论文标题
运动学变压器：使用变压器解决软机器人的反向建模问题
Kinematics Transformer: Solving The Inverse Modeling Problem of Soft Robots using Transformers
论文作者
论文摘要
软机器人操纵器在脆弱的环境（例如海洋环境）中提供了许多优势。但是，开发用于形状，运动和强制控制此类机器人所必需的分析逆模型仍然是一个具有挑战性的问题。作为分析模型的替代方法，可以使用强大的机器学习方法来学习数值模型。在本文中，提出了运动学变压器来开发软机器人四肢的准确和精确的逆运动模型。所提出的方法将反向运动学问题重新验证为顺序预测问题，并基于变压器体系结构。数值模拟表明，所提出的方法可以有效地用于控制软肢。基准研究还表明，与基线馈送神经网络相比，所提出的方法具有更好的准确性和精度
Soft robotic manipulators provide numerous advantages over conventional rigid manipulators in fragile environments such as the marine environment. However, developing analytic inverse models necessary for shape, motion, and force control of such robots remains a challenging problem. As an alternative to analytic models, numerical models can be learned using powerful machine learned methods. In this paper, the Kinematics Transformer is proposed for developing accurate and precise inverse kinematic models of soft robotic limbs. The proposed method re-casts the inverse kinematics problem as a sequential prediction problem and is based on the transformer architecture. Numerical simulations reveal that the proposed method can effectively be used in controlling a soft limb. Benchmark studies also reveal that the proposed method has better accuracy and precision compared to the baseline feed-forward neural network
