Abstract
Expressiveness and naturalness in robotic motions and behaviors can be replicated with the usage of captured human movements. Considering dance as a complex and expressive type of motion, in this paper we propose a method for generating humanoid dance motions transferred from human motion capture (MoCap) data. Motion data of samba dance was synchronized to samba music, manually annotated by experts, in order to build a spatiotemporal representation of the dance movement with variability, in relation to the respective musical temporal structure (musical meter). This enabled the determination and generation of variable dance key-poses according to the captured human body model. In order to retarget these key-poses from the original human model into the considered humanoid morphology, we propose methods for resizing and adapting the original trajectories to the robot joints, overcoming its varied kinematic constraints. Finally, a method for generating the angles for each robot joint is presented, enabling the reproduction of the desired poses in a simulated humanoid robot NAO. The achieved results validated our approach, suggesting that our method can generate poses from motion capture and reproduce them on a humanoid robot with a good degree of similarity.
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
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Simspark comunity, http://simspark.sourceforge.net/wiki
Gouaillier, D., Hugel, V., Blazevic, P., Kilner, C., Monceaux, J., Marnier, B., Serre, J., Serre, J.: The nao humanoid: a combination of performance and affordability. Computing Research Repository CoRR, abs/0807 3, 1–10 (2008)
Kim, S., Kim, C.H., You, B., Oh S.: Stable Whole-Body Motion Generation for Humanoid Robots to Imitate Human Motions. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), St. Louis, MO, USA, pp. 2518–2524. IEEE (October 2009)
Kim, T.-h., Park Il, S., Shin, S.Y.: Rhythmic-Motion Synthesis based on Motion-Beat Analysis. ACM Transactions on Graphics 22(3), 392 (2003)
Lau, N., Reis, L.P.: High-level coordination methodologies in soccer robotics, robotic soccer, pp. 167–192. Itech Education and Publishing (2007)
Nagata, N., Okumoto, K., Iwai, D., Toro, F., Inokuchi, S.: Analysis and Synthesis of Latin Dance Using Motion Capture Data. Advances in Multimedia Information Processing 3333, 39–44 (2005)
Nakaoka, S., Nakazawa, A., Yokoi, K., Hirukawa, H., Ikeuchi, K.: Generating Whole Body Motions for a Biped Humanoid Robot from Captured Human Dances. In: IEEE International Conference on Robotics and Automation (ICRA), vol. 3, pp. 3905–3910. IEEE (2003)
Nakazawa, A., Nakaoka, S., Ikeuchi, K., Yokoi, K.: Imitating human dance motions through motion structure analysis. In: Proceedings of the International Conference on Intelligent Robots and Systems (IROS), pp. 2539–2544 (2002)
Naveda, L., Leman, M.: The spatiotemporal representation of dance and music gestures using Topological Gesture Analysis (TGA). Music Perception 28(1), 93–111 (2010)
Okamoto, T., Shiratori, T., Kudoh, S., Ikeuchi, K.: Temporal Scaling of Leg Motion for Music Feedback System of a Dancing Humanoid Robot. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Taipei, Taiwan, pp. 2256–2263 (2010)
Oliveira, J.L., Naveda, L., Gouyon, F., Leman, M., Reis, L.P.: Synthesis of Dancing Motions Based on a Compact Topological Representation of Dance Styles. In: Workshop on Robots and Musical Expressions (WRME) at IROS, Taipei, Taiwan (2010)
Oliveira, J.L., Naveda, L., Gouyon, F., Leman, M., Reis, L.P., Sousa, P.: A Spatiotemporal Method for Synthesizing Expressive Dance Movements of Virtual Humanoid Characters. Special Issue on Music Content Processing by and for Robots from the EURASIP Journal on Audio, Speech, and Music Processing (submitted to, 2011)
Lau, N., Reis, L.P.: FC Portugal 2001 Team Description: Flexible Teamwork and Configurable Strategy. In: Birk, A., Coradeschi, S., Tadokoro, S. (eds.) RoboCup 2001. LNCS (LNAI), vol. 2377, pp. 515–518. Springer, Heidelberg (2002)
Ruchanurucks, M., Nakaoka, S., Kudoh, S., Ikeuchi, K.: Generation of Humanoid Robot Motions with Physical Constraints using Hierarchical B-Spline. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 674–679. IEEE (2005)
Ruchanurucks, M., Nakaoka, S., Kudoh, S., Ikeuchi, K.: Humanoid Robot Motion Generation with Sequential Physical Constraints. In: IEEE International Conference on Robotics and Automation (ICRA), Orlando, FL, USA, pp. 2649–2654. IEEE (2006)
Shiratori, T.: Synthesis of Dance Performance Based on Analyses of Human Motion and Music. Phd thesis, University of Tokyo (2006)
Shiratori, T., Kudoh, S., Nakaoka, S., Ikeuchi, K.: Temporal Scaling of Upper Body Motion for Sound Feedback System of a Dancing Humanoid Robot. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), San Diego CA, USA, pp. 3251–3257. IEEE (October 2007)
Shiratori, T., Nakazawa, A., Ikeuchi, K.: Dancing-to-Music Character Animation. In: EUROGRAPHICS, vol. 25, pp. 449–458 (September 2006)
Shiratori, T., Nakazawa, A., Ikeuchi, K.: Synthesizing Dance Performance using Musical and Motion Features. In: IEEE International Conference on Robotics and Automation, pp. 3654–3659. IEEE (2006)
Zhao, X., Huang, Q., Du, P., Wen, D., Li, K.: Humanoid Kinematics Mapping and Similarity Evaluation Based on Human Motion Capture. In: International Conference on Information Acquisition (ICIA), pp. 426–431. IEEE (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sousa, P., Oliveira, J.L., Reis, L.P., Gouyon, F. (2011). Humanized Robot Dancing: Humanoid Motion Retargeting Based in a Metrical Representation of Human Dance Styles. In: Antunes, L., Pinto, H.S. (eds) Progress in Artificial Intelligence. EPIA 2011. Lecture Notes in Computer Science(), vol 7026. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24769-9_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-24769-9_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24768-2
Online ISBN: 978-3-642-24769-9
eBook Packages: Computer ScienceComputer Science (R0)