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Environment. Actuator Motions. Raw Sensor Input. Robot states. Actuators. Sensing. State Info. Conditioned Sensor Input. Observation. Learning Trigger. Control. Learning. Commands. Behavior Controller. Sensor/action pairs. A-D on sound card. microphone.

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  1. Environment Actuator Motions Raw Sensor Input Robot states Actuators Sensing State Info Conditioned Sensor Input Observation Learning Trigger Control Learning Commands Behavior Controller Sensor/action pairs

  2. A-D on sound card microphone Note: This process occurs for each microphone at a rate of 4 times a second. In order to smooth out the amplitudes associated with intermittent chirps, the average power is kept as a running average over 1 second. ¼ second of data Zero pad /* Sum up power in chirp. */ Sum = 0 For j = start freq, end freq Sum = FFTpower[j] + Sum End j FFT Average Power = Sum/chirp freq range

  3. Left Sensor Right Sensor Sensor Values Sensor/Action FIFO Are changes in Sensor values extreme? Sensor/Action pairs Yes No Reactive Sub-system Non-Reactive Sub-system Single Commands Actuators

  4. Left Sensor Right Sensor Sensor Values Sensor/Action FIFO Do sensor values indicate reactive action needed? Sensor/Action pairs Yes No Reactive Sub-system Inhibit Non-Reactive Sub-system Single Commands Actuators

  5. Left Sensor Right Sensor Sensor Values Sensor/Action FIFO Are changes in Sensor values extreme? Sensor/Action pairs Yes No Action Sequence Library Reactive Sub-system Inhibit Non-Reactive Sub-system Single Commands Actuators

  6. Left analog Maxnet Left indicator Left microphone value T(0) Center analog Center indicator Action T(0) Right microphone value T(0) Right indicator Right analog

  7. Left micropone trend from times T(0) – T(-n) Left analog Maxnet Left indicator Center analog Center indicator Action T(0) Right indicator Right analog Right microphone trend from times T(0) – T(-n)

  8. Left microphone trend from times T(0) – T(-n) Heading Adjustment spread over times T(0) – T(n) Right microphone trend from times T(0) – T(-n)

  9. Left microphone value T(0) Heading Adjustment Right microphone value T(0)

  10. Left microphone values T(0) – T(-n) Heading Adjustment spread over times T(0) – T(n) Right microphone values T(0) – T(-n)

  11. Left microphone values T(0) – T(-n) Heading Adjustment T(0) Heading Adjustment T(1) Heading Adjustment T(2) Right microphone values T(0) – T(-n)

  12. Left microphone values T(0) – T(-n) Heading Adjustment for times T(0) – T(n) Actions T(-1) – T(-n) Right microphone values T(0) – T(-n)

  13. Left microphone values T(0) – T(-n) Left analog Maxnet Left indicator Actions T(-1) – T(-n) Center analog Center indicator Action T(0) Right indicator Right analog Right microphone values T(0) – T(-n)

  14. L L L C C C R R R Left microphone values T(0) – T(-n) Left indicator Maxnet Center indicator Action T(0) Right indicator Maxnet Actions T(-1) – T(-n) Action T(1) Maxnet Action T(2) Maxnet Action T(3) Right microphone values T(0) – T(-n)

  15. Robot states Is the core routine running and is there enough memory to learn from? Are goal states being met? Is reactive system being called often? yes no yes Trigger Reactive and non-reactive learning systems Trigger Reactive and non-reactive learning systems

  16. Sensor/Action FIFO Reactive Memory Preparation Non-Reactive Memory Preparation Reactive Memory Non - reactive Memory Reactive Behavior Generation Non-reactive Behavior Generation Output of process is a trained reactive NN Output of process is a trained non-reactive NN

  17. Reactive Memory Intensity Filter Negative and Positive Example Set Creation Correct Action Marking GA trains feed-forward NN to select the correct action given the sensor input. Output of process is a trained NN

  18. Non - reactive Memory Kohonen SOFM Codebook Codebook mirror process GA trains feed-forward NN using codebook and fitness function that maximizes sensor energy and minimizes changes in direction. Output of process is a trained NN

  19. Mirror Process Sensor/Action Memory Reactive Memory

  20. Sensor/Action Memory pairs Correlation Cluster Set Creation Reactive Memory Cluster Set Recent past Memory from T0 to T-n Is recent memory a member of the Cluster Set? Sensor FIFO Yes No Update hit count Add recent memory to Non-reactive Memory, mirror, and update miss count Recent memory Non - reactive Memory

  21. 0)LIRA 0)LIRA 0)LIRA 0)LIRA 0)LIRA 1)LIRA 1)LIRA 1)LIRA 1)LIRA 1)LIRA 2)LIRA 2)LIRA 2)LIRA 2)LIRA 2)LIRA 1)LIRA 1)LIR 2)LIRA 2)LIRA Recent Memory Kohonen SOFM Codebook SOFM Codebook Entry Close match found in SOFM codebook Intensity value from LIRA(0) used in fitness function of GA Feed forward NN Action New action replaces old action. New situation made in scratch area.

  22. 0)LIRA 0)LIRA 0) A 1)LIRA 1)LIRA 1) A 2)LIRA 2) A 2)LIRA Recent Memory RBF Network RBF Codebook Target for RBF entry (j) is RBF entry (m) RBF entry (j) Input to NN is past values from RBF codebook entries. GA uses actions from target entry to train NN. Feed forward NN

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