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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 -- • NSF -- 2
  3. 3. Draft Framework for Cyber-Physical Systems 3 This is all about systems engineering!
  4. 4. From «smart» to «intelligent» How CPS perform it Decision? Sensors Consoles Actuators Monitors 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»,, • 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. • 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!
  9. 9. Evolutionary robotics 9 Flexible Muscle-Based Locomotion for Bipedal Creatures Evolving Soft Robots with Multiple Materials (muscle, bone, etc.)
  10. 10. Breakthrough: deep learning 10 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 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
  15. 15. Cortical sensory homunculus • Body is an easy part. • Manipulation is difficult! • Non-prehensile manipulation is included. 15
  16. 16. Visuomotor examples • End-to-End Training of Deep Visuomotor Policies, • Supersizing Self-supervision: Learning to Grasp from 50K Tries and 700 Robot Hours,, 16
  17. 17. Self-learning robots • Fanuc: $7.5mln for 6% in Preferred Networks • ABB invested up to $10mln in Vicarious expert-in-eight-hours-these-robots-can-learn-for-themselves Osaro --, learning in environment, including cooperation with humans, to-teach-robots-new-tricks-in-little-time/ 17
  18. 18. Non-prehensile example • Deep Spatial Autoencoders for Visuomotor Learning, (video) 18
  19. 19. Learning to plan 19 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 ( win.html).
  21. 21. Visuomotor sensors • Computational multi-lens optics (near future) • Solid state LIDARs for driving (below $100 in five years, now $1000) --,, includes processor and neural software • But Elon Musk tell that lidar not needed, only optical cameras and radar 21
  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 (in Russian) TechInvestLab INCOSE Russian chapter