Biologically inspired kinematic synergies enable linear balance control of a humanoid robot
- 593 Downloads
Despite many efforts, balance control of humanoid robots in the presence of unforeseen external or internal forces has remained an unsolved problem. The difficulty of this problem is a consequence of the high dimensionality of the action space of a humanoid robot, due to its large number of degrees of freedom (joints), and of non-linearities in its kinematic chains. Biped biological organisms face similar difficulties, but have nevertheless solved this problem. Experimental data reveal that many biological organisms reduce the high dimensionality of their action space by generating movements through linear superposition of a rather small number of stereotypical combinations of simultaneous movements of many joints, to which we refer as kinematic synergies in this paper. We show that by constructing two suitable non-linear kinematic synergies for the lower part of the body of a humanoid robot, balance control can in fact be reduced to a linear control problem, at least in the case of relatively slow movements. We demonstrate for a variety of tasks that the humanoid robot HOAP-2 acquires through this approach the capability to balance dynamically against unforeseen disturbances that may arise from external forces or from manipulating unknown loads.
KeywordsKinematic synergies Humanoid robot Balance control Biologically inspired Motion primitives
Written under partial support by the Austrian Science Fund FWF project # P17229-N04.
This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
ESM 1 (AVI 10,723 kb)
ESM 2 (AVI 5,101 kb)
- Åstrom KJ, Wittenmark B (1995) Adaptive control. In: Adaptive control, 2nd edn. Addison Wesley Longman, ReadingGoogle Scholar
- Baerlocher P, Boulic R (1998) Task-priority formulations for the kinematic control of highly redundant articulated structures. In: Proceedings of the 1998 IEEE/RSJ international conference on intelligent robots and systems, vol 1, pp 323–329, 13–17 October 1998Google Scholar
- d’Avella A, Saltiel P, Bizzi E (2003) Combinations of muscle synergies in the construction of a natural motor behavior. Nature 6(3): 300–308Google Scholar
- Goswami A, Kallem V (2004) Rate of change of angular momentum and balance maintenance of biped robot. In: Proceedings of the 2004 IEEEE international conference on robotics and automation ICRA, vol 4, pp 3785–3790, April 2004Google Scholar
- Hauser H, Neumann G, Ijspeert AJ, Maass W (2007) Biologically inspired kinematic synergies provide a new paradigm for balance control of humanoid robots. In: Proceedings of the 7th IEEE RAS/RSJ conference on humanoids robots (HUMANOIDS07), Pittsburgh, PA, December 2007Google Scholar
- Kagami S, Kanehiro F, Tamiya Y, Inaba M, Inoue H (2001) Auto-Balancer: an online dynamic balance compensation scheme for humanoid robots. In: Donald BR, Lynch K, Rus D (eds) Algorithmic and computational robotics: new directions. A K Peters Ltd., Wellesley, pp 329–340Google Scholar
- Kajita S, Kanehiro F, Kaneko K, Yokoi K, Hirukawa H (2001) The 3D linear inverted pendulum mode: a simple modeling for a biped walking patttern generation. In: Proceedings of the 2001 IEEEE/RSJ international conference on intelligent robots and systems, Maui, pp 239–246Google Scholar
- Kober J, Peters J (2009) Policy search for motor primitives in robotics. In: Advances in neural information processing systems 22 (NIPS 2008). MIT Press, Cambridge, pp 849–856Google Scholar
- Kuo BC, Golnaraghi F (2002) Automatic control systems, 8th edn. Wiley Inc., New YorkGoogle Scholar
- Lee S-H, Goswami A (2007) Reaction mass pendulum (rmp): an explicit model for centroidal angular momentum of humanoid robots. In: IEEE international conference on robotics and automation, 10–14 April 2007, pp 4667–4672Google Scholar
- Mahboobin A, Loughlin PJ, Redfern MS, Anderson SO, Atkeson CG, Hodgins JK (2008) Sensory adaptation in human balance control: lessons for biomimetic robotic bipeds. Neural Netw 21(4): 621–627. ISSN 0893-6080. Robotics and Neuroscience.Google Scholar
- Michel O (2004) Webots: professional mobile robot simulation. J Adv Robot Syst 1: 39–42Google Scholar
- Oppenheim AV, Willsky AS (1992) Signal and systems. Prentice-Hall Inc., Englewood CliffsGoogle Scholar
- Peterka RJ (2009) Comparison of human and humanoid robot control of upright stance. J Physiol Paris 103(3–5):149–158. ISSN 0928-4257. NeuroroboticsGoogle Scholar
- Sciavicco L, Siciliano B (1999) Modelling and control of robot manipulators, 2nd edn. Springer, BerlinGoogle Scholar