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A.Levenchuk -- visuomotor learning in cyber-phisical systems

A.Levenchuk, "Cyber-physical systems architecture breakthrough: learning of visuomotor coordination", 107th meeting of INCOSE Russian chapter, 9-dec-2015

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A.Levenchuk -- visuomotor learning in cyber-phisical systems

  1. 1. Cyber-physical systems architecture breakthrough: learning of visuomotor coordination Moscow 9-dec- 2015 107th meeting of INCOSE Russian chapter
  2. 2. Cyber-physical systems • Cyber-Physical Systems or “smart” systems are co- engineered interacting networks of physical and computational components • CPS include: - Internet of Things (IoT) - Industrial Internet - Smart Cities - Smart Grid - "Smart" Anything (e.g., Cars, Buildings, Homes, Manufacturing, Hospitals, Appliances) • NIST CPS Public Working Group -- http://www.nist.gov/cps/index.cfm • NSF -- http://cps-vo.org/ 2
  3. 3. Draft Framework for Cyber-Physical Systems 3 http://www.nist.gov/el/nist-releases-draft-framework-cyber-physical-systems-developers.cfm This is all about systems engineering!
  4. 4. From «smart» to «intelligent» How CPS perform it Decision? Sensors Consoles Actuators Monitors http://www.nist.gov/el/nist-releases-draft-framework-cyber-physical-systems-developers.cfm 4
  5. 5. Where is that «intelligence»? Cyber-physical device Software Interfaces and communications Cognitive processing Hardware Sensors Mechanics Actuators 5
  6. 6. Knowledge engineering • Decision is carefully programmed (manually). • Example: robot-«butterfly», https://youtu.be/kyvW5sOcZHU, https://youtu.be/V30e77x8BQA • Every type of movement should be programmed anew • Non-adaptable to changes of environment and device • The best science available up today! • Perfect, if CPS perform only one or two movements. Not for robots, definitely! 6
  7. 7. Goal: CPS capuchin-like • Jurgen Schmithuber (July 2015): In order to pick a fruit at the top of a tree, Capuchin monkey plans a sequence of sub-goals (e.g., walk to the tree, climb the tree, grab the fruit, …) effortlessly. We will have machines with animal- level intelligence in 10 years. https://sites.google.com/site/deepernn/home/blog/briefsummaryofthepaneldiscussionatdlworkshopicml2015 • Needs planning • Needs great visuomotor coordination! • Impossible to program manually up to date. 7
  8. 8. New in «decisions»: learn to decide! Machine learning and reasoning: • Symbolic (by induction) • Evolutionary (by genetic programming) • Bayes (by probability assesement) • By analogy • Connectivist (deep learning, artificial neuron nets) 8 The Master Algorithm: combine ‘em all! http://www.amazon.com/dp/0465065708/
  9. 9. Evolutionary robotics 9 https://en.wikipedia.org/wiki/Evolutionary_robotics Flexible Muscle-Based Locomotion for Bipedal Creatures https://vimeo.com/79098420 Evolving Soft Robots with Multiple Materials (muscle, bone, etc.) https://youtu.be/z9ptOeByLA4
  10. 10. Breakthrough: deep learning 10 http://www.computervisionblog.com/2015/11/ the-deep-learning-gold-rush-of-2015.html?m=1
  11. 11. Reinforcement learning + deep learning 11 Not only «visuo» but «motor» too – with a coordination!
  12. 12. Not a rocket science • Open science • ArXiv, GitXiv • Open Source libraries • GPU in all computer stores • Conferences: ICML 2014 – 2500 participants, ICML 2015 – 4000 participants • Multiple schools (summer schools, university courses, hackathons) • Competitions (e.g. Kaggle) 12 http://deephack.me/10 teams
  13. 13. Visumotor policies/decisions/behavior/coordination 13 Visuo World reconstruct Motor Visuo Motor mediated perception behavior reflex And everything in between!
  14. 14. DeepDriving • train a deep Convolutional Neural Network (CNN) using 12 hours of human driving in a video game • show that our model can work well to drive a car in a very diverse set of virtual environments • train another CNN for car distance estimation on the KITTI dataset, results show that the direct perception approach can generalize well to real driving images • Open sourced • Autopilot Driving is not a miracle now: Tesla X, Google car, AVRORA/KAMAZ, Volvo trucks, and counting 14 http://deepdriving.cs.princeton.edu/
  15. 15. Cortical sensory homunculus • Body is an easy part. • Manipulation is difficult! • Non-prehensile manipulation is included. 15 https://en.wikipedia.org/wiki/Cortical_homunculus
  16. 16. Visuomotor examples • End-to-End Training of Deep Visuomotor Policies, http://arxiv.org/abs/1504.00702 • Supersizing Self-supervision: Learning to Grasp from 50K Tries and 700 Robot Hours, http://arxiv.org/abs/1509.06825, https://youtu.be/oSqHc0nLkm8 16
  17. 17. Self-learning robots • Fanuc: $7.5mln for 6% in Preferred Networks • ABB invested up to $10mln in Vicarious http://www.bloomberg.com/news/articles/2015-12-03/zero-to- expert-in-eight-hours-these-robots-can-learn-for-themselves Osaro -- http://www.osaro.com/, learning in environment, including cooperation with humans, http://www.technologyreview.com/news/543956/a-supercharged-system- to-teach-robots-new-tricks-in-little-time/ 17
  18. 18. Non-prehensile example • Deep Spatial Autoencoders for Visuomotor Learning, http://rll.berkeley.edu/dsae/ (video) 18
  19. 19. Learning to plan 19 https://drive.google.com/file/d/0B0PX5JnpNX8yT0JoODdHaklMLWs/view Neurocognitive Architecture for Autonomous Task Recognition, Learning and Execution (NARLE)
  20. 20. Visuomotor hardware 20 Quantum computer: waiting, but promising – currently 100mln times faster than classical desktop (http://googleresearch.blogspot.com/2015/12/when-can-quantum-annealing- win.html).
  21. 21. Visuomotor sensors • Computational multi-lens optics (near future) • Solid state LIDARs for driving (below $100 in five years, now $1000) -- http://www.quanergy.com/, http://velodynelidar.com/, includes processor and neural software • But Elon Musk tell that lidar not needed, only optical cameras and radar 21 http://blog.lidarnews.com/lidar-for-self-driving-cars/
  22. 22. Voice interface for visuomotor goal settings • Deep learning is a leading technology for a voice recognition • Voice command interface is not a problem today • General intelligence of a CPS is a problem! Company Name of Personal assistant Google Google Apple Siri Microsoft Cortana Facebook M Amazon Alexa [Cyber-physical system vendor] ??????????? 22
  23. 23. 23 Thank you! Anatoly Levenchuk TechInvestLab, president INCOSE Russian chapter, research director http://ailev.ru (in Russian) ailev@asmp.msk.su TechInvestLab INCOSE Russian chapter

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