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Combining Self-Organizing and Graph Neural Networks for Modeling Deformable Objects in Robotic Manipulation.

Angel J Valencia¹, Pierre Payeur¹

¹School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada.

Frontiers in Robotics and AI

|January 27, 2021

Summary

This study introduces a data-driven approach using graph-based machine learning to model deformable objects for robotic manipulation. This method enhances robotic dexterity by predicting object shape and dynamics, even with unknown properties.

Keywords:

deformable objects dynamic shape modeling manipulation robotics sensing shape

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Area of Science:

Robotics
Machine Learning
Computer Vision

Background:

Robotic manipulation requires accurate modeling of deformable objects for increased autonomy and dexterity.
Current analytical methods struggle with generalization in unstructured environments due to challenges in adapting to object shape and property variations.

Purpose of the Study:

To design and implement a data-driven approach for modeling deformable objects with undefined characteristics.
To enable robots to estimate and predict the state and transition dynamics of these objects for manipulation planning.

Main Methods:

Utilizing graph-based machine learning techniques to learn object models.
Training the model using Red, Green, Blue-Depth (RGB-D) sensor data.
Developing a system to estimate current object shape and predict future states.

Main Results:

The proposed data-driven approach successfully estimates the current state of deformable object shapes.
The model demonstrates the ability to predict future states, crucial for manipulation planning.
Evaluation confirms the model's effectiveness in handling objects with initially undefined properties.

Conclusions:

A novel data-driven, graph-based machine learning approach effectively models deformable objects for robotics.
This method improves robotic manipulation by enabling accurate state estimation and dynamic prediction.
The system supports advanced robotic hand manipulation planning in complex environments.