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Modeling, learning, perception, and control methods for deformable object manipulation.

Hang Yin1, Anastasia Varava2, Danica Kragic2

  • 1Robotics, Perception, and Learning (RPL), School of Electrical Engineering and Computer Science, Royal Institute for Technology (KTH), Stockholm, Sweden. hyin@kth.se.

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Summary
This summary is machine-generated.

This study surveys over 100 papers on perceiving and handling deformable objects, unifying analytical and data-driven methods. It discusses challenges and future directions for robust robotic manipulation in everyday tasks.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Vision

Background:

  • Perceiving and handling deformable objects is crucial for human daily life.
  • Automating tasks like food handling and garment sorting faces challenges in modeling, perception, planning, and control.

Purpose of the Study:

  • To survey over 100 studies on deformable object manipulation.
  • To discuss open problems and theoretical developments needed for practical systems.
  • To unify analytical and data-driven approaches from a learning perspective.

Main Methods:

  • Comprehensive literature review of over 100 studies.
  • Analysis of simulation environments for benchmarking.
  • Adoption of a learning perspective to integrate diverse approaches.

Main Results:

  • Identified key challenges in modeling, perception, planning, and control for deformable objects.
  • Highlighted the potential of data-driven methods combined with classical approaches.
  • Emphasized the role of simulation in evaluating and developing robotic systems.

Conclusions:

  • Advances in data-driven methods and simulation are key to solving deformable object manipulation challenges.
  • Integrating model priors and task data is essential for robust perception and manipulation.
  • Further research is needed for flexible, scalable, and robust solutions in real-world applications.