  In this paper, a novel approach is proposed for learning robot control in
contact-rich tasks such as wiping, by developing Diffusion Contact Model (DCM).
Previous methods of learning such tasks relied on impedance control with
time-varying stiffness tuning by performing Bayesian optimization by
trial-and-error with robots. The proposed approach aims to reduce the cost of
robot operation by predicting the robot contact trajectories from the variable
stiffness inputs and using neural models. However, contact dynamics are
inherently highly nonlinear, and their simulation requires iterative
computations such as convex optimization. Moreover, approximating such
computations by using finite-layer neural models is difficult. To overcome
these limitations, the proposed DCM used the denoising diffusion models that
could simulate the complex dynamics via iterative computations of multi-step
denoising, thus improving the prediction accuracy. Stiffness tuning experiments
conducted in simulated and real environments showed that the DCM achieved
comparable performance to a conventional robot-based optimization method while
reducing the number of robot trials.
