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Neural Robot Control

Neural Robot Control. Cornelius Weber Hybrid Intelligent Systems University of Sunderland Talk at Nottingham Trent University, 8 th December 2004 on the occasion of returning the MI competition trophy Collaborators: Mark Elshaw, Alex Zochios, Chris Rowan and Stefan Wermter. Contents.

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Neural Robot Control

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  1. Neural Robot Control Cornelius Weber Hybrid Intelligent Systems University of Sunderland Talk at Nottingham Trent University, 8th December 2004 on the occasion of returning the MI competition trophy Collaborators: Mark Elshaw, Alex Zochios, Chris Rowan and Stefan Wermter

  2. Contents • Visual cortex & reinforcement network for docking • Cortex self-imitation network for docking • Imitation networks for multiple actions: 1-stage/2-stage hierarchical network • Outlook

  3. Contents • Visual cortex & reinforcement network for docking • Cortex self-imitation network for docking • Imitation networks for multiple actions: 1-stage/2-stage hierarchical network • Outlook

  4. Example Task: Docking

  5. Docking ArchitectureInformation Flow

  6. Docking ArchitectureTraining (1/3) unsupervised training generative model sparse distributed coding

  7. V1 Receptive Fields(training result)

  8. Comparison ofResponse Characteristics linear sparse competitive winner

  9. Attractor Network:Competition via Relaxation weight profile activation profile activation update y(t+1) = f (Wlat y(t))

  10. Docking ArchitectureTraining (2/3) supervised training, attractor network for pattern completion

  11. Docking ArchitectureVisual System

  12. Docking ArchitectureTraining (3/3) reinforcement training actor-critic model

  13. Contents • Visual cortex & reinforcement network for docking • Cortex self-imitation network for docking • Imitation networks for multiple actions: 1-stage/2-stage hierarchical network • Outlook

  14. Mirror NeuronDocking ArchitectureInformation Flow

  15. Mirror NeuronDocking ArchitectureTraining unsupervised training generative model distributed coding

  16. Mirror NeuronDocking ArchitectureTraining supervised training, attractor network for prediction

  17. Mirror Neuron Self-ImitationDocking ArchitectureInformation Flow

  18. Basal Ganglia vs. Motor Cortex Basal ganglia units are active during early task acquisition but not at a later stage (rat T maze decision task). Jog et al. (1999) Science, 286, 1158-61 early: late: Basal Ganglia ≙ state space? Motor cortex might take over BG function via self-imitation.

  19. Docking via Mirror Neurons

  20. Contents • Visual cortex & reinforcement network for docking • Cortex self-imitation network for docking • Imitation networks for multiple actions: 1-stage/2-stage hierarchical network • Outlook

  21. Simulated Robot Environment

  22. Imitation Model Choice

  23. Areas of Motor- and Language Representations motor units forward back left right individual unit’s receptive fields in hidden area language units ‘go’ ‘pick’ ‘lift’ all

  24. Areas of Task-Specific Activations ‘go’ ‘pick’ ‘lift’ Production: Recognition: Activations agree with the Somatotopy-of-Action-Words Model. ‘go’ ‘pick’ ‘lift’

  25. Language InstructedImitative Behaviour ‘go’ ‘pick’ ‘lift’

  26. Imitation Model Choice

  27. Neuron’s Receptive Fields in HM Area forward backward left right motor units 4 SOM-area units

  28. Conclusion for Imitation Network A neural network as a generative model for sensory stimuli • generates interactive action sequences • allows for context dependent interactive action sequences

  29. Contents • Visual cortex & reinforcement network for docking • Cortex self-imitation network for docking • Imitation networks for multiple actions: 1-stage/2-stage hierarchical network • Outlook

  30. Outlook (1/2): Object-Background Separation for Enhanced Object Learning

  31. Outlook (2/2): Docking Range Extension by Neural Coordinate Transformations

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