Deformable Object Manipulation in Caregiving Scenarios: A Review

Liman Wang, Jihong Zhu*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

Abstract

This paper reviews the robotic manipulation of deformable objects in caregiving scenarios. Deformable objects like clothing, food, and medical supplies are ubiquitous in care tasks, yet pose modeling, control, and sensing challenges. This paper categorises caregiving deformable objects and analyses their distinct properties influencing manipulation. Key sections examine progress in simulation, perception, planning, control, and system designs for deformable object manipulation, along with end-to-end deep learning’s potential. Hybrid analytical data-driven modeling shows promise. While laboratory successes have been achieved, real-world caregiving applications lag behind. Enhancing safety, speed, generalisation, and human compatibility is crucial for adoption. The review synthesises critical technologies, capabilities, and limitations, while also pointing to open challenges in deformable object manipulation for robotic caregiving. It provides a comprehensive reference for researchers tackling this socially valuable domain. In conclusion, multi-disciplinary innovations combining analytical and data-driven methods are needed to advance real-world robot performance and safety in deformable object manipulation for patient care.

Original languageEnglish
Article number1013
Number of pages27
JournalMachines
Volume11
Issue number11
DOIs
Publication statusPublished - 7 Nov 2023

Bibliographical note

Publisher Copyright:
© 2023 by the authors.

Keywords

  • assistive robots
  • deformable object manipulation
  • machine learning
  • robotic caregiving
  • simulation environments

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