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Determining Exception Context in Assembly Operations from Multimodal Data.

Mihael Simonič1,2, Matevž Majcen Hrovat1, Sašo Džeroski3,4

  • 1Department of Automatics, Biocybernetics and Robotics, Jožef Stefan Institute, Jamova Cesta 39, 1000 Ljubljana, Slovenia.

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This study introduces a new method for robots to understand assembly errors using image and force data. This helps robots recover from unexpected failures without human help, improving robotic task completion.

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autonomous assemblyautonomous exception handlingpeg-in-holepredictive clustering treessensor fusion

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Robot assembly tasks frequently fail due to unpredictable errors, necessitating manual intervention.
  • Existing frameworks require robust context determination for successful error resolution.

Purpose of the Study:

  • To develop a novel approach for context determination from multimodal data in robotic assembly.
  • To generate unified, low-dimensional context descriptions for exception strategy learning.

Main Methods:

  • Combined a neural network for image segmentation and contact point estimation with force-torque measurements.
  • Utilized an ensemble of decision trees to integrate features from image and force-torque data.
  • Collected datasets of induced insertion failures for peg-in-hole and car starter assembly tasks.

Main Results:

  • The proposed approach successfully generates reliable low-dimensional context descriptors.
  • These descriptors are suitable as queries for statistical learning frameworks.
  • Validated on both classic and industrially relevant assembly tasks.

Conclusions:

  • The novel context determination method enhances robotic systems' ability to handle assembly exceptions.
  • This approach is crucial for enabling autonomous error recovery in robots.
  • The generated descriptors facilitate effective statistical learning for robotic exception handling.