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Related Experiment Video

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An Open-Source Virtual Reality System for the Measurement of Spatial Learning in Head-Restrained Mice
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Data sparse inference of operator spatial reward models in uncertain environments.

Hunter M Ray1, Aditya Pandey1, Nisar Ahmed1

  • 1Cooperative Human-Robot Intelligence Laboratory, Department of Aerospace Engineering, University of Colorado Boulder, Boulder, CO, United States.

Frontiers in Robotics and AI
|April 13, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an intuitive interface for human-robot teams, improving autonomous drone operation in search and rescue. The system enables faster, more efficient mission planning with minimal user input.

Keywords:
autonomous aerial vehiclesautonomy systemsgraphical modelshuman–machine systemshuman–robot interactionrescue robots

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Area of Science:

  • Robotics
  • Human-Computer Interaction
  • Artificial Intelligence

Background:

  • Human-machine teaming enhances task accomplishment through autonomous systems.
  • Existing robotic interfaces require intuitive design for general users and efficient interpretation of multimodal input.

Purpose of the Study:

  • To present a multimodal interface for dynamic reprogramming of autonomous planning algorithms.
  • To focus on uncrewed aerial systems for outdoor search and rescue missions.

Main Methods:

  • Developed the Responsive Interface for iNtuitive Aircraft Operation (RINAO).
  • Leveraged geographic databases and user-defined features for geospatial interest inference.
  • Employed reward shaping to inform optimal planning algorithms.

Main Results:

  • Validated with 10 public safety experts, showing effective and efficient geospatial interest alignment.
  • Achieved above-average usability.
  • Outperformed an inverse reinforcement learning baseline in value alignment, speed, and reduced user input.

Conclusions:

  • Multimodal preference inference enables rapid and intuitive mission specification for human-robot teams.
  • The RINAO system is effective in time-critical environments like search and rescue.