Estimating Probability Distribution with Q-learning for Biped Gait Generation and Optimization

— A new biped gait generation and optimization method is proposed in the frame of Estimation of Distribution Algorithms (EDAs) with Q-learning method. By formulating the biped gait synthesis as a constrained multi-objective optimization problem, a dynamically stable and low energy cost biped gait is generated by EDAs with Q-learning (EDA Q), which estimate probability distributions derived from the objective function to be optimized to generate searching points in the highly-coupled and high dimensional working space of biped robots. To get the preferable permutation of the interrelated parameters, Qlearning is combined to build and modify the probability models in EDA autonomously. By making use of the global optimization capability of EDA, the proposed EDA Q can also solve the local minima problem in traditional Q-learning. On the other hand, with the learning agent, EDA Q can evaluate the probability distribution model regularly without pre-designed structure and updating rule. Th...

• Type: Conference
• Year: 2006
• Where: IROS
• Authors: Lingyun Hu, Changjiu Zhou, Zengqi Sun
• Keywords: Biped Gait, Biped Gait Generation, Cost Biped Gait, IROS 2006, Robotics