Hybrid Intelligent Systems for Robotic Docking: Insights from Neural Networks
This presentation by Cornelius Weber at Nottingham Trent University explores advanced hybrid intelligent systems applied to robotic docking. The talk covers the role of visual cortex and reinforcement networks, self-imitation networks, and imitation networks for executing multiple actions in robotics. Key findings include the effectiveness of generative models in unsupervised learning, the comparison of response characteristics in attractor networks, and the significance of mirror neurons in imitative behavior. The session highlights both theoretical frameworks and practical applications in robotic systems.
Hybrid Intelligent Systems for Robotic Docking: Insights from Neural Networks
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Presentation Transcript
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
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
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
Docking ArchitectureTraining (1/3) unsupervised training generative model sparse distributed coding
Comparison ofResponse Characteristics linear sparse competitive winner
Attractor Network:Competition via Relaxation weight profile activation profile activation update y(t+1) = f (Wlat y(t))
Docking ArchitectureTraining (2/3) supervised training, attractor network for pattern completion
Docking ArchitectureTraining (3/3) reinforcement training actor-critic model
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
Mirror NeuronDocking ArchitectureTraining unsupervised training generative model distributed coding
Mirror NeuronDocking ArchitectureTraining supervised training, attractor network for prediction
Mirror Neuron Self-ImitationDocking ArchitectureInformation Flow
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.
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
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
Areas of Task-Specific Activations ‘go’ ‘pick’ ‘lift’ Production: Recognition: Activations agree with the Somatotopy-of-Action-Words Model. ‘go’ ‘pick’ ‘lift’
Language InstructedImitative Behaviour ‘go’ ‘pick’ ‘lift’
Neuron’s Receptive Fields in HM Area forward backward left right motor units 4 SOM-area units
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
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
Outlook (1/2): Object-Background Separation for Enhanced Object Learning
Outlook (2/2): Docking Range Extension by Neural Coordinate Transformations
