Evolutionary Multi-Objective Optimization for Biped Walking of Humanoid Robot

The recent remarkable progress of robotics research makes advanced skills for robots to solve complex tasks. The divide-and-conquer approach is an intuitive and efficient method when we encounter complex problems. Being a divide-and-conquer approach, the multilayered system decomposes the problem into a set of levels and each level implements a single task-achieving behaviour. Many researchers employ this approach for robot control system, dividing a complex behaviour into several simple behaviours. For example, Lie et al. developed the Evolutionary Subsumption Architecture, which enables for heterogeneous robots to acquire the cooperative object transferring task (Liu & Iba, 2003). The autonomous locomotion of humanoid robots consists of following modules: global path planning using given geometrical information, local path planning based on the observation of environment, footstep planning, and whole-body motion generator. Since these modules mainly exchange the information with their neighbours, we can observe that they are hierarchically arranged from the aspect of communication. The parameter settings among these modules are necessary to adapt the system to the targeting environment. The problem involves a number of conflicting objectives such as stability of the robot motion and speed of locomotion. In this paper, we present a parameter tuning method for multi-layered robot control system by means of Evolutionary Multi-Objective Optimization (EMO). We explore the set of parameters for modules to adapt various kinds of environments. Switching these parameter sets enables us to operate the robots effectively. We developed three modules as the experiment environment: walking pattern generator, footstep planner and path planner. In the experiment, we focused on the footstep planner shown in Fig. 1, which realizes collisionfree walking. The parameter setting was manually adjusted in previous researches (Kuffner et al., 2003; Chestnutt et al., 2005). We discuss the conflicting objectives for the optimization footstep planner, and introduce a parameter setting method using EMO. Then we propose a simple rule to use parameter sets obtained by EMO to adapt the footstep planner to both crowded and sparse fields. The rest of the paper is organized as follows: Section 2 describes our robot control system, Section 3 shows an experiment of the parameter setting of the footstep planner, and Section 4 shows an application using the parameter setting obtained from Section 3 and a 8