Robust trajectory optimization under frictional contact with iterative learning

Jingru Luo, Kris Hauser

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Optimization is often difficult to apply to robots due to the presence of modeling errors, which may cause constraints to be violated during execution on a real robot. This work presents a method to optimize trajectories with large modeling errors using a combination of robust optimization and parameter learning. In particular it considers the problem of computing a dynamically-feasible trajectory along a fixed path under frictional contact, where friction is uncertain and actuator effort is noisy. It introduces a robust time-scaling method that is able to accept confidence intervals on uncertain parameters, and uses a convex parameterization that allows dynamically-feasible motions under contact to be computed in seconds. This is combined with an iterative learning method that uses feedback from execution to learn confidence bounds on modeling parameters. Experiments on a manipulator performing a "waiter" task, on which an object is moved on a carried tray as quickly as possible, demonstrate this method can compensate for modeling uncertainties within a handful of iterations.

Original languageEnglish (US)
Title of host publicationRobotics
Subtitle of host publicationScience and Systems XI, RSS 2015
EditorsJonas Buchli, David Hsu, Lydia E. Kavraki
PublisherMIT Press Journals
ISBN (Electronic)9780992374716
StatePublished - 2015
Externally publishedYes
Event2015 Robotics: Science and Systems Conference, RSS 2015 - Rome, Italy
Duration: Jul 13 2015Jul 17 2015

Publication series

NameRobotics: Science and Systems
ISSN (Electronic)2330-765X


Other2015 Robotics: Science and Systems Conference, RSS 2015

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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