Intelligent Systems
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Impedance Optimization for Uncertain Contact Interactions Through Risk Sensitive Optimal Control

2021

Article

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This paper addresses the problem of computing optimal impedance schedules for legged locomotion tasks involving complex contact interactions. We formulate the problem of impedance regulation as a trade-off between disturbance rejection and measurement uncertainty. We extend a stochastic optimal control algorithm known as Risk Sensitive Control to take into account measurement uncertainty and propose a formal way to include such uncertainty for unknown contact locations. The approach can efficiently generate optimal state and control trajectories along with local feedback control gains, i.e. impedance schedules. Extensive simulations demonstrate the capabilities of the approach in generating meaningful stiffness and damping modulation patterns before and after contact interaction. For example, contact forces are reduced during early contacts, damping increases to anticipate a high impact event and tracking is automatically traded-off for increased stability. In particular, we show a significant improvement in performance during jumping and trotting tasks with a simulated quadruped robot.

Author(s): Bilal Hammoud and Majid Khadiv and Ludovic Righetti
Book Title: Robotics and Automation Letters
Journal: IEEE Robotics and Automation Letters
Volume: Early Access
Number (issue): 999
Pages: 1-1
Year: 2021
Month: March
Publisher: IEEE

Department(s): Movement Generation and Control
Bibtex Type: Article (article)
Paper Type: Journal

Digital: True
DOI: 10.1109/LRA.2021.3068951
Event Name: ABC
Event Place: DEF
ISBN: 2377-3766
State: Published
URL: https://ieeexplore.ieee.org/abstract/document/9387077

BibTex

@article{hammoud2021impedance,
  title = {Impedance Optimization for Uncertain Contact Interactions Through Risk Sensitive Optimal Control},
  author = {Hammoud, Bilal and Khadiv, Majid and Righetti, Ludovic},
  journal = {IEEE Robotics and Automation Letters},
  booktitle = {Robotics and Automation Letters},
  volume = {Early Access},
  number = {999},
  pages = {1-1},
  publisher = {IEEE},
  month = mar,
  year = {2021},
  doi = {10.1109/LRA.2021.3068951},
  url = {https://ieeexplore.ieee.org/abstract/document/9387077},
  month_numeric = {3}
}