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 |
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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 | |
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 | |
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Publisher | IEEE |
ISSN (Print) | 1050-4729 |
ISSN (Electronic) | 2577-087X |
Conference
Conference | 2020 International Conference on Robotics and Automation |
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Abbreviated title | ICRA 2020 |
Country/Territory | France |
City | Virtual conference |
Period | 31/05/20 → 31/08/20 |
Internet address |