Task-centric optimization of configurations for assistive robots

  title={Task-centric optimization of configurations for assistive robots},
  author={Ariel Kapusta and Charles C. Kemp},
  journal={Autonomous Robots},
Robots can provide assistance to a human by moving objects to locations around the person’s body. With a well-chosen initial configuration, a robot can better reach locations important to an assistive task despite model error, pose uncertainty, and other sources of variation. However, finding effective configurations can be challenging due to complex geometry, a large number of degrees of freedom, task complexity, and other factors. We present task-centric optimization of robot configurations… 

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TOC builds upon previous work, Taskcentric initial Configuration Selection (TCS), addressing some of the limitations of TCS, and notable alterations are selecting configurations from the continuous configuration space using a Covariance Matrix Adaptation Evolution strategy, introducing a joint-limit-weighted manipulability term, and changing the framework to move all optimization offline and using function approximation at run-time.

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  • J. DongJ. Trinkle
  • Computer Science
    2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • 2015
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