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：张拉整体机器人由刚性杆和柔性缆索构成，具有高比强度和显著变形能力，使其能够穿越非结构化地形并承受剧烈冲击。然而，由于高维度、复杂动力学及耦合架构的特性，此类机器人难以控制。基于物理仿真是开发可迁移至实体机器人的运动策略的重要途径。但受限于显著的虚实差距，张拉整体机器人的建模是一项复杂任务。针对该问题，本文提出一种面向张拉整体机器人的虚实映射迁移策略。该策略基于可微物理引擎，仅需有限真实机器人数据即可训练——包括各部件质量、几何参数等物理特性的离线测量数据，以及采用随机控制策略采集的轨迹观测数据。通过真实机器人数据，该引擎可迭代优化，并用于发现可直接迁移至实体机器人的运动策略。除虚实映射迁移流程外，本文的关键贡献还包括：接触点处非零梯度的计算方法、用于匹配张拉整体运动步态的损失函数，以及避免训练中梯度评估冲突的轨迹分割技术。通过在真实三杆张拉整体机器人上多次迭代验证该策略的有效性。