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IEEE-RAS / IFRR SCHOOL OF ROBOTICS SCINECE ON LEARNING

Lazise, Garda Lake, Italy, 24-28 September 2007

IEEE-RAS / IFRR SCHOOL OF ROBOTICS SCIENCE ON LEARNING

1. WHAT WE HAVE LEARNED.

2. NOVELTY DETECTION.

3. LEARNING WITH RECURRENT NEURAL NETWORK WITH PARAMETRIC BIASES.

4. APPLICATIONS IN OUR RESEARCH AREA

LUIS PAYA LUCA LONINI ALIREZA DERAKHSHAN

GROUP 9

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1. WHAT WE HAVE LEARNED

IEEE-RAS / IFRR SCHOOL OF ROBOTICS SCINECE ON LEARNING

Lazise, Garda Lake, Italy, 24-28 September 2007

• Use of a simple robotic platform to carry out experiments in complex techniques of machine learning.

• We have dealt with simple external information - more complex information should be added e.g. more sensory data.

• Learning by imitation

• Analytical models (system identification, policy learning by imitation).

• Non Analytical models (learning with recurrent neural networks with parametric biases).

• Statistical Analysis and Data Mining with Orange.

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2. NOVELTY DET ECTION

• Working with readings from a Magellan’s 16 sonar sensors in a wall following behavior.

• 1st train: = 12 · (standard deviation) = 0.6

• 58 kernels in the model base.

IEEE-RAS / IFRR SCHOOL OF ROBOTICS SCINECE ON LEARNING

Lazise, Garda Lake, Italy, 24-28 September 2007

• Distances of each test data to the nearest kernel of the model base.

• Not a clear novelty among the test data.

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2. NOVELTY DETECTION

• 2nd train: = 7 · (standard deviation) = 0.6

• 321 kernels in the model base.

IEEE-RAS / IFRR SCHOOL OF ROBOTICS SCINECE ON LEARNING

Lazise, Garda Lake, Italy, 24-28 September 2007

• Distances of each test data to the nearest kernel of the model base.

• Two possible candidates.

• The 2nd one (reading 100) is the novelty one (maximum distance to the nearest kernel).

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3. LEARNING WITH RECURRENT NEURAL NETWORK WITH PARAMETRIC BIASES.

• Building complex behaviors by combining simple primitive behaviors.

• Each simple primitive can be coded with 2 biases.

IEEE-RAS / IFRR SCHOOL OF ROBOTICS SCINECE ON LEARNING

Lazise, Garda Lake, Italy, 24-28 September 2007

Biases:

[0.68 0.40] Sinusoid

[0.73 0.36] Left

[0.19 0.78] Right

Biases: Keep Object Left

[0.99 0.0]

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3. LEARNING WITH RECURRENT NEURAL NETWORK WITH PARAMETRIC BIASES.

• Adding new primitives is possible

IEEE-RAS / IFRR SCHOOL OF ROBOTICS SCINECE ON LEARNING

Lazise, Garda Lake, Italy, 24-28 September 2007

Biases: Obstacle Avoidance

[0.08 0.29]

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Applications in OUR Research Area

IEEE-RAS / IFRR SCHOOL OF ROBOTICS SCINECE ON LEARNING

Lazise, Garda Lake, Italy, 24-28 September 2007

• Appearance-based Navigation

• These techniques can be applied to the localization and navigation of a mobile robot using more complex information (e.g. The information of the whole scene, laser measures, etc.).

• It is necessary to analyze the scene and extract the most relevant information.

• Classification of Playing Behavior

• Novelty Detection can be applied to categorize different Playing Behavior based on some reference behaviors.

• Human motor learning models

• Machine learning techniques and Experiments with robots can be useful to test hypothesis on neuroscientific theories on how we do organize movements

• Novel control techniques can be applied to new generation robots