juwu-19/legged_robots_control

Course project of Legged robots MICRO-507

Reinforcement learning control of biped robot

1. Modelling

On the basis of biped kinematics, dynamics and conservation of angular momentum, the impact map is computed to model robot step switching

2. PD feedback controller

PD controller for this under-actuated system is implemented

And then the gait metrics are introduced and the unconstrained optimization is carried out to decide optimal parameters as

3. TD3 agent control

The reinforcement learning toolbox is used to implement the environment and the agent. The reward function is designed as

The episode resward during training as

It can be seen that:
Pros: can achieve better performances than any other controller thanks to learning
Cons: difficult to find good reward function, training is very slow
Bonus: pretty robust against perturbations