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Human-in-the-Loop Optimization of Shared Autonomy in Assistive Robotics.

Deepak Gopinath1,2, Siddarth Jain3,2, Brenna D Argall1,3,2,4

  • 1Department of Mechanical Engineering, Northwestern University, Evanston, IL, 60208, USA.

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End-users can customize shared autonomy in assistive robotics through interactive optimization. This approach allows users to balance task performance with their preferred level of control, enhancing user-driven customization.

Keywords:
Human Factors and Human-in-the-LoopPhysically Assistive DevicesRehabilitation Robotics

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Area of Science:

  • Robotics
  • Human-Computer Interaction
  • Control Theory

Background:

  • Assistive robotics aims to enhance user capabilities.
  • Shared autonomy allows robots and users to collaborate on tasks.
  • Current methods often rely on predefined optimization, limiting user input.

Purpose of the Study:

  • To develop a mathematical framework for user-driven customization of shared autonomy in assistive robotics.
  • To enable end-users to directly influence the optimization of control sharing.
  • To explore the feasibility and outcomes of interactive optimization with end-users.

Main Methods:

  • Formalized user-driven customization as a nonlinear optimization problem.
  • Developed an interactive optimization procedure for control sharing with an assistive robotic arm.
  • Conducted a pilot study with 17 participants (4 with spinal cord injury) to evaluate the interactive optimization procedure.

Main Results:

  • All participants successfully converged to an assistance paradigm, indicating the existence of optimal solutions.
  • User preferences for control varied, with some prioritizing retaining control over maximizing task performance.
  • The study demonstrated that users can effectively drive the customization of robotic assistance.

Conclusions:

  • User-driven customization of shared autonomy is feasible and beneficial in assistive robotics.
  • Allowing end-users to perform optimization leads to personalized assistance.
  • Future research should focus on refining interactive optimization techniques and exploring diverse user needs.