Otto—Design and Control of an 8-DoF SEA-Driven Quadrupedal Robot

This paper presents the mechanical design of Otto, a lightweight 8-degrees-of-freedom (8-DoF) quadrupedal robot employing Series Elastic Actuators (SEA), and a training framework for learning locomotion control policies in simulation using Reinforcement Learning (RL). Otto's design differs from typical 12-DoF quadrupeds by lacking hip adduction-abduction degrees of freedom. This reduces the robot's cost and weight and increases complexity for tasks involving base rotation and angular twist following. The elastic elements at the joints improve compliance, energy efficiency, safety, and stability, increase robustness, and reduce damage to robot hardware components. Our locomotion control approach leverages RL to optimize policies in simulation, allowing stable and efficient movement despite mechanical constraints, i.e., an 8-DoF quadrupedal robot. Through extensive simulation training, leveraging highly parallel GPU-accelerated robotic simulators, we ensure the policy is well-suited for deployment in real-world scenarios, where accurate motion control is critical for performance. The trained policy is then transferred to the physical robot platform. We demonstrate its effectiveness in various tasks and real-life scenarios with varying payloads and terrains, and compare it with a state-of-the-art model-based method. The results show that Otto, equipped with our RL-based locomotion control, achieves robust performance, compensating for the reality gap and managing the reduced DoF available in Otto.