Tensegrity robots, composed of rigid rods connected by elastic cables, have a
number of unique properties that make them appealing for use as planetary
exploration rovers. However, control of tensegrity robots remains a difficult
problem due to their unusual structures and complex dynamics. In this work, we
show how locomotion gaits can be learned automatically using a novel extension
of mirror descent guided policy search (MDGPS) applied to periodic locomotion
movements, and we demonstrate the effectiveness of our approach on tensegrity
robot locomotion. We evaluate our method with real-world and simulated
experiments on the SUPERball tensegrity robot, showing that the learned
policies generalize to changes in system parameters, unreliable sensor
measurements, and variation in environmental conditions, including varied
terrains and a range of different gravities. Our experiments demonstrate that
our method not only learns fast, power-efficient feedback policies for rolling
gaits, but that these policies can succeed with only the limited onboard
sensing provided by SUPERball's accelerometers. We compare the learned feedback
policies to learned open-loop policies and hand-engineered controllers, and
demonstrate that the learned policy enables the first continuous, reliable
locomotion gait for the real SUPERball robot. Our code and other supplementary
materials are available from this http URL

本文使用镜像下降引导策略搜索的新扩展应用于周期性运动中，展示了如何自动学习运动步态，并在 SUPERball 张力机器人的运动中展示了我们方法的有效性。