Adaptation of Motion Capture Data of Human Arms to a Humanoid Robot Using Optimization

Abstract

Interactions of a humanoid with a human are important, when the humanoid is requested to provide people with human-friendly services in unknown or uncertain environment. Such interactions may require more complicated and human-like behaviors from the humanoid. In this work the arm motions of a human are discussed as the early stage of human motion imitation by a humanoid. A motion capture system is used to obtain human-friendly arm motions as references. However the captured motions may not be applied directly to the humanoid, since the differences in geometric or dynamics aspects as length, mass, degrees of freedom, and kinematics and dynamics capabilities exist between the humanoid and the human. To overcome this difficulty a method to adapt captured motions to a humanoid is developed. The geometric difference in the arm length is resolved by scaling the arm length of the humanoid with a constant. Using the scaled geometry of the humanoid the imitation of actor's arm motions is achieved by solving an inverse kinematics problem formulated using optimization. The errors between the captured trajectories of actor arms and the approximated trajectories of humanoid arms are minimized. Such dynamics capabilities of the joint motors as limits of joint position, velocity and acceleration are also imposed on the optimization problem. Two motions of one hand waiving and performing a statement in sign language are imitated by a humanoid through dynamics simulation. Interactions between a human and a robot, especially a humanoid, will have being more important for the robot to work with the human in unknown or uncertain environment. Such interactions may require more complicated and human-like motions from a hu- manoid such that the motions are safe and friendly to humans. The humanoid can be controlled by planing motions or may be taught by humans to perform complex motions for working with humans. For the second case the humanoid may learn motions directly from a human through its cameras. The humanoid will be required to move more intelligently if it works with humans daily in the future. From this reason the humanoid needs to imitate human motions. The process of human motion imitation begins with measuring hu- man motions as accurately as possible. The most popular way for the measurement is to use a motion capture system that can capture the motions of a human in the form of time trajectories of markers attached on the human body. These human motions have been used for animation or human motion analysis. However the captured mo- tions may not be applicable directly to the humanoid, since the dif- ferences between the two characters, human and humanoid, in the geometric and system aspects exist. On the other hands, the lengths, masses, and movement capabilities of the humanoid are much dif- ferent from those of the human such that the appropriate conversion of the captured motions to the humanoid is needed. The imitation of a human motion by a humanoid has been stud- ied by several researchers. (1) presented an adaptation method of human motion capture data for feasible walking pattern of a hu- manoid. The developed method used a Fourier expansion to obtain desired trajectories of the Zero Moment Point (ZMP) from human motion capture database. An optimization problem to determine the reaction forces of the foot against the ground corresponding to the