Skip to main content
SpringerLink
Log in

A biomimetic, force-field based computational model for motion planning and bimanual coordination in humanoid robots

  • Published:
Autonomous Robots Aims and scope Submit manuscript

Abstract

This paper addresses the problem of planning the movement of highly redundant humanoid robots based on non-linear attractor dynamics, where the attractor landscape is obtained by combining multiple force fields in different reference systems. The computational process of relaxation in the attractor landscape is similar to coordinating the movements of a puppet by means of attached strings, the strings in our case being the virtual force fields generated by the intended/attended goal and the other task dependent combinations of constraints involved in the execution of the task. Hence the name PMP (Passive Motion Paradigm) was given to the computational model. The method does not require explicit kinematic inversion and the computational mechanism does not crash near kinematic singularities or when the robot is asked to achieve a final pose that is outside its intrinsic workspace: what happens, in this case, is the gentle degradation of performance that characterizes humans in the same situations. Further, the measure of inconsistency in the relaxation in such cases can be directly used to trigger higher level reasoning in terms of breaking the goal into a sequence of subgoals directed towards searching and perhaps using tools to realize the otherwise unrealizable goal. The basic PMP model has been further expanded in the present paper by means of (1) a non-linear dynamical timing mechanism that provides terminal attractor properties to the relaxation process and (2) branching units that allow to ‘compose’ complex PMP-networks to coordinate multiple kinematic chains in a complex structure, including manipulated tools. A preliminary evaluation of the approach has been carried out with the 53 degrees of freedom humanoid robot iCub, with particular reference to trajectory formation and bimanual/whole upper body coordination under the presence of different structural and task specific constraints.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Abend, W., Bizzi, E., & Morasso, P. (1982). Human arm trajectory formation. Brain, 105, 331–348.

    Article  Google Scholar 

  • Atkeson, C. G., Hale, J. G., Pollick, F. et al. (2000). Using humanoid robots to study human behavior. IEEE Intelligent Systems, 15, 46–56.

    Article  Google Scholar 

  • Baillieul, J. (1985). Kinematic programming alternatives for redundant manipulators. In IEEE international conference on robotics and automation (pp. 722–728).

  • Balestrino, A., De Maria, G., & Sciavicco, L. (1984). Robust control of robotic manipulators. In Proceedings of the 9th IFAC world congress (Vol. 5, pp. 2435–2440).

  • Bizzi, E., Mussa Ivaldi, F. A., & Giszter, S. (1991). Computations underlying the execution of movement: A biological perspective. Science, 253, 287–291.

    Article  Google Scholar 

  • Boysen, S. T., & Himes, G. T. (1999). Current issues and emerging theories in animal cognition. Annual Reviews of Psychology, 50, 683–705.

    Article  Google Scholar 

  • Brooks, R. A. (1997). The Cog project. Journal of the Robotics Society of Japan, 15, 968–970.

    Google Scholar 

  • Brooks, R. A., & Stein, L. A. (1994). Building brains for bodies. Autonomous Robots, 1(1), 7–25.

    Article  Google Scholar 

  • Bullock, D., & Grossberg, S. (1988). Neural dynamics of planned arm movements: Emergent invariants and speed-accuracy properties. Psychological Review, 95, 49–90.

    Article  Google Scholar 

  • Buss, S. R., & Kim, J.-S. (2005). Selectively damped least squares for inverse kinematics. Journal of Graphics Tools, 10(3), 37–49.

    Google Scholar 

  • Chappell, L., & Kacelnik, J. (2002). Selection of tool diameter by new Caledonian crows Corvus moneduloides. Animal Cognition, 7, 121–127.

    Article  Google Scholar 

  • Emery, N. J., & Clayton, N. S. (2004). The mentality of crows: Convergent evolution of intelligence in corvids and apes. Science, 306, 1903–1907.

    Article  Google Scholar 

  • Flash, T., & Hogan, N. (1985). The coordination of arm movements: an experimentally confirmed mathematical model. The Journal of Neuroscience, 5, 688–703.

    Google Scholar 

  • Hersch, M., & Billard, A. G. (2008). Reaching with multi-referential dynamical systems. Autonomous Robots, 25(1–2), 71–83.

    Article  Google Scholar 

  • Hirose, M., & Ogawa, K. (2007). Honda humanoid robots development. Philosophical Transaction A: Mathematical Physical and Engineering Sciences, 365, 11–19.

    Article  Google Scholar 

  • Hoffmann, H., Pastor, P., Dae-Hyung, P., & Schaal, S. (2009a). Biologically-inspired dynamical systems for movement generation: Automatic real-time goal adaptation and obstacle avoidance. In ICRA 2009.

  • Hoffmann, H., Pastor, P., Asfour, T., & Schaal, S. (2009b). Learning and generalization of motor skills by learning from demonstration. In ICRA 2009.

  • Ijspeert, A. J., Nakanishi, J., & Schaal, S. (2002). Movement imitation with nonlinear dynamical systems in humanoid robots. In Proceed IEEE ICRA2002 (pp. 1398–1403).

  • Jordan, M. I. (1986). Attractor dynamics and parallelism in a connectionist sequential machine. In Proc. eighth ann conf cognitive science society (pp. 531–546). Hillsdale: Erlbaum.

    Google Scholar 

  • Liegeosis, A. (1977). Automatic supervisory control of the configuration and behavior of multibody mechanisms. IEEE Transactions on Systems, Man, and Cybernetics, 7, 868–871.

    Article  Google Scholar 

  • Limongelli, L., Boysen, S. T., & Visalberghi, E. (1995). Comprehension of cause-effect relations in a tool-using task by chimpanzees (Pan troglodytes). Journal of Comparative Psychology, 109, 18–26.

    Article  Google Scholar 

  • Metta, G., Fitzpatrick, P., & Natale, L. (2006). YARP: Yet another robot platform. International Journal of Advanced Robotics Systems, 3, 43–48.

    Google Scholar 

  • Metta, G., Sandini, G., Vernon, D., Natale, L., & Nori, F. (2008). The iCub humanoid robot: An open platform for research in embodied cognition. In PerMIS: Performance metrics for intelligent systems workshop, Aug 19–21, 2008. Washington DC: USA.

  • Miall, R. C., & Wolpert, D. M. (1996). Forward models for physiological motor control. Neural Networks, 9, 1265–1279.

    Article  MATH  Google Scholar 

  • Mohan, V., & Morasso, P. (2007). Towards reasoning and coordinating action in the mental space. International Journal of Neural Systems, 17(4), 1–13.

    Article  Google Scholar 

  • Morasso, P. (1981). Spatial control of arm movements. Experimental Brain Research, 42, 223–227.

    Article  Google Scholar 

  • Morasso, P., Sanguineti, V., & Spada, G. (1997). A computational theory of targeting movements based on force fields and topology representing networks. Neurocomputing, 15, 414–434.

    Article  Google Scholar 

  • Mussa Ivaldi, F. A., Morasso, P., & Zaccaria, R. (1988). Kinematic networks. A distributed model for representing and regularizing motor redundancy. Biological Cybernetics, 60, 1–16.

    Google Scholar 

  • Nakamura, Y., & Hanafusa, H. (1986). Inverse kinematics solutions with singularity robustness for robot manipulator control. Journal of Dynamic Systems, Measurement, and Control, 108, 163–171.

    Article  MATH  Google Scholar 

  • Nishiwaki, K., Kuffner, J., Kagami, S., Inaba, M., & Inoue, H. (2007). The experimental humanoid robot H7: A research platform for autonomous behaviour. Philosophical Transaction A: Mathematical Physical and Engineering Sciences, 365, 79–107.

    Article  Google Scholar 

  • Pagliano, S., Sanguineti, V., & Morasso, P. (1991). A neural framework for robot motor planning. In IEE/RSJ international workshop on intelligent robots and systems IROS ’91.

  • Rizzolatti, G., Fadiga, L., Fogassi, L., & Gallese, G. (1997). The space around us. Science, 190–191.

  • Shadmehr, R., & Mussa-Ivaldi, F. A. (1994). Adaptive representation of dynamics during learning of a motor task. The Journal of Neuroscience, 14, 3208–3224.

    Google Scholar 

  • Šoch, M., & Lórencz, R. (2005). Solving inverse kinematics—a new approach to the extended Jacobian technique. Acta Polytechnica, 45, 21–26.

    Google Scholar 

  • Taylor, J. G. (2003). The CODAM model and deficits of consciousness. In Lecture notes in computer science (Vol. 2774/2003). Berlin/Heidelberg: Springer.

    Google Scholar 

  • Tikhanoff, V., Cangelosi, A., Fitzpatrick, P., Metta, G., Natale, L., & Nori, F. (2008). An open-source simulator for cognitive robotics research. Cogprints, article 6238.

  • Tsuji, T., Morasso, P., Shigehashi, K., & Kaneko, M. (1995). Motion planning for manipulators using artificial potential field approach that can adjust convergence time of generated arm trajectory. Journal Robotics Society of Japan, 13, 285–290.

    Google Scholar 

  • Uno, Y., Kawato, M., & Suzuki, R. (1989). Formation and control of optimal trajectory in human multijoint arm movement: minimum torque-change model. Biological Cybernetics, 61, 89–101.

    Article  Google Scholar 

  • Visalberghi, E., & Tomasello, M. (1997). Primate causal understanding in the physical and in the social domains. Behavioral Processes, 42, 189–203.

    Article  Google Scholar 

  • Wampler, C. W. (1986). Manipulator inverse kinematic solutions based on vector formulations and damped least squares methods. IEEE Transaction on Systems, Man, and Cybernetics, 16, 93–101.

    Article  MATH  Google Scholar 

  • Whitney, D. E. (1969). Resolved motion rate control of manipulators and human prosthesis. IEEE Transactions on Man-Machine Systems, MMS-10, 47–53.

    Article  Google Scholar 

  • Wolovich, W. A., & Elliot, H. (1984). A computational technique for inverse kinematics. In Proceedings of the 23rd IEEE conf. on decision and control (pp. 1359–1363).

  • Wolpert, D. M., & Kawato, M. (1998). Multiple paired forward and inverse models for motor control. Neural Networks, 11, 1317–1329.

    Article  Google Scholar 

  • Zak, M. (1988). Terminal attractors for addressable memory in neural networks. Physics Letters, 133, 218–222.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Robotics, Brain and Cognitive Sciences Department, Italian Institute of Technology, Genoa, Italy

    V. Mohan, P. Morasso, G. Metta & G. Sandini

  2. DIST, University of Genoa, Genoa, Italy

    P. Morasso, G. Metta & G. Sandini

Authors
  1. V. Mohan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. Morasso
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. G. Metta
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. G. Sandini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to V. Mohan.

Electronic Supplementary Material

Below is the link to the electronic supplementary material. (WMW 21.1 MB)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mohan, V., Morasso, P., Metta, G. et al. A biomimetic, force-field based computational model for motion planning and bimanual coordination in humanoid robots. Auton Robot 27, 291–307 (2009). https://doi.org/10.1007/s10514-009-9127-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10514-009-9127-x

Keywords

Search

Navigation