Robotic adaptation to unanticipated operating conditions is crucial to achieving persistence and robustness in complex real world settings. For a wide range of cutting-edge robotic systems, such as micro- and nano-scale robots, soft robots, medical robots, and bio-hybrid robots, it is infeasible to anticipate the operating environment a priori due to complexities that arise from numerous factors including imprecision in manufacturing, chemo-mechanical forces, and poorly understood contact mechanics. Drawing inspiration from data-driven modeling, geometric mechanics (or gauge theory), and adaptive control, we employ an adaptive system identification framework and demonstrate its efficacy in enhancing the performance of principally kinematic locomotors (those governed by Rayleigh dissipation or zero momentum conservation). We showcase the capability of the adaptive model to efficiently accommodate varying terrains and iteratively modified behaviors within a behavior optimization framework. This provides both the ability to improve fundamental behaviors and perform motion tracking to precision. Notably, we are capable of optimizing the gaits of the Purcell swimmer using approximately 10 cycles per link, which for the nine-link Purcell swimmer provides a factor of ten improvement in optimization speed over the state of the art. Beyond simply a computational speed up, this ten-fold improvement may enable this method to be successfully deployed for in-situ behavior refinement, injury recovery, and terrain adaptation, particularly in domains where simulations provide poor guides for the real world.

翻译：机器人对意外操作条件的适应能力对于在复杂现实环境中实现持久性和鲁棒性至关重要。对于各类前沿机器人系统（如微纳机器人、软体机器人、医疗机器人和生物混合机器人），由于制造误差、化学机械力以及接触力学认知不足等诸多因素带来的复杂性，无法在运行前预判其工作环境。受数据驱动建模、几何力学（或规范场论）和自适应控制的启发，我们采用自适应系统辨识框架，并验证了其在提升主要运动学运动器（受瑞利耗散或零动量守恒支配的系统）性能方面的有效性。我们展示了自适应模型在行为优化框架内高效适应多变地形和迭代改良行为的能力，既能改进基础行为，又能实现精准运动轨迹跟踪。值得注意的是，我们使用每连接约10个循环即可优化珀塞尔游泳器的步态，对于九连接珀塞尔游泳器而言，这比现有技术的优化速度提升了十倍。除计算加速外，这一数量级提升还可使该方法成功应用于现场行为改良、损伤恢复和地形适应，特别是在仿真难以有效指导现实环境的领域中。