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World Representation Through Artificial Neural Networks: An Introduction - Luis Contreras 2020.06.16 | RoboCup@Home Education

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RoboCup@Home Education
Online Classroom: Invited Lecture Series

= World Representation Through Artificial Neural Networks: An Introduction =
Speaker: Luis Contreras | Tamagawa University
Date and Time:
- June 16, 2020 (Tue) 09:00~11:00 (GMT+8 China/Malaysia)
- June 15, 2020 (Mon) 21:00~23:00 (EDT New York)
- June 16, 2020 (Tue) 03:00~05:00 (CEST Italy/France)

https://www.robocupathomeedu.org/learn/online-classroom/invited-lecture-series

Published in: Engineering
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World Representation Through Artificial Neural Networks: An Introduction - Luis Contreras 2020.06.16 | RoboCup@Home Education

  1. 1. RoboCup@Home Education ONLINE CLASSROOM Invited Lecture Series Luis Contreras received his Ph.D. in Computer Science at the Visual Information Laboratory, in the Department of Computer Vision, University of Bristol, UK. Currently, he is a research fellow at the Advanced Intelligence & Robotics Research Center, Tamagawa University, Japan. He has also been an active member of the Bio-robotics Laboratory at the Faculty of Engineering, National Autonomous University of Mexico, Mexico. He has been working on service robots and has tested his latest results at the RoboCup and similar robot competitions for the last ten years. World Representation Through Artificial Neural Networks Speaker: Luis Contreras | Tamagawa University Time: June 16, 2020 (Tue) 09:00~11:00 (GMT+8) https://www.robocupathomeedu.org/learn/online-classroom/invited-lecture-series Highlights ● Artificial Neural Networks and its application to Object Recognition ● Convolutional Neural Networks
  2. 2. RoboCup@Home Education | www.RoboCupatHomeEDU.org Robot Localisation: An Introduction ● Speaker: Luis Contreras | Tamagawa University ● Host: Jeffrey Tan | @HomeEDU ● Date and Time: ○ June 16, 2020 (Tue) 09:00~11:00 (GMT+8 China/Malaysia) ○ June 15, 2020 (Mon) 21:00~23:00 (EDT New York) ○ June 15, 2020 (Mon) 03:00~05:00 (CEST Italy/France) ○ Web: https://www.robocupathomeedu.org/learn/online-classroom/invited-lecture-series ** Privacy reminder: Video will be recorded and published online ** RoboCup@Home Education Online Classroom 2
  3. 3. RoboCup@Home Education | www.RoboCupatHomeEDU.org RoboCup@Home Education is an educational initiative in RoboCup@Home that promotes educational efforts to boost RoboCup@Home participation and artificial intelligence (AI)-focused service robot development. Under this initiative, currently there are 4 efforts in active operation: 1. RoboCup@Home Education Challenge events (national, regional, international) 2. Open Source Educational Robot Platforms for RoboCup@Home (service robotics) 3. OpenCourseWare for the learning of AI-focused service robot development 4. Outreach Programs (local workshops, international academic exchanges, etc.) Web: https://www.robocupathomeedu.org/ FB: https://www.facebook.com/robocupathomeedu/ RoboCup@Home Education 3
  4. 4. RoboCup@Home Education | www.RoboCupatHomeEDU.org Special Online Challenge Tracks ● Open Platform Online Classroom [EN] ● Open Platform Online Classroom [CN] ● Standard Platform Pepper 2.9 Online Classroom [EN] ● Standard Platform Pepper 2.5 Online Classroom [CN] More details: https://www.robocupathomeedu.org/learn/online -classroom Invited Lecture Series ● Robotics Development with MATLAB [EN] ● Robot Localisation: An Introduction [EN] ● World Representation Through Artificial Neural Networks: An Introduction [EN] ● Introduction to NLP [EN] ● ROS with AI [TH] Regular Online Classroom Tracks ● Introduction to Service Robotics [EN] ○ 6 weeks ○ ROS, Python ○ Speech, Vision, Navigation, Arm RoboCup@Home Education Online Classroom 4
  5. 5. RoboCup@Home Education | www.RoboCupatHomeEDU.org Luis Contreras | Tamagawa University 5 Luis Contreras received his Ph.D. in Computer Science at the Visual Information Laboratory, in the Department of Computer Vision, University of Bristol, UK. Currently, he is a research fellow at the Advanced Intelligence & Robotics Research Center, Tamagawa University, Japan. He has also been an active member of the Bio-robotics Laboratory at the Faculty of Engineering, National Autonomous University of Mexico, Mexico. He has been working on service robots and has tested his latest results at the RoboCup and similar robot competitions for the last ten years.
  6. 6. tamagawa.jp World representation through Artificial Neural Networks: An introduction Luis Angel Contreras-Toledo, PhD Advance Intelligence and Robotics Research Center Tamagawa University https://aibot.jp/ 2020
  7. 7. tamagawa.jp Data collection: variable selection and measurement x – Humidity y – Rain
  8. 8. tamagawa.jp Data collection: variable selection and measurement x – Humidity y – Rain
  9. 9. tamagawa.jp Data collection: variable selection and measurement x – Humidity y – Rain
  10. 10. tamagawa.jp Data collection: variable selection and measurement x – Humidity y – Rain
  11. 11. tamagawa.jp Regression model 𝑦 = 𝑎0 x – Humidity y – Rain
  12. 12. tamagawa.jp Regression model 𝑦 = 𝑎0 + ∆0 x – Humidity y – Rain
  13. 13. tamagawa.jp x – Humidity y – Rain Regression model 𝑦 = 𝑎0 + ∆0
  14. 14. tamagawa.jp x – Humidity y – Rain Regression model 𝑦 = 𝑎0 + ∆0
  15. 15. tamagawa.jp x – Humidity y – Rain Regression model 𝑦 = 𝑎0 + 𝑎1 𝑥
  16. 16. tamagawa.jp x – Humidity y – Rain Regression model 𝑦 = (𝑎0+∆0) + (𝑎1+∆1)𝑥
  17. 17. tamagawa.jp x – Humidity y – Rain Regression model 𝑦 = (𝑎0+∆0) + (𝑎1+∆1)𝑥
  18. 18. tamagawa.jp x – Humidity y – Rain Regression model 𝑦 = (𝑎0+∆0) + (𝑎1+∆1)𝑥
  19. 19. tamagawa.jp Regression model 𝑦 = 𝑎0 + 𝑎1 𝑥 + 𝑎2 𝑥2 + ⋯ + 𝑎9 𝑥9 x – Humidity y – Rain
  20. 20. tamagawa.jp Regression model 𝑦 = 𝑎0 𝑦 = 𝑎0 + 𝑎1 𝑥 𝑦 = 𝑎0 + ⋯ + 𝑎9 𝑥9 Which model is better? How do you generate such model?
  21. 21. tamagawa.jp Content • Artificial Neural Networks • Convolutional Neural Networks
  22. 22. tamagawa.jp Artificial Neural Networks Single neuron model (sigmoid function)
  23. 23. tamagawa.jp Artificial Neural Networks Multiple neuron model and two neurons
  24. 24. tamagawa.jp Artificial Neural Networks Multiple neuron model and four neurons
  25. 25. tamagawa.jp Artificial Neural Networks Multiple neuron model and eight neurons
  26. 26. tamagawa.jp Artificial Neural Networks Multivariable regression model x1 – Humidity x2 – Temperature y – Rain
  27. 27. tamagawa.jp Artificial Neural Networks Multiple neuron model with two input variables
  28. 28. tamagawa.jp Artificial Neural Networks Neural network representation
  29. 29. tamagawa.jp Artificial Neural Networks Neural network representation
  30. 30. tamagawa.jp Artificial Neural Networks Case study: Handwritten digit recognition problem 5 0 4 1 9 2
  31. 31. tamagawa.jp Artificial Neural Networks The MNIST database of handwritten digits
  32. 32. tamagawa.jp Artificial Neural Networks Multivariable and multivariate problem
  33. 33. tamagawa.jp Artificial Neural Networks Multivariable and multivariate problem 28 28 0 9 1 2 3 4 5 6 7 8
  34. 34. tamagawa.jp Artificial Neural Networks Multivariable and multivariate problem
  35. 35. tamagawa.jp Artificial Neural Networks Multiple layer artificial neural networks allow different designs
  36. 36. tamagawa.jp Artificial Neural Networks Multiple layer artificial neural networks allow different designs
  37. 37. tamagawa.jp Content • Artificial Neural Networks • Convolutional Neural Networks
  38. 38. tamagawa.jp Convolutional Neural Networks In the standard neural networks, the spatial properties are lost
  39. 39. tamagawa.jp Convolutional Neural Networks It helps to think of the inputs as two dimensional arrays
  40. 40. tamagawa.jp Convolutional Neural Networks Local receptive fields (or filters) can be extracted
  41. 41. tamagawa.jp Convolutional Neural Networks We apply the local receptive field (or filter) to the whole image
  42. 42. tamagawa.jp Convolutional Neural Networks We apply the local receptive field (or filter) to the whole image
  43. 43. tamagawa.jp Convolutional Neural Networks This filter is learning some spatial relationships in small regions
  44. 44. tamagawa.jp Convolutional Neural Networks We can have as many filters as we need
  45. 45. tamagawa.jp Convolutional Neural Networks Each filter will learn different properties
  46. 46. tamagawa.jp Convolutional Neural Networks Standard CNN architecture
  47. 47. tamagawa.jp Convolutional Neural Networks Standard CNN architecture
  48. 48. tamagawa.jp Convolutional Neural Networks Standard CNN architecture
  49. 49. tamagawa.jp Convolutional Neural Networks An insight on what is happening inside
  50. 50. tamagawa.jp Convolutional Neural Networks An insight on what is happening inside
  51. 51. tamagawa.jp Convolutional Neural Networks An insight on what is happening inside
  52. 52. tamagawa.jp Convolutional Neural Networks A more complex CNN architecture
  53. 53. tamagawa.jp Convolutional Neural Networks Some applications: OpenPose https://github.com/CMU-Perceptual-Computing-Lab/openpose
  54. 54. tamagawa.jp Convolutional Neural Networks Some applications: PoseNet http://mi.eng.cam.ac.uk/projects/relocalisation/
  55. 55. tamagawa.jp Convolutional Neural Networks Some applications: PoseNet http://mi.eng.cam.ac.uk/projects/relocalisation/
  56. 56. tamagawa.jp Convolutional Neural Networks Some applications: TensorFlow.js https://js.tensorflow.org/
  57. 57. tamagawa.jp Convolutional Neural Networks Some applications: TensorFlow.js https://js.tensorflow.org/
  58. 58. tamagawa.jp Convolutional Neural Networks Some applications: TensorFlow.js https://js.tensorflow.org/
  59. 59. tamagawa.jp Convolutional Neural Networks RGBD Datasets: Past, Present and Future http://www.michaelfirman.co.uk/RGBDdatasets/
  60. 60. tamagawa.jp World representation through Artificial Neural Networks: An introduction Luis Angel Contreras-Toledo, PhD Advance Intelligence and Robotics Research Center Tamagawa University https://aibot.jp/ 2020
  61. 61. Web: https://www.robocupathomeedu.org/ FB: https://www.facebook.com/robocupathomeedu/ GitHub: https://github.com/robocupathomeedu/ Online Classroom: https://www.robocupathomeedu.org/learn/online-classroom Contact: oc@robocupathomeedu.org RoboCup@Home Education ONLINE CLASSROOM Invited Lecture Series
  62. 62. RoboCup@Home Education ONLINE CLASSROOM Invited Lecture Series Introduction to Natural Language Processing Speaker: Sebastian Castro | MIT CSAIL Time: July 08, 2020 (Wed) 19:00~21:00 (GMT+8) https://www.robocupathomeedu.org/learn/online-classroom/invited-lecture-series Sebastian Castro is a robotics software engineer at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), working on home service robotics research with the Toyota Human Support Robot (HSR). He was previously the primary technical contact for RoboCup and robotics education at MathWorks. Sebastian holds a B.S. and M.S. in Mechanical Engineering from Cornell University, with a focus on dynamics, systems, and controls for robotics applications. Highlights ● A quick introduction to the state of Natural Language Processing (NLP) ● Demonstrations in Python

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