Object Structure from Manipulation via Particle Filter and Robot-based Active Learning

TL;DR

This paper introduces a novel interactive object segmentation method using particle filters and active learning, enabling robots to incrementally and automatically learn detailed object structures for improved manipulation.

Contribution

It presents a new dynamic process for interactive segmentation that combines particle filtering and active learning, advancing robotic object modeling without extensive manual labeling.

Findings

More accurate object modeling achieved

Richer object structural information revealed

Demonstrated effectiveness on humanoid robot

Abstract

To learn object models for robotic manipulation, unsupervised methods cannot provide accurate object structural information and supervised methods require a large amount of manually labeled training samples, thus interactive object segmentation is developed to automate object modeling. In this article, we formulate a novel dynamic process for interactive object segmentation, and develop a solution based on particle filter and active learning so that a robot can manipulate and learn object structures incrementally and automatically. We demonstrate our method with a humanoidrobot on different types of objects, and compare its segmentation performancewith established methods on selected objects. The result shows that our approach allows more accurate object modeling and reveals richer object structural information.