Sitting pose generation using genetic algorithm for NAO humanoid robots

Humanoid robots are increasingly used to perform human mimicking tasks, such as walking, grasping, standing and sitting on objects. To generate poses interactively using a humanoid robot, the performed poses should be controlled to satisfy any potential interaction with the surrounding environment. In this paper, a simulated humanoid robot “NAO” is used to discover a fitness-based optimal sitting pose performed on various types of sittable-objects, varying in shape and height. Using an initial set of random valid sitting poses as the input generation, genetic algorithm (GA) is applied to construct the fitness-based optimal sitting pose for the robot to fit well on the sittable-object (i.e. box and ball). The used fitness criteria reflecting pose stability (i.e. how feasible the pose is based on real world physical limitation), converts poses into numerical stability level. The feasibility of the proposed approach is measured through a simulated environment using V-Rep simulator which shows how the GA is able to generate a fitness-based optimal sitting-pose. The real “NAO” robot is used to perform results generated by the simulation.