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Aquila: An Open-Source GPU-Accelerated Toolkit for
Cognitive Robotics Research
Martin Peniak and Anthony Morse
Agenda

 What is Cognitive Robotics?

 The iCub humanoid robot

 GPUs and robotics

 Aquila - past, present and future




                                     2
Cognitive Robotics


                     3
Cognitive Robotics

 Cognitive Robotics draws from classical robotics, artificial intelligence, cognitive science and
 neurobiology to elucidate and synthesize aspects of action-oriented intelligence.


 Linguistics                                                                 Computer Science




 Psychology                                                                     AI         Neuroscience




                                                                                                 4
Cognitive Robotics

                     Substractions


                              Memory


                 World Puzzles


                             ...
 ...

                                       5
Ungrounded Symbol Systems

      Computers can easily be pre-programmed to memorise a
      dictionary, but cannot fully understand the language they use


                                                      STRENGTH: power to resist force




                                FORCE: strength or power exerted upon an object



   PUSH: to apply force to (something) in order to move it                   POWER: applied force producing motion


                                                                                                                         6
Example of self-referential, amodal network of word definitions in Webster’s Dictionary (Roy 2005) Chinese Room (Searle 1980)
Cognitive Robotics

 An objects identity or category, it’s meaning lies in the profile of
 dynamic interactions that it affords




                                                                        7
Cognitive Robotics

 Replicating child psychology
 experiments

 Testing psychological theory

 Finding common processes underlying
 disparate phenomenon

 Prompting new questions and
 investigations

 Developing technology


                                       8
iCub humanoid robot
                      The dimensions are similar to that of a 3.5 year old
                      child

                      53 degrees of freedom

                      Came from the European Framework 6 project:
                      RobotCub (www.robotcub.org)

                      There are now 20 iCubs in different labs in Europe
                      and 1 in the US

                      Continued design - v2.0 to come out soon

                      Various ongoing project outcomes are distributed
                      via an open-source software repository and via
                      hardware upgrades

                      A free iCub simulator is available




                                                                  9
iCub humanoid robot
Touch sensitive skin and compliant actuation




                                               10
iCub humanoid robot
Dexterous hands for object manipulation




                                          11
iCub humanoid robot
Almost walking




                      12
iCub humanoid robot
Simulator

  Open-source

  Developed as part of a joint effort
  with the European project iTalk

  Widely adopted within cognitive
  robotics community




                                        V. Tikhanoff, P. Fitzpatrick, F. Nori, L. Natale, G. Metta, and A. Cangelosi, “The icub humanoid robotsimulator,”
                                        In International Conference on Intel ligentRObots and Systems IROS, Nice, France, 2008                              13
GPUs in robotics
It’s all about real time

   Motion compliance < 1 ms
   Vision (30fps) < 33 ms
   Vision (60fps) < 16 ms

We typically take 33 ms as the cut-off time. 1 complete cycle of
everything critical MUST be completed in that time.

Of course some processes are not critical and their information
can be used as and when it becomes available, subject to
various constraints.




                                                                   14
GPUs in robotics
It’s all about real time

   Our models of learning, integrating multi-modal
   information, and controlling the robot are based on
   Neural Networks

   Each neuron within a neural network computes its own
   activation based on local information

   Learning algorithms continuously adapt the strength of
   connections between neurons

   Both processes are inherently parallel


                                                            15
GPUs in robotics
It’s all about real time

   CUDA lets us build much bigger and more complex
   neural networks while remaining within the real time
   constraints

   CUDA also accelerates some of the pre-processing
   required (e.g. vision processing)




                                                          16
GPUs in robotics
Vision processing example

  Carlo Ciliberto and Vadim Tikhanoff used GPU-accelerated SIFT-based feature
  extraction for iCub object recognition

  “In Robotics it is crucial to have systems that perform in real time since the artificial agent has to continuously adapt to
  (possibly sudden) changes in the environment. Clearly the CPU implementation of the SIFT detection/extraction was
  unsatisfactory in this regard while the GPU one allowed us to overcome this obstacle.”

  “We did not perform a rigorous comparison between CPU and GPU implementations since the difference of computational
  times was so huge we did not bother. In particular we experienced times greater than 1000 ms for the OpenCV Sift
  detector/extractor on CPU while around 20 ms for the GPU implementation.”




                                                                                                                      17
Aquila
An Open-Source GPU-Accelerated Toolkit for Cognitive Robotics Research



                                                                         18
Aquila
History

  Project started in 2009 as a simple open-source GUI application for our research with iCub

  CUDA was adopted to accelerate growing number of interacting systems

  Several limitations have recently led to a completely decoupled redesign




                                                                                               19
Aquila
Research with iCub made easier and more accessible
“There are many advantages using Aquila ....I am mainly using Aquila for eye tracking and the feature of
video streaming using the screen in the iCub world. The biggest advantage is the ability to record the
eyes and control what is displayed on the screen at the same time.”

“My future goal would be to add a new eye tracking method inspired by human infants visual
development system and Aquila has been a huge advantage providing the fast and user
friendly experimental environment.”
                                                          Neda Hantehzadeh, Southern Illinois University of Carbondale




                                                                                                                         20
Aquila
Terminal

   Interfaces to iCub face expression and force control modules
   ICub connection visualisation




                                                                  21
Aquila
Sequence Recorder

  Records, saves and replays sequences
  Works with force control module
  Set a home position, force threshold monitoring
  Different options: timing, custom output formatting, etc.




                                                              22
Aquila
Epigenetic Robotics Architecture
  Learning of names and objects through body association
  Research provided a number of predictions directly inspiring a number of follow up experiments in child development closing
  the gap between robotics and psychology




                                                                                                                                                                                                 23
            Morse, A.F., Belpaeme, T., Cangelosi, A., & Floccia, C., (2011) Modelling U Shaped Perfromance Curves in Ongoing Development. Proceedings of the Cognitive Science Conference 2011
Aquila
Multiple Time-scales Recurrent Neural Network

     Humans are good at learning complex actions                                                    Motor primitives are flexibly combined into novel sequences of actions

     Constant repetition of movements with certain                                                  Human motor control system known to have motor primitives implemented as
     components segmented as reusable elements                                                      low as at the spinal cord and hi-level planning and execution takes place in
                                                                                                    primary motor cortex




                                                                                                                                                                                                        24
M. Peniak, D. Marocco, J. Tani, Y. Yamashita, K. Fischer, A. Cangelosi (2011). Multiple Time Scales Recurrent Neural Network for Complex Action Acquisition. International Joint Conference on Development and Learning
(ICDL) and Epigenetic Robotics (ICDL-EPIROB) 2011. Frankfurt, Germany, August 24-27, 2011.
Aquila
Self-organising Maps

  Also known as Kohonen maps

  Type of artificial neural network that is
  trained using unsupervised learning

  Representing multidimensional data in
  much lower dimensional spaces –
  typically one or two dimensions

  This process, of reducing the
  dimensionality of vectors, is essentially
  a data compression technique known
  as vector quantisation

  Example: 3D colour input (rgb values)
  represented by a 2D map
                                              25
Aquila
Abstraction-reaction Accumulator
      Inspired by the early cybernetics work
      Developed by Chris Larcombe




C. Larcombe, A. Morse, A. Cangelosi (2011). Learning to React to Abstractions: Accumulating Adaptations in a Humanoid Embodiment. International Joint Conference on Development and Learning (ICDL) 26 Epigenetic
                                                                                                                                                                                                    and
Robotics (ICDL-EPIROB) 2011.
Aquila
Vision

  iCub and USB cameras
  Filters and transforms
  Record frame and videos
  Kinect support for iCub teleoperation




                                          27
Aquila
Simulator




            28
Aquila
Simulator




            29
Aquila
History

    Modules not available individually

    Compilation more difficult due to increasing number of modules

    Host machine execution only

    Linux-only support




                                                                     30
Aquila 2.0
Present

  Modules
       are independent from Aquila but can make use of its features (graphical interface, module management, etc.)
       can run in multiple instances on different hosts and GPU devices across networks


  Linux, Windows and OSX support

  Compiles as part of the main iCub repository

  Simpler, faster and more efficient

  Better platform for developers

  Ongoing work is focused on porting the old modules

                                                                                                                     31
Aquila 2.0

 Aquila 2.0
      detects compiled Aquila-compliant modules on
      the network

      launches any of these modules on any GPU or
      CPU processor found on the network

      dynamically creates their graphical user
      interfaces under individual tabs


 Aquila-compliant modules
      run as a standalone processes

      can be used without Aquila (terminal mode)

      specific ports are used for Aquila interfacing




                                                       32
Aquila and Evolutionary Robotics


                                   33
Evolutionary Robotics

            Relatively new technique for the automatic creation of autonomous robots

            Inspired by the Darwinian principle of selective reproduction of the fittest

            Views robots as autonomous artificial organisms that develop their own skills in
            close interaction with the environment and without human intervention

            Drawing heavily on biology and ethology, it uses the tools of neural networks,
            genetic algorithms, dynamic systems, and biomorphic engineering




                                                                                                                                                            34
Nolfi S. and Floreano D. [2000]. Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines. Cambridge, MA: MIT Press/Bradford Books
Evolutionary Robotics

            The evolutionary process is notoriously time-consuming
            GPU implementations of genetic algorithms result in better results in much shorter time
                     For example see: P. Pospichal and J. Jaros, "Gpu-based acceleration of the genetic algorithm," in GECCO 2010




                                                                                                                                                            35
Nolfi S. and Floreano D. [2000]. Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines. Cambridge, MA: MIT Press/Bradford Books
Current Development at NVIDIA
Aquila and Evolutionary Robotics

  Novel GPU implementation of scalable genetic algorithms

  New module that is using these algorithms to facilitate
  evolution of a biologically-inspired vision system

  Modules ready by the end of 2012




                                                            36
"Imagination is the highest form of research"
                                 Albert Einstein




                                      Anthony Morse                          Martin Peniak
                              http://fostsvn.uopnet.plymouth.ac.uk/amorse/    www.martinpeniak.com
                                      anthony.morse@plymouth.ac.uk         martin.peniak@plymouth.ac.uk




                                                                                                          37

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Aquila: An Open-Source GPU-Accelerated Toolkit for Cognitive and Neuro-Robotics Research

  • 1. Aquila: An Open-Source GPU-Accelerated Toolkit for Cognitive Robotics Research Martin Peniak and Anthony Morse
  • 2. Agenda What is Cognitive Robotics? The iCub humanoid robot GPUs and robotics Aquila - past, present and future 2
  • 4. Cognitive Robotics Cognitive Robotics draws from classical robotics, artificial intelligence, cognitive science and neurobiology to elucidate and synthesize aspects of action-oriented intelligence. Linguistics Computer Science Psychology AI Neuroscience 4
  • 5. Cognitive Robotics Substractions Memory World Puzzles ... ... 5
  • 6. Ungrounded Symbol Systems Computers can easily be pre-programmed to memorise a dictionary, but cannot fully understand the language they use STRENGTH: power to resist force FORCE: strength or power exerted upon an object PUSH: to apply force to (something) in order to move it POWER: applied force producing motion 6 Example of self-referential, amodal network of word definitions in Webster’s Dictionary (Roy 2005) Chinese Room (Searle 1980)
  • 7. Cognitive Robotics An objects identity or category, it’s meaning lies in the profile of dynamic interactions that it affords 7
  • 8. Cognitive Robotics Replicating child psychology experiments Testing psychological theory Finding common processes underlying disparate phenomenon Prompting new questions and investigations Developing technology 8
  • 9. iCub humanoid robot The dimensions are similar to that of a 3.5 year old child 53 degrees of freedom Came from the European Framework 6 project: RobotCub (www.robotcub.org) There are now 20 iCubs in different labs in Europe and 1 in the US Continued design - v2.0 to come out soon Various ongoing project outcomes are distributed via an open-source software repository and via hardware upgrades A free iCub simulator is available 9
  • 10. iCub humanoid robot Touch sensitive skin and compliant actuation 10
  • 11. iCub humanoid robot Dexterous hands for object manipulation 11
  • 13. iCub humanoid robot Simulator Open-source Developed as part of a joint effort with the European project iTalk Widely adopted within cognitive robotics community V. Tikhanoff, P. Fitzpatrick, F. Nori, L. Natale, G. Metta, and A. Cangelosi, “The icub humanoid robotsimulator,” In International Conference on Intel ligentRObots and Systems IROS, Nice, France, 2008 13
  • 14. GPUs in robotics It’s all about real time Motion compliance < 1 ms Vision (30fps) < 33 ms Vision (60fps) < 16 ms We typically take 33 ms as the cut-off time. 1 complete cycle of everything critical MUST be completed in that time. Of course some processes are not critical and their information can be used as and when it becomes available, subject to various constraints. 14
  • 15. GPUs in robotics It’s all about real time Our models of learning, integrating multi-modal information, and controlling the robot are based on Neural Networks Each neuron within a neural network computes its own activation based on local information Learning algorithms continuously adapt the strength of connections between neurons Both processes are inherently parallel 15
  • 16. GPUs in robotics It’s all about real time CUDA lets us build much bigger and more complex neural networks while remaining within the real time constraints CUDA also accelerates some of the pre-processing required (e.g. vision processing) 16
  • 17. GPUs in robotics Vision processing example Carlo Ciliberto and Vadim Tikhanoff used GPU-accelerated SIFT-based feature extraction for iCub object recognition “In Robotics it is crucial to have systems that perform in real time since the artificial agent has to continuously adapt to (possibly sudden) changes in the environment. Clearly the CPU implementation of the SIFT detection/extraction was unsatisfactory in this regard while the GPU one allowed us to overcome this obstacle.” “We did not perform a rigorous comparison between CPU and GPU implementations since the difference of computational times was so huge we did not bother. In particular we experienced times greater than 1000 ms for the OpenCV Sift detector/extractor on CPU while around 20 ms for the GPU implementation.” 17
  • 18. Aquila An Open-Source GPU-Accelerated Toolkit for Cognitive Robotics Research 18
  • 19. Aquila History Project started in 2009 as a simple open-source GUI application for our research with iCub CUDA was adopted to accelerate growing number of interacting systems Several limitations have recently led to a completely decoupled redesign 19
  • 20. Aquila Research with iCub made easier and more accessible “There are many advantages using Aquila ....I am mainly using Aquila for eye tracking and the feature of video streaming using the screen in the iCub world. The biggest advantage is the ability to record the eyes and control what is displayed on the screen at the same time.” “My future goal would be to add a new eye tracking method inspired by human infants visual development system and Aquila has been a huge advantage providing the fast and user friendly experimental environment.” Neda Hantehzadeh, Southern Illinois University of Carbondale 20
  • 21. Aquila Terminal Interfaces to iCub face expression and force control modules ICub connection visualisation 21
  • 22. Aquila Sequence Recorder Records, saves and replays sequences Works with force control module Set a home position, force threshold monitoring Different options: timing, custom output formatting, etc. 22
  • 23. Aquila Epigenetic Robotics Architecture Learning of names and objects through body association Research provided a number of predictions directly inspiring a number of follow up experiments in child development closing the gap between robotics and psychology 23 Morse, A.F., Belpaeme, T., Cangelosi, A., & Floccia, C., (2011) Modelling U Shaped Perfromance Curves in Ongoing Development. Proceedings of the Cognitive Science Conference 2011
  • 24. Aquila Multiple Time-scales Recurrent Neural Network Humans are good at learning complex actions Motor primitives are flexibly combined into novel sequences of actions Constant repetition of movements with certain Human motor control system known to have motor primitives implemented as components segmented as reusable elements low as at the spinal cord and hi-level planning and execution takes place in primary motor cortex 24 M. Peniak, D. Marocco, J. Tani, Y. Yamashita, K. Fischer, A. Cangelosi (2011). Multiple Time Scales Recurrent Neural Network for Complex Action Acquisition. International Joint Conference on Development and Learning (ICDL) and Epigenetic Robotics (ICDL-EPIROB) 2011. Frankfurt, Germany, August 24-27, 2011.
  • 25. Aquila Self-organising Maps Also known as Kohonen maps Type of artificial neural network that is trained using unsupervised learning Representing multidimensional data in much lower dimensional spaces – typically one or two dimensions This process, of reducing the dimensionality of vectors, is essentially a data compression technique known as vector quantisation Example: 3D colour input (rgb values) represented by a 2D map 25
  • 26. Aquila Abstraction-reaction Accumulator Inspired by the early cybernetics work Developed by Chris Larcombe C. Larcombe, A. Morse, A. Cangelosi (2011). Learning to React to Abstractions: Accumulating Adaptations in a Humanoid Embodiment. International Joint Conference on Development and Learning (ICDL) 26 Epigenetic and Robotics (ICDL-EPIROB) 2011.
  • 27. Aquila Vision iCub and USB cameras Filters and transforms Record frame and videos Kinect support for iCub teleoperation 27
  • 30. Aquila History Modules not available individually Compilation more difficult due to increasing number of modules Host machine execution only Linux-only support 30
  • 31. Aquila 2.0 Present Modules are independent from Aquila but can make use of its features (graphical interface, module management, etc.) can run in multiple instances on different hosts and GPU devices across networks Linux, Windows and OSX support Compiles as part of the main iCub repository Simpler, faster and more efficient Better platform for developers Ongoing work is focused on porting the old modules 31
  • 32. Aquila 2.0 Aquila 2.0 detects compiled Aquila-compliant modules on the network launches any of these modules on any GPU or CPU processor found on the network dynamically creates their graphical user interfaces under individual tabs Aquila-compliant modules run as a standalone processes can be used without Aquila (terminal mode) specific ports are used for Aquila interfacing 32
  • 33. Aquila and Evolutionary Robotics 33
  • 34. Evolutionary Robotics Relatively new technique for the automatic creation of autonomous robots Inspired by the Darwinian principle of selective reproduction of the fittest Views robots as autonomous artificial organisms that develop their own skills in close interaction with the environment and without human intervention Drawing heavily on biology and ethology, it uses the tools of neural networks, genetic algorithms, dynamic systems, and biomorphic engineering 34 Nolfi S. and Floreano D. [2000]. Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines. Cambridge, MA: MIT Press/Bradford Books
  • 35. Evolutionary Robotics The evolutionary process is notoriously time-consuming GPU implementations of genetic algorithms result in better results in much shorter time For example see: P. Pospichal and J. Jaros, "Gpu-based acceleration of the genetic algorithm," in GECCO 2010 35 Nolfi S. and Floreano D. [2000]. Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines. Cambridge, MA: MIT Press/Bradford Books
  • 36. Current Development at NVIDIA Aquila and Evolutionary Robotics Novel GPU implementation of scalable genetic algorithms New module that is using these algorithms to facilitate evolution of a biologically-inspired vision system Modules ready by the end of 2012 36
  • 37. "Imagination is the highest form of research" Albert Einstein Anthony Morse Martin Peniak http://fostsvn.uopnet.plymouth.ac.uk/amorse/ www.martinpeniak.com anthony.morse@plymouth.ac.uk martin.peniak@plymouth.ac.uk 37