Whole Body Model Predictive Control with a Memory of Motion: Experiments on a Torque-Controlled Talos

Ewen Dantec; Rohan Budhiraja; Adria Roig; Teguh Lembono; Guilhem Saurel; Olivier  Stasse; Pierre Fernbach; Steve Tonneau; Sethu Vijayakumar; Sylvain Calinon; Michel Taïx; Nicolas  Mansard

doi:10.1109/ICRA48506.2021.9560742

Whole Body Model Predictive Control with a Memory of Motion: Experiments on a Torque-Controlled Talos

Ewen Dantec, Rohan Budhiraja, Adria Roig, Teguh Lembono, Guilhem Saurel, Olivier Stasse, Pierre Fernbach, Steve Tonneau, Sethu Vijayakumar, Sylvain Calinon, Michel Taïx, Nicolas Mansard

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract / Description of output

This paper presents the first successful experiment implementing whole-body model predictive control with state feedback on a torque-control humanoid robot. We demonstrate that our control scheme is able to do whole-body target tracking, control the balance in front of strong external perturbations and avoid collision with an external object. The key elements for this success are threefold. First, optimal control over a receding horizon is implemented with Crocoddyl, an optimal control library based on differential dynamics programming, providing state-feedback control in less than 10 ms. Second, a warm start strategy based on memory of motion has been implemented to overcome the sensitivity of the optimal control solver to initial conditions. Finally, the optimal trajectories are executed by a low-level torque controller, feedbacking on direct torque measurement at high frequency. This paper provides the details of the method, along with analytical benchmarks with the real humanoid robot Talos.

A video of the experiment is available at https://peertube.laas.fr/videos/watch/cbc25927-337c-4635-a1bc-153b9aeb4135

Original language	English
Title of host publication	2021 IEEE International Conference on Robotics and Automation (ICRA)
Publisher	Institute of Electrical and Electronics Engineers
Pages	8202-8208
Number of pages	7
ISBN (Electronic)	978-1-7281-9077-8
ISBN (Print)	978-1-7281-9078-5
DOIs	https://doi.org/10.1109/ICRA48506.2021.9560742
Publication status	Published - 18 Oct 2021
Event	2021 IEEE International Conference on Robotics and Automation - Xi'an, China Duration: 30 May 2021 → 5 Jun 2021

Publication series

Name
ISSN (Print)	1050-4729
ISSN (Electronic)	2577-087X

Conference

Conference	2021 IEEE International Conference on Robotics and Automation
Abbreviated title	ICRA 2021
Country/Territory	China
City	Xi'an
Period	30/05/21 → 5/06/21

Access to Document

10.1109/ICRA48506.2021.9560742Licence: All Rights Reserved

Whole Body Model_DANTEC_DOA28022021_AFVAccepted author manuscript, 1.57 MBLicence: All Rights Reserved

https://ieeexplore.ieee.org/document/9560742Licence: All Rights Reserved

MEMMO - Memory of Motion
Vijayakumar, S.
EU government bodies
1/01/18 → 30/06/22
Project: Research

Cite this

Dantec, E., Budhiraja, R., Roig, A., Lembono, T., Saurel, G., Stasse, O., Fernbach, P., Tonneau, S., Vijayakumar, S., Calinon, S., Taïx, M., & Mansard, N. (2021). Whole Body Model Predictive Control with a Memory of Motion: Experiments on a Torque-Controlled Talos. In 2021 IEEE International Conference on Robotics and Automation (ICRA) (pp. 8202-8208). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICRA48506.2021.9560742

@inproceedings{8a5979532d7e445ba9829df202c78226,

title = "Whole Body Model Predictive Control with a Memory of Motion: Experiments on a Torque-Controlled Talos",

abstract = "This paper presents the first successful experiment implementing whole-body model predictive control with state feedback on a torque-control humanoid robot. We demonstrate that our control scheme is able to do whole-body target tracking, control the balance in front of strong external perturbations and avoid collision with an external object. The key elements for this success are threefold. First, optimal control over a receding horizon is implemented with Crocoddyl, an optimal control library based on differential dynamics programming, providing state-feedback control in less than 10 ms. Second, a warm start strategy based on memory of motion has been implemented to overcome the sensitivity of the optimal control solver to initial conditions. Finally, the optimal trajectories are executed by a low-level torque controller, feedbacking on direct torque measurement at high frequency. This paper provides the details of the method, along with analytical benchmarks with the real humanoid robot Talos.A video of the experiment is available at https://peertube.laas.fr/videos/watch/cbc25927-337c-4635-a1bc-153b9aeb4135 ",

author = "Ewen Dantec and Rohan Budhiraja and Adria Roig and Teguh Lembono and Guilhem Saurel and Olivier Stasse and Pierre Fernbach and Steve Tonneau and Sethu Vijayakumar and Sylvain Calinon and Michel Ta{\"i}x and Nicolas Mansard",

year = "2021",

month = oct,

day = "18",

doi = "10.1109/ICRA48506.2021.9560742",

language = "English",

isbn = "978-1-7281-9078-5",

publisher = "Institute of Electrical and Electronics Engineers",

pages = "8202--8208",

booktitle = "2021 IEEE International Conference on Robotics and Automation (ICRA)",

address = "United States",

note = "2021 IEEE International Conference on Robotics and Automation, ICRA 2021 ; Conference date: 30-05-2021 Through 05-06-2021",

}

Dantec, E, Budhiraja, R, Roig, A, Lembono, T, Saurel, G, Stasse, O, Fernbach, P, Tonneau, S , Vijayakumar, S, Calinon, S, Taïx, M & Mansard, N 2021, Whole Body Model Predictive Control with a Memory of Motion: Experiments on a Torque-Controlled Talos. in 2021 IEEE International Conference on Robotics and Automation (ICRA) . Institute of Electrical and Electronics Engineers, pp. 8202-8208, 2021 IEEE International Conference on Robotics and Automation, Xi'an, China, 30/05/21. https://doi.org/10.1109/ICRA48506.2021.9560742

Whole Body Model Predictive Control with a Memory of Motion: Experiments on a Torque-Controlled Talos. / Dantec, Ewen; Budhiraja, Rohan; Roig, Adria et al.
2021 IEEE International Conference on Robotics and Automation (ICRA) . Institute of Electrical and Electronics Engineers, 2021. p. 8202-8208.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Whole Body Model Predictive Control with a Memory of Motion: Experiments on a Torque-Controlled Talos

AU - Dantec, Ewen

AU - Budhiraja, Rohan

AU - Roig, Adria

AU - Lembono, Teguh

AU - Saurel, Guilhem

AU - Stasse, Olivier

AU - Fernbach, Pierre

AU - Tonneau, Steve

AU - Vijayakumar, Sethu

AU - Calinon, Sylvain

AU - Taïx, Michel

AU - Mansard, Nicolas

PY - 2021/10/18

Y1 - 2021/10/18

N2 - This paper presents the first successful experiment implementing whole-body model predictive control with state feedback on a torque-control humanoid robot. We demonstrate that our control scheme is able to do whole-body target tracking, control the balance in front of strong external perturbations and avoid collision with an external object. The key elements for this success are threefold. First, optimal control over a receding horizon is implemented with Crocoddyl, an optimal control library based on differential dynamics programming, providing state-feedback control in less than 10 ms. Second, a warm start strategy based on memory of motion has been implemented to overcome the sensitivity of the optimal control solver to initial conditions. Finally, the optimal trajectories are executed by a low-level torque controller, feedbacking on direct torque measurement at high frequency. This paper provides the details of the method, along with analytical benchmarks with the real humanoid robot Talos.A video of the experiment is available at https://peertube.laas.fr/videos/watch/cbc25927-337c-4635-a1bc-153b9aeb4135

AB - This paper presents the first successful experiment implementing whole-body model predictive control with state feedback on a torque-control humanoid robot. We demonstrate that our control scheme is able to do whole-body target tracking, control the balance in front of strong external perturbations and avoid collision with an external object. The key elements for this success are threefold. First, optimal control over a receding horizon is implemented with Crocoddyl, an optimal control library based on differential dynamics programming, providing state-feedback control in less than 10 ms. Second, a warm start strategy based on memory of motion has been implemented to overcome the sensitivity of the optimal control solver to initial conditions. Finally, the optimal trajectories are executed by a low-level torque controller, feedbacking on direct torque measurement at high frequency. This paper provides the details of the method, along with analytical benchmarks with the real humanoid robot Talos.A video of the experiment is available at https://peertube.laas.fr/videos/watch/cbc25927-337c-4635-a1bc-153b9aeb4135

U2 - 10.1109/ICRA48506.2021.9560742

DO - 10.1109/ICRA48506.2021.9560742

M3 - Conference contribution

SN - 978-1-7281-9078-5

SP - 8202

EP - 8208

BT - 2021 IEEE International Conference on Robotics and Automation (ICRA)

PB - Institute of Electrical and Electronics Engineers

T2 - 2021 IEEE International Conference on Robotics and Automation

Y2 - 30 May 2021 through 5 June 2021

ER -

Whole Body Model Predictive Control with a Memory of Motion: Experiments on a Torque-Controlled Talos

Abstract / Description of output

Publication series

Conference

Access to Document

Fingerprint

Projects

MEMMO - Memory of Motion

Cite this