  This work presents a methodology to design trajectory tracking feedback
control laws, which embed non-parametric statistical models, such as Gaussian
Processes (GPs). The aim is to minimize unmodeled dynamics such as undesired
slippages. The proposed approach has the benefit of avoiding complex
terramechanics analysis to directly estimate from data the robot dynamics on a
wide class of trajectories. Experiments in both real and simulated environments
prove that the proposed methodology is promising.
