Sim-to-Real: Learning Agile Locomotion For Quadruped Robots

Erwin Coumans
Danijar Hafner
Steven Bohez


Designing agile locomotion for quadruped robots often requires extensive expertise and tedious manual tuning. In this paper, we present a system to automate this process by leveraging deep reinforcement learning techniques. Our system can learn quadruped locomotion from scratch with simple reward signals. In addition, users can provide an open loop reference to guide the learning process if more control over the learned gait is needed. The control policies are learned in a physical simulator and then deployed to real robots. In robotics, policies trained in simulation often does not transfer to the real world. We narrow this reality gap by improving the physical simulator and learning robust policies. We improve the simulation using system identification, developing an accurate actuator model and simulating latency. We learn robust controllers by randomizing the physical environments, adding perturbations and designing a compact observation space. We evaluate our system on two agile locomotion gaits: trotting and galloping. After learning in simulation, a quadruped robot can successfully perform both gaits in real world.