This paper employs a reinforcement learning-based model identification method aimed at enhancing the accuracy of the dynamics for our snake robot, called COBRA. Leveraging gradient information and iterative optimization, the proposed approach refines the parameters of COBRA's dynamical model such as coefficient of friction and actuator parameters using experimental and simulated data. Experimental validation on the hardware platform demonstrates the efficacy of the proposed approach, highlighting its potential to address sim-to-real gap in robot implementation.

翻译：本文采用基于强化学习的模型辨识方法，旨在提高我们称为COBRA的蛇形机器人动力学模型的精度。该方法利用梯度信息和迭代优化，通过实验数据与仿真数据对COBRA动力学模型中的摩擦系数和执行器参数等关键参数进行精细化校准。在硬件平台上的实验验证证明了所提方法的有效性，突显了其在解决机器人实施中仿真到现实差距方面的潜力。