Learning to Catch Piglets in Flight
Ozan \c{C}atal, Lawrence De Mol, Tim Verbelen, Bart Dhoedt

TL;DR
This paper introduces a robotic system that combines RGB-D and radar sensors with deep learning to successfully catch flying objects, demonstrated on a stuffed Piglet with high accuracy.
Contribution
The paper presents a novel multi-sensor, deep learning-based control system for catching flying objects, outperforming traditional methods in accuracy.
Findings
Deep learning approach achieves 80% success rate in catching Piglet.
Multi-sensor fusion improves detection and interception accuracy.
Deep learning outperforms color filtering and ballistic regression methods.
Abstract
Catching objects in-flight is an outstanding challenge in robotics. In this paper, we present a closed-loop control system fusing data from two sensor modalities: an RGB-D camera and a radar. To develop and test our method, we start with an easy to identify object: a stuffed Piglet. We implement and compare two approaches to detect and track the object, and to predict the interception point. A baseline model uses colour filtering for locating the thrown object in the environment, while the interception point is predicted using a least squares regression over the physical ballistic trajectory equations. A deep learning based method uses artificial neural networks for both object detection and interception point prediction. We show that we are able to successfully catch Piglet in 80% of the cases with our deep learning approach.
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Taxonomy
TopicsAnomaly Detection Techniques and Applications · Video Surveillance and Tracking Methods · Advanced Optical Sensing Technologies
MethodsTest
