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：针对复杂真实环境中出现的意外工况，机器人系统的自适应能力对实现持久性与鲁棒性至关重要。在微型/纳米机器人、软体机器人、医疗机器人和生物混合机器人等前沿机器人系统中，由于制造精度不足、化学-机械力作用以及接触力学机制不明等复杂因素，预先感知运行环境往往难以实现。本文受数据驱动建模、几何力学（规范场论）与自适应控制的启发，采用自适应系统辨识框架，验证了该方法对纯运动学运动器（受瑞利耗散或零动量守恒支配的系统）性能提升的有效性。我们展示了该自适应模型在行为优化框架内高效适应多变地形和迭代修正行为的能力，既可实现基础行为优化，又能完成精密运动跟踪。值得注意的是，使用该方法优化Purcell游泳器步态时，每个关节仅需约10个运动周期，对九关节Purcell游泳器而言，相较现有最优方法实现了十倍优化速度提升。这种计算加速不仅限于理论层面，十倍性能提升更可实现该方法在行为原位优化、损伤恢复与地形适应等场景的成功部署——尤其适用于仿真难以准确指导真实世界的应用领域。