Barry: A High-Payload and Agile Quadruped Robot

This paper introduces Barry, a dynamically bal- ancing quadruped robot optimized for high payload capabilities and efficiency. It presents a new high-torque and low-inertia leg design, which includes custom-built high-efficiency actuators and transparent, sensorless transmissions. The robot’s reinforcement learning-based controller is trained to fully leverage the new hardware capabilities to balance and steer the robot. The newly developed controller can manage the non-linearities introduced by the new leg design and handle unmodeled payloads up to 90 kg while operating at high efficiency. The approach’s efficacy is demonstrated by a high payload-to-weight ratio verified with multiple tests, with a maximum ratio of 2 on flat terrain. Experiments also demonstrate Barry’s power consumption and cost of transport, which converge to a value of 0.7 at 1.4 m/s, regardless of the payload mass.