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Updated: Nov 19, 2025

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A Hybrid Framework for Understanding and Predicting Human Reaching Motions.

Ozgur S Oguz1, Zhehua Zhou1, Dirk Wollherr1

  • 1Department of Electrical and Computer Engineering (EI), Technical University of Munich (TUM), Munich, Germany.

Frontiers in Robotics and AI
|January 27, 2021
PubMed
Summary
This summary is machine-generated.

Predicting human arm movements for robot collaboration requires understanding motor control. This study combines inverse optimal control with probabilistic movement primitives for efficient and accurate online prediction, accounting for individual differences.

Keywords:
human motion modelinghuman-in-the-loop controlhuman–robot collaborationinverse optimal controlprobabilistic movement primitivesreaching motion prediction

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Area of Science:

  • Robotics
  • Human-Robot Interaction
  • Motor Control
  • Biomechanics

Background:

  • Human-robot collaboration in confined spaces necessitates robots understanding and predicting human motion.
  • Model-based approaches are crucial for interpreting human motor control, which relies on biomechanical properties.
  • Reaching motions are often modeled as optimization problems, but the inverse problem of identifying optimality criteria is non-unique.

Purpose of the Study:

  • To implement an inverse optimal control (IOC) approach to identify cost functions governing human motion execution.
  • To develop an efficient and accurate method for online prediction of human reaching motions for human-robot interaction (HRI).
  • To create a framework for descriptive and generative modeling of human reaching motions.

Main Methods:

  • Implemented an inverse optimal control (IOC) approach to determine the combination of cost functions for motion execution.
  • Combined IOC with probabilistic movement primitives to create a hybrid model for prediction.
  • Evaluated the model for online capability, accuracy, and consideration of motor variability and interpersonal differences.

Main Results:

  • Human reaching motions are governed by a trade-off between kinematics and dynamics cost functions.
  • The initial IOC approach lacked sufficient computational efficiency for online HRI prediction.
  • The hybrid model combining IOC and probabilistic movement primitives achieved online-capable prediction with high efficiency and accuracy.

Conclusions:

  • The proposed hybrid framework provides a descriptive and generative model of human reaching motions.
  • This model can be effectively utilized online for human-in-the-loop robot control and task execution.
  • The framework accounts for motor variability and interpersonal differences, enhancing prediction robustness.