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Momentum control with hierarchical inverse dynamics on a torque-controlled humanoid

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Abstract

Hierarchical inverse dynamics based on cascades of quadratic programs have been proposed for the control of legged robots. They have important benefits but to the best of our knowledge have never been implemented on a torque controlled humanoid where model inaccuracies, sensor noise and real-time computation requirements can be problematic. Using a reformulation of existing algorithms, we propose a simplification of the problem that allows to achieve real-time control. Momentum-based control is integrated in the task hierarchy and a LQR design approach is used to compute the desired associated closed-loop behavior and improve performance. Extensive experiments on various balancing and tracking tasks show very robust performance in the face of unknown disturbances, even when the humanoid is standing on one foot. Our results demonstrate that hierarchical inverse dynamics together with momentum control can be efficiently used for feedback control under real robot conditions.

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Notes

  1. Part of the material presented in this paper has been presented at the 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

  2. We originally proposed the simplification in a technical note (Herzog et al. 2013).

  3. The part of \(\mathbf{M_l}\) multiplying the base acceleration is always full rank.

  4. The movie is also available on www.youtube.com/watch?v=jMj3Uv2Q8Xg.

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Acknowledgments

We would like to thank Ambarish Goswami and Seungkook Yun for hosting us at the Honda Research Institute for one week and for their precious help in understanding the original momentum-based controller. We would also like to thank Ambarish Goswami and Sung-Hee Lee for giving us an early access to their publication. We are also grateful to Daniel Kappler for helping us with the videos. Last, but not least, we would like to thank the anonymous reviewers for their very valuable comments that helped improve the final version of the paper. This research was supported in part by National Science Foundation Grants IIS-1205249, IIS-1017134, CNS-0960061, EECS-0926052, the DARPA program on Autonomous Robotic Manipulation, the Office of Naval Research, the Okawa Foundation, and the Max-Planck-Society. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding organizations.

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Authors and Affiliations

  1. Autonomous Motion Department, Max-Planck Institute Intelligent Systems, 72076, Tübingen, Germany

    Alexander Herzog, Felix Grimminger, Stefan Schaal & Ludovic Righetti

  2. Computational Learning and Motor Control Lab, University of Southern California, Los Angeles, CA, 90089, USA

    Nicholas Rotella, Sean Mason, Stefan Schaal & Ludovic Righetti

Authors
  1. Alexander Herzog
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  2. Nicholas Rotella
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  3. Sean Mason
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  4. Felix Grimminger
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  5. Stefan Schaal
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  6. Ludovic Righetti
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Corresponding author

Correspondence to Alexander Herzog.

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Herzog, A., Rotella, N., Mason, S. et al. Momentum control with hierarchical inverse dynamics on a torque-controlled humanoid. Auton Robot 40, 473–491 (2016). https://doi.org/10.1007/s10514-015-9476-6

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