Teaching icub to recognize. objects. Giulia Pasquale. PhD student
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1 Teaching icub to recognize RobotCub Consortium. All rights reservted. This content is excluded from our Creative Commons license. For more information, see objects Giulia Pasquale PhD student IIT, icub Facility University of Genoa, DIBRIS Laboratory for Computational and Statistical Learning 1
2 Picture of Lorenzo Natale removed due to copyright restrictions. Please see the video. Picture of Lorenzo Rosasco removed due to copyright restrictions. Please see the video. Supervisors and collaborators Picture of Carlo Ciliberto removed due to copyright restrictions. Please see the video. Picture of Francesca Odone removed due to copyright restrictions. Please see the video. 2
3 Deep Learning Breakthrough in Computer Vision DEEP DEEP NETWORKS BIG DATASETS Credits: A. Vedaldi Oxford Visual Geometry Group. All rights reserved. This content is excluded from our Creative Commons license. For more information, see Credits: Fei-Fei Li Source Unknown. All rights reserved. This content is excluded from our Creative Commons license. For more information, see
4 Deep Learning Breakthrough in Computer Vision Figure removed due to copyright restrictions. Please see the video. Source: Figures 9, 11 & 12 from Russakovsky, Olga, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang et al. "Imagenet large scale visual recognition challenge." International Journal of Computer Vision 115, no. 3 (2015):
5 Deep Learning Breakthrough in Computer Vision IMAGENET PRE-TRAINING Krizhevsky et al (2012) N custom Courtesy of Neural Information Processing Systems. Used with permission. Source: Krizhevsky, Alex, IlyaSutskever, and Geoffrey E. Hinton. "Imagene classification with deep convolutional neural networks." In Advances in neural information processing systems, pp Andrea Vedaldi. All rights reserved. This content is excluded from our Creative Commons license. For more information, see Credits: A. Vedaldi 5
6 Meanwhile, in Robotics Image of a baby removed due to copyright restrictions. Please see the video. 6
7 Meanwhile, in Robotics RobotCub Consortium. All rights reserved. This content is excluded from our Creative Commons license. For more information, see spectrum.ieee.org DARPA. All rights reserved. This content is excluded from our Creative Commons license. For more information, see DARPA. All rights reserved. This content is excluded AUVSI. All rights reserved. This content is excluded from from our Creative Commons icense. For more information, our Creative Commons license. For more information, see see 7
8 Meanwhile, in Robotics TELE-OPERATION Image removed due to copyright restrictions. Please see the video. Image removed due to copyright restrictions. Please see the video. 3D MAPPING & STRONG SUPERVISION Courtesy of Shuran Song, Linguang Zhang and Jianxiong Xiao. License CC BY. Song et al (2015), arxiv:
9 Setting: Interactive Object Learning Verbal instructions of a teacher Robot s attention (motion, color-based segmentation) Where is the soap? RobotCub Consortium. All rights reserved. This content is excluded from our Creative Commons license. For more information, see 9
10 Setting: On the fly Recognition Verbal instructions of a teacher Robot s attention (motion) This is a sprayer! Courtesy of Giulia Pasquale, Carlo Ciliberto, Francesca Odone, Lorenzo Rosasco and Lorenzo Natale. Used with permission. 10
![Deep Convolutional Network code Representation Extraction Linear Classifier Krizhevsky network [ Caffe BVLC Reference CaffeNet ] Courtesy of Neural Information Processing Systems.](/docs-images/90/102220556/images/11-3.jpg "Used with permission. Source: Krizhevsky, Alex, IlyaSutskever, and Geoffrey E. Hinton.\"Imagene classification with deep convolutional neural networks.")
11 Applications: Interactive Object Learn ing & On the fly Recognition This is a cup! Motion, Color & Luminance Verbal Supervision Segmentation RobotCub Consortium. All rights reserved. This content is excluded from our Creative Commons license. For more information, see Deep Convolutional Network code Representation Extraction Linear Classifier Krizhevsky network [ Caffe BVLC Reference CaffeNet ] Courtesy of Neural Information Processing Systems. Used with permission. Source: Krizhevsky, Alex, IlyaSutskever, and Geoffrey E. Hinton."Imagene classification with deep convolutional neural networks." In Advances in neural information processing systems, pp can cup wallet soap cup car scores Linear Classifier RLS [ GURLS ] 11
12 Applications: Interactive Object Learn ing & On the fly Recognition Verbal Supervision Segmentation This is a cup! Motion, Color & Luminance Courtesy of Giulia Pasquale, Carlo Ciliberto, Francesca Odone, Lorenzo Rosasco, Lorenzo Natale. Used with permission. Source: Pasquale, Giulia, Carlo Ciliberto, Francesca Odone, Lorenzo Rosasco, Lorenzo Natale."Teaching icubto recognize objects using deep Convolutional Neural Networks." In MLIS@ICML, pp Deep Convolutional Network code Representation Extraction Linear Classifier Krizhevsky network [ Caffe BVLC Reference CaffeNet ] Courtesy of Neural Information Processing Systems. Used with permission. Source: Krizhevsky, Alex, IlyaSutskever, and Geoffrey E. Hinton. "Imagene classification with deep convolutional neural networks." In Advances in neural information processing systems, pp Linear Classifier cup scores can wallet soap cup car RLS [ GURLS ] 12
13 An ideal robotic visual recognition system Self-supervised Reliable Teaching through time day 1 day 2 day 3 day X What is this? mug mug mug mug mug Exploits contextual information Learns incrementally detergent detergent detergent detergent detergent sponge sponge sponge sponge sponge Source Unknown. All rights reserved. This content is excluded from ourcreative Commons license. For more information, see 13
14 Application: On the fly Recognition This is a cup! Motion, Color & Luminance? Self-supervised? Reliable Courtesy of Giulia Pasquale, Carlo Ciliberto, Francesca Odone, Lorenzo Rosasco, Lorenzo Natale. Used with permission. Source: Pasquale, Giulia, Carlo Ciliberto, Francesca Odone, Lorenzo Rosasco, Lorenzo Natale. "Teaching icubto recognize objects using deep Convolutional Neural Networks." In MLIS@ICML, pp Deep Convolutional Network code? Exploits contextual information? Learns incrementally cup Krizhevsky network [ Caffe BVLC Reference CaffeNet ] Giulia Pasquale, Carlo Ciliberto, Francesca Odone, Lorenzo Rosasco and Lorenzo Natale. All rights reserved. This content is excluded from our Creative Commons license. For more information, see scores Linear Classifier can wallet soap cup car RLS [ GURLS ] 14
15 icubworld28 Dataset Overview 2014: Household 7 categories 4 objects/category 28 objects 4 acquisitions laundry detergent plate dishwashing detergent sponge cup soap sprayer Courtesy of Giulia Pasquale, Carlo Ciliberto, Francesca Odone, Lorenzo Rosasco and Lorenzo Natale. Used with permission. Benchmarking deep Conv Nets for Real-world Object Recognition: How many Objects can icub Learn? arxiv: , 15
16 icubworld28 Dataset Examples of Acquired Videos 2014: Household day1 day2 day3 day4 TRAIN TEST Giulia Pasquale, Carlo Ciliberto, Francesca Odone, Lorenzo Rosasco and Lorenzo Natale. All rights reserved. This content is excluded from our Creative Commons license. For more information, see Benchmarking deep Conv Nets for Real-world Object Recognition: How many Objects can icub Learn? arxiv: , 16
17 icubworld28 Dataset Object Identification Data Sheet? Self-supervised? Reliable? Exploits contextual information? Learns incrementally Courtesy of Giulia Pasquale, Carlo Ciliberto, Francesca Odone, Lorenzo Rosasco and Lorenzo Natale. Used with permission. Benchmarking deep Conv Nets for Real-world Object Recognition: How many Objects can icub Learn? arxiv: , 17
18 icubworld28 Dataset Clutter and Scale? Self-supervised? Reliable Image Crop 1 Crop 2 Manual? Exploits contextual information? Learns incrementally TRAIN TEST Accuracy (%) Image Crop1 Crop2 Manual Image Crop Crop Manual Courtesy of Giulia Pasquale, Carlo Ciliberto, Francesca Odone, Lorenzo Rosasco and Lorenzo Natale. Used with permission. Benchmarking deep Conv Nets for Real-world Object Recognition: How many Objects can icub Learn? arxiv: , 18
19 icubworld28 Dataset Temporal Contextual Information? Self-supervised? Reliable? Exploits contextual information? Learns incrementally Courtesy of Giulia Pasquale, Carlo Ciliberto, Francesca Odone, Lorenzo Rosasco and Lorenzo Natale. Used with permission. Benchmarking deep Conv Nets for Real-world Object Recognition: How many Objects can icub Learn? arxiv: , 19
20 icubworld Datasets Ongoing Work 2015: Kitchen + Food + Toys + Bathroom + Daily use + Office + Tools oven glove squeezer cup bottle box can beach: shovel bucket toy rake body cream hair brush soap sunglasses hair clip watch mouse organizer calculator paint brush scissors scotch 1. Object Categorization Datas et 2. Continuously Expandable in Time 21 categories 10 objects/category 200 objects 3. Tagged by Nuisance Factors 5 acquisitions divided by nuisance: scale 2D rotation 3D rotation translation mixed 4. Depth information available (left+right cameras) 20
21 icubworld Datasets Disparity-driven segmentation Courtesy of Giulia Pasquale, Tanis Mar, Carlo Ciliberto, Lorenzo Rosasco, and Lorenzo Natale. Used with permission. Enabling Depth-driven Visual Attention on the icub robot: Instructions for Use and New Perspectives submitted to Humanoids
22 icubworld Datasets Ongoing Work 2015: Kitchen + Food + Toys + Bathroom + Daily use + Office + Tools oven glove squeezer cup bottle box can shovel bucket toy rake body cream hair brush soap sunglasses hair clip watch mouse organizer calculator paint brush scissors scotch 22
23 icubworld Datasets Ongoing Work 2015: Kitchen + Food + Toys + Bathroom + Daily use + Office + Tools translation scale mixed 2D rotation 3D rotation Application & Data are available for projects 5.2 & 5.3!! 23
24 24
25 MIT OpenCourseWare Resource: Brains, Minds and Machines Summer Course Tomaso Poggio and Gabriel Kreiman The following may not correspond to a particular course on MIT OpenCourseWare, but has been provided by the author as an individual learning resource. For information about citing these materials or our Terms of Use, visit:
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