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Showing 1 to 4 of 4 for “"Push Recovery"”.

  1. Standing Balance of Legged Robots: Leveraging Reduced Order Models to Improve Balancing Performance

    … this proposed model to the existing models in push recovery simulations using two optimal control frameworks: trajectory optimization and a nonlinear model predictive control. We also examined the implementation of nonlinear model predictive controllers on a simulated one legged robot. With …

    queens Repository record for Standing Balance of Legged Robots: Leveraging Reduced Order Models to Improve Balancing Performance (opens in a new tab)

  2. Optimization for control and planning of multi-contact dynamic motion

    … of multiple simple models used for balancing and push recovery. Using the notions of barrier functions and occupation measures, we explicitly bound the set of disturbances from which a robot can recover by balancing or stepping. The primary contributions of this thesis are computational in nature, …

    mit Repository record for Optimization for control and planning of multi-contact dynamic motion (opens in a new tab)

  3. Dynamic Locomotion and Whole-Body Control for Compliant Humanoids

    … in simulation, and compliant locomotion and push recovery are demonstrated in hardware. We discuss practical considerations that led to a successful implementation on the THOR hardware platform and conclude with an application of the presented control framework for humanoid firefighting …

    vt Repository record for Dynamic Locomotion and Whole-Body Control for Compliant Humanoids (opens in a new tab)

  4. Multi-step recovery strategy for humanoid robots using model predictive control

    Made available in DSpace on 2019-08-23T20:36:13Z (GMT). No. of bitstreams: 2 MATIJEVICH-THESIS-2019.pdf: 1689446 bytes, checksum: c41c3a94a10006aa2ead403c11aebf8d (MD5) LICENSE.txt: 4213 bytes, checksum: 1306b3614c66f8a5b95ee47dcfc8aa21 (MD5) Previous issue date: 2019-04-26

    uiuc Repository record for Multi-step recovery strategy for humanoid robots using model predictive control (opens in a new tab)