The Modular Behavioral Environment for Humanoids and other Robots (MoBeE)

Mikhail Frank, Jürgen Leitner, Marijn Stollenga, Simon Harding, Alexander Förster, Jürgen Schmidhuber

2012

Abstract

To produce even the simplest human-like behaviors, a humanoid robot must be able to see, act, and react, within a tightly integrated behavioral control system. Although there exists a rich body of literature in Computer Vision, Path Planning, and Feedback Control, wherein many critical subproblems are addressed individually, most demonstrable behaviors for humanoid robots do not effectively integrate elements from all three disciplines. Consequently, tasks that seem trivial to us humans, such as pick-and-place in an unstructured environment, remain far beyond the state-of-the-art in experimental robotics. We view this primarily as a software engineering problem, and have therefore developed MoBeE, a novel behavioral framework for humanoids and other complex robots, which integrates elements from vision, planning, and control, facilitating the synthesis of autonomous, adaptive behaviors. We communicate the efficacy of MoBeE through several demonstrative experiments. We first develop Adaptive Roadmap Planning by integrating a reactive feedback controller into a roadmap planner. Then, an industrial manipulator teaches a humanoid to localize objects as the two robots operate autonomously in a shared workspace. Finally, an integrated vision, planning, control system is applied to a real-world reaching task using the humanoid robot.

References

  1. Brooks, R. (1991). Intelligence without representation. Artificial intelligence, 47(1):139-159.
  2. Denavit, J. and Hartenberg, R. (1955). A kinematic notation for lower-pair mechanisms based on matrices. Trans. of the ASME. Journal of Applied Mechanics, 22:215- 221.
  3. Fitzpatrick, P., Metta, G., and Natale, L. (2008). Towards long-lived robot genes. Robotics and Autonomous Systems, 56(1):29-45.
  4. Forsyth, D. and Ponce, J. (2002). Computer vision: a modern approach. Prentice Hall Professional Technical Reference.
  5. Franklin, G., Powell, J., and Emami-Naeini (1994). Feedback control of dynamic systems, volume 2. AddisonWesley Reading, MA.
  6. Gupta, K. (1986). Kinematic analysis of manipulators using the zero reference position description. The International Journal of Robotics Research, 5(2):5.
  7. Hartley, R. and Zisserman, A. (2000). Multiple view geometry in computer vision, volume 2. Cambridge Univ Press.
  8. Jackson, J. (2007). Microsoft robotics studio: A technical introduction. IEEE Robotics & Automation Magazine, 14(4):82-87.
  9. Kaufmann, G. (2010). A flexible and safe environment for robotic experiments : a sandbox and testbed for experiments intended for the humanoid robot icub. Master's thesis, Università della Svizzera italiana (USI).
  10. Kazerounian, K., Latif, K., Alvarado, C., et al. (2005a). Protofold: A successive kinetostatic compliance method for protein conformation prediction. Journal of Mechanical Design, 127:712.
  11. Kazerounian, K., Latif, K., Rodriguez, K., Alvarado, C., et al. (2005b). Nano-kinematics for analysis of protein molecules. Journal of Mechanical Design, 127:699.
  12. LaValle, S. (2006). Planning algorithms. Cambridge Univ Pr.
  13. Metta, G., Fitzpatrick, P., and Natale, L. (2006). YARP: Yet Another Robot Platform. International Journal of Advanced Robotic Systems, 3(1).
  14. Quigley, M., Gerkey, B., Conley, K., Faust, J., Foote, T., Leibs, J., Berger, E., Wheeler, R., and Ng, A. (2009). ROS: an open-source Robot Operating System. In International Conference on Robotics and Automation, Open-Source Software workshop.
  15. The Boost Graph Library (accessed 2012). Bgl, the boost graph library. url=http://www.boost.org/libs/graph/.
  16. The CGAL Project (accessed 2012). Cgal, the computational geometry algorithms library. url=http:// www.cgal.org/.
  17. van den Bergen, G. (2004). Collision Detection in Interactive 3D Environments. Morgan Kaufmann.
  18. Wierstra, D., Schaul, T., Peters, J., and Schmidhuber, J. (2008). Natural evolution strategies. In Evolutionary Computation, 2008. CEC 2008.(IEEE World Congress on Computational Intelligence). IEEE Congress on, pages 3381-3387. IEEE.
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Paper Citation


in Harvard Style

Frank M., Leitner J., Stollenga M., Harding S., Förster A. and Schmidhuber J. (2012). The Modular Behavioral Environment for Humanoids and other Robots (MoBeE) . In Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-989-8565-22-8, pages 304-313. DOI: 10.5220/0004041703040313


in Bibtex Style

@conference{icinco12,
author={Mikhail Frank and Jürgen Leitner and Marijn Stollenga and Simon Harding and Alexander Förster and Jürgen Schmidhuber},
title={The Modular Behavioral Environment for Humanoids and other Robots (MoBeE)},
booktitle={Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2012},
pages={304-313},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004041703040313},
isbn={978-989-8565-22-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - The Modular Behavioral Environment for Humanoids and other Robots (MoBeE)
SN - 978-989-8565-22-8
AU - Frank M.
AU - Leitner J.
AU - Stollenga M.
AU - Harding S.
AU - Förster A.
AU - Schmidhuber J.
PY - 2012
SP - 304
EP - 313
DO - 10.5220/0004041703040313