Tensegrity robots, composed of rigid rods and flexible cables, exhibit high strength-to-weight ratios and significant deformations, which enable them to navigate unstructured terrains and survive harsh impacts. They are hard to control, however, due to high dimensionality, complex dynamics, and a coupled architecture. Physics-based simulation is a promising avenue for developing locomotion policies that can be transferred to real robots. Nevertheless, modeling tensegrity robots is a complex task due to a substantial sim2real gap. To address this issue, this paper describes a Real2Sim2Real (R2S2R) strategy for tensegrity robots. This strategy is based on a differentiable physics engine that can be trained given limited data from a real robot. These data include offline measurements of physical properties, such as mass and geometry for various robot components, and the observation of a trajectory using a random control policy. With the data from the real robot, the engine can be iteratively refined and used to discover locomotion policies that are directly transferable to the real robot. Beyond the R2S2R pipeline, key contributions of this work include computing non-zero gradients at contact points, a loss function for matching tensegrity locomotion gaits, and a trajectory segmentation technique that avoids conflicts in gradient evaluation during training. Multiple iterations of the R2S2R process are demonstrated and evaluated on a real 3-bar tensegrity robot.

翻译：张力结构机器人由刚性杆和柔性电缆组成，具有高强度重量比和显着的变形，使它们能够穿越不规则地形和抵御强烈的冲击。然而，它们很难控制，因为具有高维度、复杂动态和相互耦合结构。基于物理的模拟是开发可转移到真实机器人的运动控制策略的一个有前途的途径。然而，由于存在相当大的真实到仿真的鸿沟，对张力结构机器人进行建模是一项复杂的任务。为了解决这个问题，本文描述了一种张力结构机器人的真实到仿真到真实策略。该策略基于可微分物理引擎，可以在给定有限的真实机器人数据的情况下进行训练。这些数据包括离线测量物理属性（例如各种机器人组件的质量和几何）以及使用随机控制策略观察轨迹的数据。通过真实机器人数据的迭代训练，引擎可以被不断完善，并用于发现可直接转移至真实机器人的运动控制策略。除了R2S2R管道之外，本文的关键贡献还包括计算接触点处的非零梯度、匹配张力结构运动步态的损失函数以及在训练期间避免梯度冲突的轨迹分割技术。R2S2R过程的多次迭代在真实3棒张力结构机器人上进行演示并进行评估。