Tensegrity robots, composed of rigid rods and flexible cables, exhibit high
strength-to-weight ratios and extreme deformations, enabling them to navigate
unstructured terrain and even survive harsh impacts. However, they are hard to
control due to their high dimensionality, complex dynamics, and coupled
architecture. Physics-based simulation is one avenue for developing locomotion
policies that can then be transferred to real robots, but modeling tensegrity
robots is a complex task, so simulations experience a substantial sim2real gap.
To address this issue, this paper describes a Real2Sim2Real strategy for
tensegrity robots. This strategy is based on a differential physics engine that
can be trained given limited data from a real robot (i.e. offline measurements
and one random trajectory) and achieve a high enough accuracy to discover
transferable locomotion policies. Beyond the overall pipeline, key
contributions of this work include computing non-zero gradients at contact
points, a loss function, and a trajectory segmentation technique that avoid
conflicts in gradient evaluation during training. The proposed pipeline is
demonstrated and evaluated on a real 3-bar tensegrity robot.

本文提出了一种基于实体机器人和物理模拟引擎相结合的 Real2Sim2Real 方法，可以在有限的数据下训练物理模拟引擎以发现转移的运动策略，并通过采用轨迹分段技术，计算接触点的非零梯度，并避免了训练过程中的梯度评估冲突。该方法在三根杆条张力机器人中得到了证明和评估