Adaptation of human motion capture data to humanoid robots for motion imitation using optimization

The interactions between a humanoid robot and human are important, when the humanoid robot is requested to serve people with human-friendly services. For such interactions, the imitation of human arm motions by a humanoid robot is discussed as the first step for imitating the full body motion of a human. The human motions captured by a motion capture system may not be applied directly to the humanoid robot because of the differences in geometric aspect, dynamics and kinematic capabilities between the robot and human. To overcome this difficulty, a method to adapt captured motions to the humanoid robot is developed. The geometric difference in the arm length is resolved by scaling the arm length of the robot with a constant based on a length ratio. The imitation of human arm motion is then realized by solving an inverse kinematics problem which is formulated as an optimization task. The errors between the captured trajectories of human arms and the approximated trajectories of robot's arms are minimized. The dynamics capabilities of the joint motors such as limits of joint position and velocity, are imposed on the optimization problem. Several human motions are imitated by the humanoid robots developed in our institute.

[1]  Stefan Schaal,et al.  Learning from Demonstration , 1996, NIPS.

[2]  John J. Craig,et al.  Introduction to Robotics Mechanics and Control , 1986 .

[3]  Maja J. Mataric,et al.  Getting Humanoids to Move and Imitate , 2000, IEEE Intell. Syst..

[4]  ChangHwan Kim,et al.  Motion-embedded cog jacobian for a real-time humanoid motion generation , 2005, ICINCO.

[5]  Kazuhito Yokoi,et al.  Generating whole body motions for a biped humanoid robot from captured human dances , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[6]  Jessica K. Hodgins,et al.  Adapting simulated behaviors for new characters , 1997, SIGGRAPH.

[7]  Kazuhito Yokoi,et al.  Leg motion primitives for a dancing humanoid robot , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[8]  Maja J. Mataric,et al.  Parametric primitives for motor representation and control , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[9]  Maja J. Mataric,et al.  Markerless kinematic model and motion capture from volume sequences , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[10]  Zhaoqin Peng,et al.  Kinematics mapping and similarity evaluation of humanoid motion based on human motion capture , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[11]  Christopher G. Atkeson,et al.  Adapting human motion for the control of a humanoid robot , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[12]  Aude Billard,et al.  A biologically inspired robotic model for learning by imitation , 2000, AGENTS '00.

[13]  Yoshihiko Nakamura,et al.  Making feasible walking motion of humanoid robots from human motion capture data , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).