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3D Visual Data-Driven Spatiotemporal Deformations for Non-Rigid Object Grasping Using Robot Hands.

Carlos M Mateo1, Pablo Gil2, Fernando Torres3

  • 1Computer Science Research Institute, University of Alicante, San Vicente del Raspeig, Alicante 03690, Spain. carlos.mateo@ua.es.

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Summary
This summary is machine-generated.

This study introduces a visual sensing system using RGBD data for intelligent robot grasping of non-rigid objects. It effectively monitors grasping by detecting surface deformations without needing object models or force data.

Keywords:
3D-object recognitionsensing for robot manipulationvision algorithms for graspingvisual perception

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Intelligent grasping tasks often face uncertainty, especially with non-rigid objects.
  • Traditional sensors like tactile and force sensors may not always provide sufficient data for complex manipulations.
  • A robust visual perception system is needed to support robot controllers in such scenarios.

Purpose of the Study:

  • To present a visual sensing system using range imaging for robot manipulation of non-rigid objects.
  • To develop a visual approach based on RGBD data to supervise robot-object interaction and detect surface deformations.
  • To enhance intelligent grasping by providing crucial feedback when other sensors fail.

Main Methods:

  • Implemented a novel visual approach utilizing RGBD data for robot manipulation.
  • Developed a deformation detection method to monitor object surface changes during grasping.
  • Integrated a pattern recognition process for robot hand pose independence.
  • Tested the system in real-time with a multi-fingered robot hand and various household objects.

Main Results:

  • The visual system successfully supervised grasping tasks with flexible household objects in real-time.
  • The method detected surface deformations, generating event messages for the robot controller.
  • Performance was validated without relying on object deformation models or material properties.
  • The approach demonstrated robustness, independent of robot hand pose.

Conclusions:

  • The proposed visual sensing system offers a reliable method for monitoring intelligent grasping of non-rigid objects.
  • RGBD data and deformation detection provide valuable feedback, especially when force/tactile data is unavailable.
  • This system enhances robot manipulation capabilities for complex and uncertain grasping scenarios.