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准确性。该方法利用梯度信息和迭代优化，通过实验与仿真数据对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表明，所提方法能有效提升模型精度，展现了其在解决机器人实施过程中仿真-现实差距问题方面的潜力。