Teach Biped Robots to Walk via Gait Principles and Reinforcement Learning with Adversarial Critics

Understand

Controlling a biped robot to walk stably is a challenging task considering its nonlinearity and hybrid dynamics.

• Reinforcement learning can address these issues by directly mapping the observed states to optimal actions that maximize the cumulative reward.
• However, the local minima caused by unsuitable rewards and the overestimation of the cumulative reward impede the maximization of the cumulative reward.
• To increase the cumulative reward, this paper designs a gait reward based on walking principles, which compensates the local minima for unnatural motions.