Continuous Versatile Jumping Using Learned Action Residuals

Understand

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.