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.

翻译：本文提出了一种新颖的方法，通过开发扩散接触模型（DCM），用于学习擦拭等富接触任务中的机器人控制。以往学习此类任务的方法依赖于通过贝叶斯优化对机器人进行试错式时变刚度调谐的阻抗控制。所提方法旨在通过利用变刚度输入预测机器人接触轨迹并采用神经模型，降低机器人操作成本。然而，接触动力学本质上高度非线性，其仿真需要凸优化等迭代计算。此外，使用有限层神经模型逼近此类计算十分困难。为克服这些限制，所提出的DCM采用去噪扩散模型，通过多步去噪的迭代计算模拟复杂动力学，从而提升预测精度。在仿真和真实环境中进行的刚度调谐实验表明，DCM在减少机器人试验次数的同时，实现了与传统基于机器人优化方法相当的性能。