Overview

In this project, we control the locomotion of a quadrupedal robot via CPG and RL respectively.

The CPG controller generates different gaits according to predefined parameter sets and keeps balance after tuning, which . The maximum speed reached with the gait pronk, whereas the fastest gait for real dogs is rotatory gallop. In our opinion,

  • for a faster locomotion with simple CPG, more sensors are needed to keep balance and seize the right moment to swing. (This implies the importance of reflex)
  • CPG provides a convenient and explainable approach to convert the gait to specific physical parameters
  • however, while it doesn’t take much effort to tune the parameters for most gaits, it’s a hard to make the robot move at a speed comparable to a real dog for some gaits.

We used 2 reward functions of different types and used both Proximal Policy Optimization (PPO) and Soft Actor Critic (SAC) in our experiments. The quadrupedal robot achieves a fastest speed at 4.8m/s

Some Result Demos

The full list

  • Slow walking using CPG
  • Quadruped galloping gait in the competiton environment with locomotion controller trained by DRL (PPO).
  • Quadruped walking gait in the plain environment trained by DRL (SAC).
  • Quadruped back walking in the competition environment trained by DRL (SAC) with joint PD control method.

For more info