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Related Experiment Video

Updated: Dec 21, 2025

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses
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Probabilistic Human Intent Recognition for Shared Autonomy in Assistive Robotics.

Siddarth Jain1, Brenna Argall1

  • 1Northwestern University, USA and Shirley Ryan AbilityLab, USA.

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|May 20, 2020
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Summary
This summary is machine-generated.

This study introduces a Bayesian filtering method for inferring human collaborator intent in shared autonomy systems. Personalized intent recognition improves human-robot collaboration, even with varying control interfaces.

Keywords:
Assistive RoboticsAssistive TeleoperationHuman Intent RecognitionHuman-Robot InteractionIntent InferenceProbabilistic ModelingShared Autonomy

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Area of Science:

  • Human-Robot Interaction
  • Artificial Intelligence
  • Robotics

Background:

  • Effective human-robot collaboration in shared autonomy hinges on accurately predicting human intentions.
  • Meaningful assistance requires the autonomy to infer the human collaborator's intended goal.
  • Existing methods often struggle with diverse control interfaces and personalized user behavior.

Purpose of the Study:

  • To develop a mathematical formulation for intent inference in assistive teleoperation under shared autonomy.
  • To probabilistically infer user goals by fusing non-verbal observations and modeling human behavior.
  • To personalize intent recognition through user-customized adjustable rationality.

Main Methods:

  • A recursive Bayesian filtering approach is employed to probabilistically reason about user goals.
  • Multiple non-verbal observations and contextual information are fused for intent recognition.
  • Human agent behavior is modeled as goal-directed actions with adjustable rationality, optimized per user.

Main Results:

  • The proposed approach demonstrates superior or comparable performance to existing methods in intent inference across various scenarios and tasks.
  • Human subject studies validate the effectiveness of probabilistic modeling and user-customized adjustable rationality.
  • Analysis reveals the impact of control interface limitations on intent inference accuracy.

Conclusions:

  • Probabilistic modeling and incorporating goal-directed human behavior with user-customized rationality significantly benefit intent inference.
  • The developed intent inference approach directly enhances shared autonomy performance.
  • Control interface characteristics are critical factors influencing the success of intent inference in assistive teleoperation.