Finite State Machine-Based Motion-Free Learning of Biped Walking

Robust Motion Control of Biped Walking Robots

This paper proposes a robust mathematical approach for motion control. The proposed control technique is applied to a three-degree of freedom Biped walking robot for illustration. The design technique is divided into two major steps; the first one is to establish a robust position control scheme with both guaranteed stability and trajectory tracking capability using a model-reference technique ...

Poincaré-Map-Based Reinforcement Learning For Biped Walking

We propose a model-based reinforcement learning algorithm for biped walking in which the robot learns to appropriately modulate an observed walking pattern. Viapoints are detected from the observed walking trajectories using the minimum jerk criterion. The learning algorithm modulates the via-points as control actions to improve walking trajectories. This decision is based on a learned model of...

Biped dynamic walking using reinforcement learning

This paper presents some results from a study of biped dynamic walking using reinforcement learning. During this study a hardware biped robot was built, a new reinforcement learning algorithm as well as a new learning architecture were developed. The biped learned dynamic walking without any previous knowledge about its dynamic model. The Self Scaling Reinforcement learning algorithm was develo...

Finite State Machine Based SDL

Walking Pattern Generation for Planar Biped Walking Using Q-learning

In this research, a stable biped walking pattern is generated using reinforcement learning. The biped walking pattern is chosen as a simple third order polynomial. To complete the walking pattern, four boundary conditions are needed. Initial position and velocity and final position and velocity of the joint are selected as boundary conditions. In order to find the proper boundary condition valu...