Abstract

This paper explores the concept of interactive perception, in which sensing guides manipulation, in the context of extracting and classifying unknown objects within a cluttered environment. In the proposed approach, a pile of objects lies on a flat background, and the goal of the robot is to isolate, interact with, and classify each object so that its properties can be obtained. The algorithm considers each object to be classified using color, shape, and flexibility. The approach works with a variety of objects relevant to service robot applications, including both rigid objects such as bottles, cans, and pliers as well as non-rigid objects such as soft toy animals, socks, and shoes. Experiments on a number of different piles of objects demonstrate the ability of efficiently isolating and classifying each item through interaction.

Highlights

  • Humans routinely shuffle through papers on a desk or sift through objects in a drawer to more quickly and efficiently identify items of interest

  • Inspired by the above work, this paper introduces a new approach to interactive perception, in which successive www.intechopen.com

  • Work in interactive perception was performed by Christensen and Nørgaard [4], who developed an autonomous system to autonomously learn the physical properties of an unknown object by colliding with a mobile robot and tracking the resulting trajectories using an overhead camera

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Summary

Introduction

Humans routinely shuffle through papers on a desk or sift through objects in a drawer to more quickly and efficiently identify items of interest In such cases, it is our interaction with the environment that increases our understanding of the surroundings, in order to more effectively guide our actions to achieve the desired goal. Animals such as racoons and cats are known to use their front paws to poke and swat at objects to better understand them, whether it involves playing with a toy or trying to catch a rodent [28]. We show that deliberate actions can change the state of the world in a way that simplifies perception and future interactions

Previous work
Extracting and isolating objects
Graph‐based segmentation
Stereo matching
Determining grasp point
Classifying and labeling objects
Color histogram labeling
Skeletonization
Labeling revolute joints using motion
Platform
Extraction and isolation experiment
Classification of soft toy animals
Classification of metal and plastic recyclables
Sorting of socks and shoes
Conclusion
Full Text
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