  Jumping is essential for legged robots to traverse through difficult
terrains. In this work, we propose a hierarchical framework that combines
optimal control and reinforcement learning to learn continuous jumping motions
for quadrupedal robots. The core of our framework is a stance controller, which
combines a manually designed acceleration controller with a learned residual
policy. As the acceleration controller warm starts policy for efficient
training, the trained policy overcomes the limitation of the acceleration
controller and improves the jumping stability. In addition, a low-level
whole-body controller converts the body pose command from the stance controller
to motor commands. After training in simulation, our framework can be deployed
directly to the real robot, and perform versatile, continuous jumping motions,
including omni-directional jumps at up to 50cm high, 60cm forward, and
jump-turning at up to 90 degrees. Please visit our website for more results:
https://sites.google.com/view/learning-to-jump.
