Abstract
Dance notation is a useful tool for providing a high-level description of complex movements to others. Given a description, human dancers are able to reproduce the intended detailed movements easily because they know how to move their joints to generate the described motion either through training or common sense. Robots, on the other hand, lack such training or common sense and therefore are incapable of creating fine details of the motion on their own. One possible way to “teach” a robot exactly how to move its joints is to use human motion data that include all the details. Unfortunately, using human motions for controlling robots is not straightforward due to various differences between human and robot bodies. This article will review some of the difficulties and give a brief survey on techniques for mapping human motions to robots.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
S. Coros, A. Karpathy, B. Jones, L. Reveret, M. van de Panne, Locomotion skills for simulated quadrupeds. ACM Trans. Graph. 30(4), Article 59 (2011)
M. Posa, C. Cantu, R. Tedrake, A direct method for trajectory optimization of rigid bodies. Int. J. Robot. Res. 33(1), 69–81 (2014)
Y. Nakanishi, Y. Asano, T. Kozuki, H. Mizoguchi, Y. Motegi, M. Osada, T. Shirai, J. Urata, K. Okada, M Inaba, Design concept of detail musculoskeletal humanoid “Kenshiro”—toward a real human body musculoskeletal simulator, in Proceedings of the 2012 IEEE-RAS International Conference on Humanoid Robots (2012), pp. 1–6
M. Vukobratovic, J. Stepanenko, On the stability of anthropomorphic systems. Math. Biosci. 15, 1–37 (1972)
S. Kajita, F. Kanehiro, K. Kaneko, K. Fujiwara, K. Harada, K. Yokoi, H. Hirukawa, Biped walking pattern generation by using preview control of zero-moment point, in Proceedings of IEEE International Conference on Robotics and Automation (2003), pp. 1620–1626
T. Sugihara, Simulated regulator to synthesize ZMP manipulation and foot location for autonomous control of biped robots, in Proceedings of IEEE International Conference on Robotics and Automation (2008), pp. 1264–1269
H. Hirukawa, S. Hattori, K. Harada, S. Kajita, K. Kaneko, K. Fujiwara, M. Morisawa, A universal stability criterion of the foot contact of legged robots-adios ZMP, in Proceedings of IEEE International Conference on Robotics and Automation (2006), pp. 1976–1983
Y. Zheng, K. Yamane, Adapting human motions to humanoid robots through time warping based on a general motion feasibility index, in Proceedings of IEEE International Conference on Robotics and Automation (2015, in press)
S. Nakaoka, A. Nakazawa, F. Kanehiro, K. Kaneko, M. Morisawa, H. Hirukawa, K. Ikeuchi, Learning from observation paradigm: leg task models for enabling a biped humanoid robot to imitate human dances. Int. J. Robot. Res. 26(8), 829–844 (2010)
K. Miura, M. Morisawa, F. Kanehiro, S. Kajia, K. Kaneko, K. Yokoi, Human-like walking with toe supporting for humanoids, in IEEE/RSJ International Conference on Intelligent Robots and Systems (2011), pp. 4428–4435
V.B. Zordan, J.K. Hodgins. Motion Capture-Driven Simulations that Hit and React, in Proceedings of ACM SIGGRAPH Symposium on Computer Animation, San Antonio, TX, July 2002, pp. 89–96
A. Safonova, J.K. Hodgins, N.S. Pollard, Synthesizing physically realistic human motion in low-dimensional, behavior-specific spaces. ACM Trans. Graph. 23(3), 514–521 (2004)
K.W. Sok, M.M. Kim, J.H. Lee, Simulating biped behaviors from human motion data. ACM Trans. Graph. 26(3), 107 (2007)
Ch. Ott, D.H. Lee, Y. Nakamura, Motion capture based human motion recognition and imitation by direct marker control, in Proceedings of IEEE-RAS International Conference on Humanoid Robots (2008), pp. 399–405
M. Da Silva, Y. Abe, J. Popović, Interactive simulation of stylized human locomotion. ACM Trans. Graph. 27(3), 82 (2008)
U. Muico, Y.J. Lee, J. Popović, Z. Popović, Contact-aware nonlinear control of dynamic characters. ACM Trans. Graph. 28(3), 81 (2009)
K.K. Yin, K. Loken, M. van de Panne, SIMBICON: simple biped locomotion control. ACM Trans. Graph. 26(3), 105 (2007)
S. Jain, Y. Ye, C.K. Liu, Optimization-based interactive motion synthesis. ACM Trans. Graph. 28(1), 10 (2009)
S. Coros, P. Beaudoin, M. van de Panne, Generalized biped walking control. ACM Trans. Graph. 29(4), Article 130 (2010)
K. Yamane, Y. Nakamura, Dynamics filter-concept and implementation of on-line motion generator for human figures. IEEE Trans. Robot. Autom. 19(3), 421–432 (2003)
K. Yamane, J.K. Hodgins, Simultaneous tracking and balancing of humanoid robots for imitating human motion capture data, in Proceedings of IEEE/RSJ International Conference on Intelligent Robot Systems, Oct 2009, pp. 2510–2517
K. Yamane, S.O. Anderson, J.K. Hodgins, Controlling humanoid robots with human motion data: experimental validation, in Proceedings of IEEE-RAS International Conference on Humanoid Robots, Nashville, TN, Dec 2010, pp. 504–510
K. Yamane, Practical kinematic and dynamic calibration methods for force-controlled humanoid robots, in Proceedings of IEEE-RAS International Conference on Humanoids Robots, Bled, Slovenia, Oct 2011
J. Kober, J.A. Bagnell, J. Peters, Reinforcement learning in robotics: A survey. Int. J. Robot. Res. 32(11), 1238–1274 (2013)
C.G. Atkeson, S. Schaal, Robot learning from demonstration, in International Conference on Machine Learning (1997), pp. 12–20
J. Nakanishi, J. Morimoto, G. Endo, G. Cheng, S. Schaal, M. Kawato, Learning from demonstration and adaptation of biped locomotion. Robot. Auton. Syst. 47, 79–91 (2004)
J. Kober, A. Wilhelm, E. Oztop, J. Peters, Reinforcement learning to adjust parameterized motor primitives to new situations. Auton. Robots 33, 361–379 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Yamane, K. (2016). Human Motion Tracking by Robots. 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_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-25739-6_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25737-2
Online ISBN: 978-3-319-25739-6
eBook Packages: EngineeringEngineering (R0)