Abstract
This chapter discusses the possibility of using Laban notation to program humanoid robots. Laban notation documents human movements by a sequence of symbols that express movements as defined in the physical space. We show, by reasoning around the simple action of “taking a ball”, the flexibility of the notation that is able to describe an action with different level of details, depending on the final objective of the notation. These characteristics make Laban notation suitable as a high level language and as a motion segmentation tool for humanoid robot programming and control. The main problem in robotics is to express actions that are defined and operate in the physical space in terms of robot motions that originate in the robot motor control space. This is the fundamental robotics issue of inversion. We will first show how symbols used by Laban to describe human gestures can be translated in terms of actions for the robot by using a framework called Stack of Tasks. We will then report on an experience tending to implement on a simulated humanoid platform the notation score of a “Tutting Dance” executed by a dancer. Once the whole movement has been implemented on the robot, it has been again notated by using Laban notation. The comparison between both scores shows that robot’s movements are slightly different from dancer’s ones. We then discuss about plausible origins of these differences.
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Notes
- 1.
The original version of the Tutting dance can be found at the following link https://www.youtube.com/watch?v=082Akz8hGLY.
References
B. Choi, Y. Chen, Humanoid Motion Description Language (Lund University Cognitive Studies, 2002)
K. Kahol, P. Tripathi, S. Panchanathan, Automated gesture segmentation from dance sequences, in 2004 Proceedings of Sixth IEEE International Conference on Automatic Face and Gesture Recognition (IEEE, 2004), pp. 883–888
K. Kahol, P. Tripathi, S. Panchanathan, T. Rikakis, Gesture segmentation in complex motion sequences, in Proceedings IEEE International Conference on Image Processing (2003), pp. 105–108
A. Hutchinson Guest, Choreo-Graphics, A Comparison of Dance Notation Systems From the Fifteenth Century to the Present (Gordon and Breach Science Publishers S.A., New York, 1989)
M. Ghallab, D. Nau, P. Traverso, Automated Planning: Theory and Practice (Morgan Kaufmann Publishers, Elsevier, 2004)
E. Yoshida, J.P. Laumond, C. Esteves, O. Kanoun, A. Mallet, K. Sakaguchi, T. Yokoi, Motion autonomy for humanoids, experiments on hrp-2 no. 14. Comp. Animation Virtual Worlds 20, 5–6 (2009)
T. Siméon, J.-P. Laumond, J. Cortés, A. Sahbani, Manipulation planning with probabilistic roadmaps. Int. J. Robot. Res. 23(7–8), 729–746 (2004)
D. Sternad, S. Schaal, Segmentation of endpoint trajectories does not imply segmented control. Exp. Brain Res. 124(1), 118–136 (1999)
N. Mansard, F. Chaumette, Task sequencing for high-level sensor-based control. IEEE Trans. Rob. 23(1), 60–72 (2007)
C. Samson, B. Espiau, M. Le Borgne, Robot Control: The Task Function Approach (Oxford University Press, Oxford, 1991)
O. Khatib, A unified approach for motion and force control of robot manipulators: the operational space formulation. Robot. Autom. IEEE J. 3(1), 43–53 (1987)
O. Kanoun, J.-P. Laumond, E. Yoshida, Planning foot placements for a humanoid robot: a problem of inverse kinematics. Int. J. Robot. Res. 30(4), 476–485 (2011)
A.H. Guest, Labanotation: The System of Analyzing and Recording Movement (Psychology Press, 2005)
A. Knust. Dictionnaire usuel de Cinétographie Laban (Labanotation). Ressouvenances (2011)
T. Flash, N. Hogan, The coordination of arm movements—an experimentally confirmed mathematical-model. J. Neurosci. 5(7), 1688–1703 (1985)
P.J. Stapley, T. Pozzo, G. Cheron, A. Grishin, Does the coordination between posture and movement during human whole-body reaching ensure center of mass stabilization? Exp. Brain Res. 129(1), 134–146 (1999)
A. Sciutti, L. Demougeot, B. Berret, S. Toma, G. Sandini, C. Papaxanthis, T. Pozzo, Visual gravity influences arm movement planning. J. Neurophysiol. 107(12), 3433–3445 (2012)
J. Gaveau, B. Berret, L. Demougeot, L. Fadiga, T. Pozzo, C. Papaxanthis, Energy-related optimal control accounts for gravitational load: comparing shoulder, elbow, and wrist rotations. J. Neurophysiol. 111(1), 4–16 (2014)
R. Parent, Computer Animation: Algorithms and Techniques (Morgan Kaufmann Publishers, Elsevier, 2001)
Acknowledgements
This work is supported by ERC-ADG project 340050 Actanthrope. Authors thank Noëlle Simonet, professor of the Kinetography Laban at CNMDP (Conservatoire National de Musique et de Danse de Paris), for reviewing the Laban scores, and Tiphaine Jahier, dancer and Laban notator, for her participation to read notations and perform actions.
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Salaris, P., Abe, N., Laumond, JP. (2016). A Worked-Out Experience in Programming Humanoid Robots via the Kinetography Laban. In: Laumond, JP., Abe, N. (eds) Dance Notations and Robot Motion. Springer Tracts in Advanced Robotics, vol 111. Springer, Cham. https://doi.org/10.1007/978-3-319-25739-6_16
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