We present the design of a novel compliant quadruped robot: Cheetahcub, and a series of locomotion experiments with fast trotting gaits. The robot's leg configuration is based on a spring-loaded, pantograph mechanism with multiple segments. A dedicated open loop locomotion controller was derived and implemented. Experiments were run in simulation and in hardware on flat terrain and with a step-down, demonstrating the robot's self-stabilizing properties. The robot reached a running trot with short flight phases with a maximum Froude number of FR=1.30, or 6.9 body lengths per second. Morphological parameters such as the leg design also played a role. By adding distal in-series elasticity, self-stability and maximum robot speed improved. Our robot has several advantages, especially when compared to larger and stiffer quadruped robot designs. 1) It is, to the best of our knowledge, the fastest of all quadruped robots below 30 kg (in terms of Froude number and body lengths per second). 2) It shows self-stabilizing behavior over a large range of speeds with open loop control. 3) It is lightweight, compact, electrically powered. 4) It is cheap, easy to reproduce, robust, and safe to handle. This makes it an excellent tool for research of multi-segment legs in quadruped robots.

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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 realworld 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 http://rll.berkeley.edu/drl_tensegrity * These authors contributed equally to this work.

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