in terms of data efficiency.