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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. GROUP 9. LUIS PAYA LUCA LONINI ALIREZA DERAKHSHAN. WHAT WE HAVE LEARNED. NOVELTY DETECTION.
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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 GROUP 9 LUIS PAYA LUCA LONINI ALIREZA DERAKHSHAN • WHAT WE HAVE LEARNED. • NOVELTY DETECTION. • LEARNING WITH RECURRENT NEURAL NETWORK WITH PARAMETRIC BIASES. • APPLICATIONS IN OUR RESEARCH AREA
IEEE-RAS / IFRR SCHOOL OF ROBOTICS SCINECE ON LEARNING 1. WHAT WE HAVE LEARNED 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.
IEEE-RAS / IFRR SCHOOL OF ROBOTICS SCINECE ON LEARNING 2. NOVELTY DET ECTION Lazise, Garda Lake, Italy, 24-28 September 2007 • Working with readings from a Magellan’s 16 sonar sensors in a wall following behavior. • 1st train: • s = 12 · (standard deviation) • q = 0.6 • 58 kernels in the model base. • Distances of each test data to the nearest kernel of the model base. • Not a clear novelty among the test data.
IEEE-RAS / IFRR SCHOOL OF ROBOTICS SCINECE ON LEARNING 2. NOVELTY DETECTION Lazise, Garda Lake, Italy, 24-28 September 2007 • 2nd train: • s = 7 · (standard deviation) • q = 0.6 • 321 kernels in the model base. • 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).
IEEE-RAS / IFRR SCHOOL OF ROBOTICS SCINECE ON LEARNING 3. LEARNING WITH RECURRENT NEURAL NETWORK WITH PARAMETRIC BIASES. Lazise, Garda Lake, Italy, 24-28 September 2007 • Building complex behaviors by combining simple primitive behaviors. • Each simple primitive can be coded with 2 biases. Biases: [0.68 0.40] Sinusoid [0.73 0.36] Left [0.19 0.78] Right Biases: Keep Object Left [0.99 0.0]
IEEE-RAS / IFRR SCHOOL OF ROBOTICS SCINECE ON LEARNING 3. LEARNING WITH RECURRENT NEURAL NETWORK WITH PARAMETRIC BIASES. Lazise, Garda Lake, Italy, 24-28 September 2007 • Adding new primitives is possible Biases: Obstacle Avoidance [0.08 0.29]
IEEE-RAS / IFRR SCHOOL OF ROBOTICS SCINECE ON LEARNING Applications in OUR Research Area 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
