Evolutionary motion design for humanoid robots

ABSTRACT

We propose a new approach to generating the motion of humanoid robots intuitively by means of Interactive Evolutionary Computation (IEC). In our system, novice users are able to design effective motions through the subjective evaluation of displayed individuals, even if they do not have any technical knowledge. The motions evolved by the IEC system are not necessarily stable nor feasible in real environments. Thus, appropriate adjustments are required to revise the motions. For this purpose, we use a real-valued GA in a dynamic simulator. We empirically show the effectiveness of our approach by designing a kick motion for a humanoid robot.

REFERENCES

• 1: K. Nishiwaki, S. Kagami, Y. Kuniyoshi, M. Inaba, and H. Inoue. Online generation of humanoid walking motion based on a fast generation method of motion pattern that follows desired zmp. Proceedings of the 2002 IEEE/RSJ Intl. Conference on Intelligent Robots and Systems, EPFL, Lausanne, Switzerland, pages 2684--2689, 2002.
• 2: S. Nakaoka, A. Nakazawa, K. Yokoi, and K. Ikeuchi. Leg motion primitives for a humanoid robot to imitate human dances. Sixth International Conference on Humans and Computers(HS2003), August 2003.
• 3: K. Yamane and Y. Nakamura. Synergetic cg choreography through constraining and deconstraining at will. Proceedings of International Conference on Robotics and Automation, pages 855--862, May 2002.
• 4: H. Takagi. Interactive evolutionary computation - cooperation of computational intelligence and human kansei. Proceeding of 5th International Conference on Soft Computing and Information/Intelligent Systems, pages 41--50, Oct 1998.
• 5: H. Wakaki and H. Iba. Motion design of a 3d-cg avatar using interactive evolutionary computation. In 2002 IEEE international Conference on Systems, Man and Cybernetics (SMC'02). IEEE Press, 2002.
• 6: K. Wolff and P. Nordin. Learning biped locomotion from first principles on a simulated Humanoid Robot using Linear Genetic Programming. GECCO, pages 495--506, 2003.
• 7: F. Kanehiro, H. Hirukawa, and S. Kajita. Open architecture humanoid robotics platform. The International Journal of Robotics Research, 23(2):155--165, Oct 2004.
• 8: M.Vukobratovic and J.Stepanenko. On the stability of anthropomorphic systems. Mathematical Biosciences, 15:1--37, 1972.
• 9: Y. Inoue, T. Tohge, and H. Iba. Learning to acquire autonomous behavior - cooperation by humanoid robots. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO2004), pages 590--602, 2004.
• 10: Y. Kuroki, B. Blank, T. Mikami, P. Mayeux, A. Miyamoto, R. Playter, K. Nagasaka, M. Raibert, M. Nagano, and J. Yamaguchi. Motion creating system for a small biped entertainment robot. In Proceedings of the 2003 IEEE/RSJ Intl. Conference on Intelligent Robots and Systems, pages 1394--1399, 2003.
• 11: Carlos M. Fonseca , Peter J. Fleming, Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization, Proceedings of the 5th International Conference on Genetic Algorithms, p.416-423, June 01, 1993

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.9 Robotics

General Terms:
Algorithms, Experimentation

Keywords:
evolutionary robotics, humanoid robot, interactive evolutionary computation, motion design, multi-objective GA