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Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy

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Learning Compliant Box-in-Box Insertion through Haptic-Based Robotic Teleoperation.

Sreekanth Kana¹, Juhi Gurnani¹, Vishal Ramanathan¹

¹School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore.

Sensors (Basel, Switzerland)

|November 14, 2023

Summary

This summary is machine-generated.

This study automates box-in-box insertion using Learning from Demonstration (LfD) and robotic teleoperation. The method leverages natural box compliance for precise, adaptable robotic packaging solutions.

Keywords:

Gaussian mixture regression Learning from Demonstration barycentric interpolation box-in-box insertion compliant insertion haptic feedback human–robot collaboration robotic automation teleoperation

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Area of Science:

Robotics and Automation
Logistics and Supply Chain Management
Machine Learning for Robotics

Background:

Automating compliant object insertion, like box-in-box tasks in logistics, is challenging due to difficulties in modeling object deformation.
Learning from Demonstration (LfD) offers a viable approach for complex robotic tasks that are hard to model mathematically.

Purpose of the Study:

To develop and validate an automated system for the box-in-box insertion task using Learning from Demonstration.
To address the challenges of modeling object deformation and enabling precise insertion with position-controlled robots.

Main Methods:

A master-slave teleoperated robot system was used for haptic demonstration of the insertion task.
Gaussian Mixture Regression was employed for probabilistic trajectory learning from demonstrations.
Barycentric interpolation was utilized for generalizing the insertion task and adapting to object position variations.

Main Results:

The proposed framework successfully demonstrated automated box-in-box insertion by utilizing the natural compliance of the boxes.
The method enabled precise insertion even with a position-controlled robot, showcasing adaptability.
Experimental validation confirmed the generalizability and repeatability of the developed strategy.

Conclusions:

Learning from Demonstration combined with probabilistic methods and interpolation provides an effective solution for complex robotic insertion tasks.
The approach successfully leverages object compliance, overcoming limitations in modeling deformation for automated packaging applications.
The validated method offers a robust and adaptable solution for real-world logistics and packaging automation.