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Estimating the Orientation of Objects from Tactile Sensing Data Using Machine Learning Methods and Visual Frames of

Vinicius Prado da Fonseca1, Thiago Eustaquio Alves de Oliveira2, Emil M Petriu3

  • 1School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada. vfons006@uottawa.ca.

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Summary
This summary is machine-generated.

Robots can now estimate object orientation during manipulation using tactile and visual data, inspired by human senses. This approach enhances robotic grasping stability and tracking capabilities for unknown objects.

Keywords:
fuzzy controlin-hand manipulationmachine learningpose estimationunderactuated robotic hands

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Area of Science:

  • Robotics
  • Machine Learning
  • Sensor Fusion

Background:

  • Underactuated robotic hands offer adaptive grasping for unknown objects.
  • Maintaining stable grasp and tracking object state requires integrating tactile and visual sensing.
  • Human somatosensation integrates "What and Where" information for skilled manipulation.

Purpose of the Study:

  • To propose a novel approach for estimating in-hand object pose using tactile data and visual frames of reference.
  • To develop a system mimicking the human "What and Where" subsystem for robotic manipulation.
  • To enhance robotic manipulation by learning object-tactile sensing data relationships.

Main Methods:

  • Utilized machine learning to estimate object orientation from tactile sensor data on underactuated fingers.
  • Integrated tactile sensing (local object information) with a vision system (egocentric/allocentric frames).
  • Developed a dual fuzzy logic controller for autonomous stable grasping under external forces and object rotations.

Main Results:

  • Ridge regressor achieved 0.077° mean squared error in estimating object orientation under external forces.
  • Multilayer perceptron (MLP) neural network achieved 0.067° mean squared error during open-loop object rotations.
  • The fuzzy controller successfully maintained stable grasps for data collection under various conditions.

Conclusions:

  • The proposed system effectively estimates in-hand object pose by combining tactile and visual data.
  • Machine learning models, particularly ridge regressor and MLP, show high accuracy in orientation estimation.
  • The approach offers a pathway towards more sophisticated robotic in-hand manipulation capabilities.