Unified Push Recovery Fundamentals: Inspiration from Human Study

Christopher McGreavy; Kai Yuan; Daniel Gordon; Kang Tan; Wouter J. Wolfslag; Sethu Vijayakumar; Zhibin Li

doi:10.1109/ICRA40945.2020.9196911

Unified Push Recovery Fundamentals: Inspiration from Human Study

Christopher McGreavy, Kai Yuan, Daniel Gordon, Kang Tan, Wouter J. Wolfslag, Sethu Vijayakumar, Zhibin Li

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

Abstract

Currently for balance recovery, humans outperform humanoid robots that used hand-designed controllers. This study aims to close this gap by finding control principles which are shared across all recovery strategies. We do this by formulating experiments to test human strategies and quantify criteria for identifying strategies. A minimum jerk control principle is shown to accurately recreate human CoM recovery trajectories. Using this principle, we formulate a Model-Predictive Control (MPC) for the use in floating base systems (eg legged robots). The feasibility of generated motions from the MPC for implementation on the real robot is then validated using an Inverted Pendulum Model. Finally, we demonstrate improved capability over humans by tuning the parameters for time-optimal recovery performance.

Original language	English
Title of host publication	2020 IEEE International Conference on Robotics and Automation (ICRA)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	10876-10882
Number of pages	7
ISBN (Electronic)	978-1-7281-7395-5
ISBN (Print)	978-1-7281-7396-2
DOIs	https://doi.org/10.1109/ICRA40945.2020.9196911
Publication status	Published - 15 Sep 2020
Event	2020 International Conference on Robotics and Automation - Virtual conference, France Duration: 31 May 2020 → 31 Aug 2020 https://www.icra2020.org/

Publication series

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

Conference

Conference	2020 International Conference on Robotics and Automation
Abbreviated title	ICRA 2020
Country	France
City	Virtual conference
Period	31/05/20 → 31/08/20
Internet address	https://www.icra2020.org/

Access to Document

10.1109/ICRA40945.2020.9196911Licence: All Rights Reserved

Unified Push Recovery_MCGREAVY_DOA22012020_AFVAccepted author manuscript, 4.79 MB

https://ieeexplore.ieee.org/document/9196911Licence: All Rights Reserved

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Cite this

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title = "Unified Push Recovery Fundamentals: Inspiration from Human Study",

abstract = "Currently for balance recovery, humans outperform humanoid robots that used hand-designed controllers. This study aims to close this gap by finding control principles which are shared across all recovery strategies. We do this by formulating experiments to test human strategies and quantify criteria for identifying strategies. A minimum jerk control principle is shown to accurately recreate human CoM recovery trajectories. Using this principle, we formulate a Model-Predictive Control (MPC) for the use in floating base systems (eg legged robots). The feasibility of generated motions from the MPC for implementation on the real robot is then validated using an Inverted Pendulum Model. Finally, we demonstrate improved capability over humans by tuning the parameters for time-optimal recovery performance.",

author = "Christopher McGreavy and Kai Yuan and Daniel Gordon and Kang Tan and Wolfslag, {Wouter J.} and Sethu Vijayakumar and Zhibin Li",

year = "2020",

month = sep,

day = "15",

doi = "10.1109/ICRA40945.2020.9196911",

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publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

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booktitle = "2020 IEEE International Conference on Robotics and Automation (ICRA)",

address = "United States",

note = "2020 International Conference on Robotics and Automation, ICRA 2020 ; Conference date: 31-05-2020 Through 31-08-2020",

url = "https://www.icra2020.org/",

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McGreavy, C, Yuan, K, Gordon, D, Tan, K, Wolfslag, WJ, Vijayakumar, S & Li, Z 2020, Unified Push Recovery Fundamentals: Inspiration from Human Study. in 2020 IEEE International Conference on Robotics and Automation (ICRA). Institute of Electrical and Electronics Engineers (IEEE), pp. 10876-10882, 2020 International Conference on Robotics and Automation, Virtual conference, France, 31/05/20. https://doi.org/10.1109/ICRA40945.2020.9196911

Unified Push Recovery Fundamentals: Inspiration from Human Study. / McGreavy, Christopher; Yuan, Kai; Gordon, Daniel; Tan, Kang; Wolfslag, Wouter J.; Vijayakumar, Sethu ; Li, Zhibin.

2020 IEEE International Conference on Robotics and Automation (ICRA). Institute of Electrical and Electronics Engineers (IEEE), 2020. p. 10876-10882.

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

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T1 - Unified Push Recovery Fundamentals: Inspiration from Human Study

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AU - Vijayakumar, Sethu

AU - Li, Zhibin

PY - 2020/9/15

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N2 - Currently for balance recovery, humans outperform humanoid robots that used hand-designed controllers. This study aims to close this gap by finding control principles which are shared across all recovery strategies. We do this by formulating experiments to test human strategies and quantify criteria for identifying strategies. A minimum jerk control principle is shown to accurately recreate human CoM recovery trajectories. Using this principle, we formulate a Model-Predictive Control (MPC) for the use in floating base systems (eg legged robots). The feasibility of generated motions from the MPC for implementation on the real robot is then validated using an Inverted Pendulum Model. Finally, we demonstrate improved capability over humans by tuning the parameters for time-optimal recovery performance.

AB - Currently for balance recovery, humans outperform humanoid robots that used hand-designed controllers. This study aims to close this gap by finding control principles which are shared across all recovery strategies. We do this by formulating experiments to test human strategies and quantify criteria for identifying strategies. A minimum jerk control principle is shown to accurately recreate human CoM recovery trajectories. Using this principle, we formulate a Model-Predictive Control (MPC) for the use in floating base systems (eg legged robots). The feasibility of generated motions from the MPC for implementation on the real robot is then validated using an Inverted Pendulum Model. Finally, we demonstrate improved capability over humans by tuning the parameters for time-optimal recovery performance.

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DO - 10.1109/ICRA40945.2020.9196911

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BT - 2020 IEEE International Conference on Robotics and Automation (ICRA)

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