research-article

Animating human lower limbs using contact-invariant optimization

Authors:

Emanuel Todorov,

Vladlen KoltunAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 32, Issue 6

Article No.: 203, Pages 1 - 8

https://doi.org/10.1145/2508363.2508365

Published: 01 November 2013 Publication History

Abstract

We present a trajectory optimization approach to animating human activities that are driven by the lower body. Our approach is based on contact-invariant optimization. We develop a simplified and generalized formulation of contact-invariant optimization that enables continuous optimization over contact timings. This formulation is applied to a fully physical humanoid model whose lower limbs are actuated by musculotendon units. Our approach does not rely on prior motion data or on task-specific controllers. Motion is synthesized from first principles, given only a detailed physical model of the body and spacetime constraints. We demonstrate the approach on a variety of activities, such as walking, running, jumping, and kicking. Our approach produces walking motions that quantitatively match ground-truth data, and predicts aspects of human gait initiation, incline walking, and locomotion in reduced gravity.

Supplementary Material

ZIP File (a203-mordatch.zip)

Supplemental material.

Download
20.36 MB

References

[1]

Al Borno, M., de Lasa, M., and Hertzmann, A. 2013. Trajectory optimization for full-body movements with complex contacts. IEEE Trans. Vis. Comput. Graph. 19, 8.

Digital Library

[2]

Alexander, R. M. 2003. Principles of Animal Locomotion. Princeton University Press.

[3]

An, K. N., Takahashi, K., Harrigan, T. P., and Chao, E. Y. 1984. Determination of muscle orientations and moment arms. Journal of Biomechanical Engineering 106, 3.

[4]

Anderson, F. C., and Pandy, M. G. 1999. A dynamic optimization solution for vertical jumping in three dimensions. Comput. Methods in Biomechanics and Biomedical Engineering 2, 3.

[5]

Anderson, F. C., and Pandy, M. G. 2001. Dynamic optimization of human walking. J. Biomech. Eng. 123, 5.

[6]

Anderson, F. C. 1999. A dynamic optimization solution for a complete cycle of normal gait. PhD thesis, U. of Texas at Austin.

[7]

Cavagna, G. A., Willems, P. A., and Heglund, N. C. 1998. Walking on Mars. Nature 393, 6686.

[8]

Cohen, M. F. 1992. Interactive spacetime control for animation. In Proc. SIGGRAPH.

Digital Library

[9]

Coros, S., Beaudoin, P., and van de Panne, M. 2010. Generalized biped walking control. ACM Trans. Graph. 29, 4.

Digital Library

[10]

de Lasa, M., Mordatch, I., and Hertzmann, A. 2010. Feature-based locomotion controllers. ACM Trans. Graph. 29, 4.

Digital Library

[11]

Delp, S. L., Loan, P., Hoy, M. G., Zajac, F. E., Topp, E. L., and Rosen, J. M. 1990. An interactive graphics-based model of the lower extremity to study orthopaedic surgical procedures. IEEE Transactions on Biomedical Engineering 37, 8.

[12]

Elble, R. J., Moody, C., Leffler, K., and Sinha, R. 1994. The initiation of normal walking. Movement Disorders 9, 2.

[13]

Erez, T., and Todorov, E. 2012. Trajectory optimization for domains with contacts using inverse dynamics. In Proc. IROS.

[14]

Faloutsos, P., van de Panne, M., and Terzopoulos, D. 2001. Composable controllers for physics-based character animation. In Proc. SIGGRAPH.

Digital Library

[15]

Fang, A. C., and Pollard, N. 2003. Efficient synthesis of physically valid human motion. ACM Trans. Graph. 22, 3.

Digital Library

[16]

Geyer, H., and Herr, H. 2010. A muscle-reflex model that encodes principles of legged mechanics produces human walking dynamics and muscle activities. IEEE Transactions on Neural Systems and Rehabilitation Engineering 18, 3.

[17]

Harris, C. M., and Wolpert, D. M. 1998. Signal-dependent noise determines motor planning. Nature 394.

[18]

Hodgins, J. K., Wooten, W. L., Brogan, D. C., and O'Brien, J. F. 1995. Animating human athletics. In Proc. SIGGRAPH.

Digital Library

[19]

Lay, A. N., Hass, C. J., and Gregor, R. J. 2006. The effects of sloped surfaces on locomotion: a kinematic and kinetic analysis. Journal of Biomechanics 39.

[20]

Lee, S.-H., Sifakis, E., and Terzopoulos, D. 2009. Comprehensive biomechanical modeling and simulation of the upper body. ACM Trans. Graph. 28, 4.

Digital Library

[21]

Liu, C. K., and Popović, Z. 2002. Synthesis of complex dynamic character motion from simple animations. ACM Trans. Graph. 21, 3.

Digital Library

[22]

Liu, C. K., Hertzmann, A., and Popović, Z. 2005. Learning physics-based motion style with nonlinear inverse optimization. ACM Trans. Graph. 24, 3.

Digital Library

[23]

Margaria, R., and Cavagna, G. A. 1964. Human locomotion in subgravity. Aerospace Medicine 35.

[24]

Martins, J. R. R. A., Sturdza, P., and Alonso, J. J. 2003. The complex-step derivative approximation. ACM Trans. Math. Softw. 29, 3.

Digital Library

[25]

Millard, M., and Delp, S. L. 2012. A computationally efficient muscle model. In ASME Summer Bioeng. Conf.

[26]

Millard, M., Uchida, T., Seth, A., and Delp, S. L. 2013. Flexing computational muscle: Modeling and simulation of musculotendon dynamics. Journal of Biomech. Eng. 135.

[27]

Mordatch, I., Popović, Z., and Todorov, E. 2012. Contact-invariant optimization for hand manipulation. In Proc. SCA.

Digital Library

[28]

Mordatch, I., Todorov, E., and Popović, Z. 2012. Discovery of complex behaviors through contact-invariant optimization. ACM Trans. Graph. 31, 4.

Digital Library

[29]

Perry, J., and Burnfield, J. 2010. Gait Analysis: Normal and Pathological Function. Slack Incorporated.

[30]

Popović, Z., and Witkin, A. 1999. Physically based motion transformation. In Proc. SIGGRAPH.

Digital Library

[31]

Safonova, A., Hodgins, J. K., and Pollard, N. 2004. Synthesizing physically realistic human motion in low-dimensional, behavior-specific spaces. ACM Trans. Graph. 23, 3.

Digital Library

[32]

Srinivasan, M., and Ruina, A. 2006. Computer optimization of a minimal biped model discovers walking and running. Nature 439.

[33]

Sulejmanpašić, A., and Popović, J. 2005. Adaptation of performed ballistic motion. ACM Trans. Graph. 24, 1.

Digital Library

[34]

Tassa, Y., and Todorov, E. 2010. Stochastic complementarity for local control of discontinuous dynamics. In Proc. RSS.

[35]

Todorov, E., Erez, T., and Tassa, Y. 2012. MuJoCo: A physics engine for model-based control. In Proc. IROS.

[36]

Wampler, K., and Popović, Z. 2009. Optimal gait and form for animal locomotion. ACM Trans. Graph. 28, 3.

Digital Library

[37]

Wang, J. M., Hamner, S. R., Delp, S. L., and Koltun, V. 2012. Optimizing locomotion controllers using biologically-based actuators and objectives. ACM Trans. Graph. 31, 4.

Digital Library

[38]

Witkin, A. P., and Kass, M. 1988. Spacetime constraints. In Proc. SIGGRAPH.

Digital Library

[39]

Yin, K., Loken, K., and van de Panne, M. 2007. SIMBICON: Simple biped locomotion control. ACM Trans. Graph. 26, 3.

Digital Library

Cited By

Seethapathi NJain ASrinivasan M(2024)Walking speeds are lower for short distance and turning locomotion: Experiments and modeling in low-cost prosthesis usersPLOS ONE10.1371/journal.pone.029599319:1(e0295993)Online publication date: 2-Jan-2024
https://doi.org/10.1371/journal.pone.0295993
Luo CTang PMa YHuang D(2024)Learning to Play Guitar with Robotic HandsComputer Graphics Forum10.1111/cgf.15166Online publication date: 9-Oct-2024
https://doi.org/10.1111/cgf.15166
Xie PXu WTang TYu ZLu C(2024)MS-MANO: Enabling Hand Pose Tracking with Biomechanical Constraints2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.00231(2382-2392)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.00231
Show More Cited By

Index Terms

Animating human lower limbs using contact-invariant optimization
1. Computing methodologies
  1. Computer graphics
    1. Animation

Recommendations

Kinematics Modeling and Simulation Analysis of Human Upper Limbs for Rod Lifting
ICBBS '23: Proceedings of the 2023 12th International Conference on Bioinformatics and Biomedical Science

With the development of technology, exoskeleton robots have wide application in industrialization and civilian. The purpose of this paper is to analyze the kinematics and kinetics during rod lifting, thus providing the necessary suggestion for power grid ...
Development of Abnormal Gait Detection and Vibratory Stimulation System on Lower Limbs to Improve Gait Stability

The purpose of this study is to develop an abnormal gait detection algorithm and a vibratory stimulation system on a lower limb to improve gait stability and prevent falls. The system consists of a gait measurement module, an abnormal gait detection ...
Research on EMG Signal of Human Lower Limbs Based on Empirical Mode Decomposition
2022 IEEE International Conference on Mechatronics and Automation (ICMA)
To recognize Electromyography (EMG) signal of lower limbs more effectively, this paper analyzed EMG signal of gastrocnemius muscle during dorsal flexion, plantar flexion, knee flexion, knee flexion and hip flexion; Based on advantages of Empirical Mode ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 32, Issue 6

November 2013

671 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/2508363

Issue’s Table of Contents

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 November 2013

Published in TOG Volume 32, Issue 6

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

61
Total Citations
View Citations
688
Total Downloads

Downloads (Last 12 months)25
Downloads (Last 6 weeks)3

Reflects downloads up to 22 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Seethapathi NJain ASrinivasan M(2024)Walking speeds are lower for short distance and turning locomotion: Experiments and modeling in low-cost prosthesis usersPLOS ONE10.1371/journal.pone.029599319:1(e0295993)Online publication date: 2-Jan-2024
https://doi.org/10.1371/journal.pone.0295993
Luo CTang PMa YHuang D(2024)Learning to Play Guitar with Robotic HandsComputer Graphics Forum10.1111/cgf.15166Online publication date: 9-Oct-2024
https://doi.org/10.1111/cgf.15166
Xie PXu WTang TYu ZLu C(2024)MS-MANO: Enabling Hand Pose Tracking with Biomechanical Constraints2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.00231(2382-2392)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.00231
Zhu HMeduri ARighetti L(2023)Efficient Object Manipulation Planning with Monte Carlo Tree Search2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)10.1109/IROS55552.2023.10341813(10628-10635)Online publication date: 1-Oct-2023
https://doi.org/10.1109/IROS55552.2023.10341813
Tao TWilson MGou Rvan de Panne M(2022)Learning to Get UpACM SIGGRAPH 2022 Conference Proceedings10.1145/3528233.3530697(1-10)Online publication date: 27-Jul-2022
https://dl.acm.org/doi/10.1145/3528233.3530697
Weng JHashemi EArami A(2022)Adaptive Reference Inverse Optimal Control for Natural Walking With Musculoskeletal ModelsIEEE Transactions on Neural Systems and Rehabilitation Engineering10.1109/TNSRE.2022.318069030(1567-1575)Online publication date: 2022
https://doi.org/10.1109/TNSRE.2022.3180690
Averta GAverta G(2022)Natural Motion: Embedding Human-Likeliness in Robot MovementsHuman-Aware Robotics: Modeling Human Motor Skills for the Design, Planning and Control of a New Generation of Robotic Devices10.1007/978-3-030-92521-5_8(143-170)Online publication date: 25-Jan-2022
https://doi.org/10.1007/978-3-030-92521-5_8
Yin ZYang ZVan De Panne MYin K(2021)Discovering diverse athletic jumping strategiesACM Transactions on Graphics10.1145/3450626.345981740:4(1-17)Online publication date: 19-Jul-2021
https://dl.acm.org/doi/10.1145/3450626.3459817
Bangaru SMichel JMu KBernstein GLi TRagan-Kelley J(2021)Systematically differentiating parametric discontinuitiesACM Transactions on Graphics10.1145/3450626.345977540:4(1-18)Online publication date: 19-Jul-2021
https://dl.acm.org/doi/10.1145/3450626.3459775
Peng XMa ZAbbeel PLevine SKanazawa A(2021)AMPACM Transactions on Graphics10.1145/3450626.345967040:4(1-20)Online publication date: 19-Jul-2021
https://dl.acm.org/doi/10.1145/3450626.3459670
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Issue’s Table of Contents