1. On-line Novelty Detection With Application to Mobile Robotics Stephen Marsland Imaging Science and Biomedical Engineering University of Manchester

2. Novelty Detection Highlighting inputs that differ in some way from the ‘normal’ stream of inputs

3. Novelty Detection in Animals Detect unusual stimuli –reduce the amount of information that needs to be processed –enables the animal to focus on important information –helps avoid predators

4. Novelty Detection in Artificial Learning Systems Detect unusual stimuli –reduce the amount of information that needs to be processed –enables the learner to focus on important information –helps avoid predators! …

5. Novelty Detection in Artificial Learning Systems Detect unusual stimuli –Reduce the amount the system has to learn Detect anomalies in a datastream Detect when class pdfs or mixture ratios change Detect when a new class appears

6. Statistical Outlier Detection

10. Novelty Detection in Artificial Learning Systems Applications include: –Machine fault monitoring –Medical diagnosis –Inspection agent –Pre-processing for inputs

11. Approach Learn a representation of normality Evaluate how well each input fits into the acquired model Highlight those inputs that are not well represented by the model p(x|C 1 )P(C 1 ) p(x|C 2 )P(C 2 )

12. Requirements Learning algorithm Method of evaluating the novelty of a new input Some parameter tuning (how much should the learning algorithm generalise)

13. Possible Approaches: Auto-assocative network

14. Learn to reproduce inputs at the outputs, so that the bottleneck learns a lower dimensional representation - principal components After training, will settle to a trained input, and subtracting the input from the output shows the novelty in the current input

15. Possible Approaches: Mixtures of Gaussians Train a Gaussian Mixture Model to represent the training data (simple using EM) Look for input points that are not contained within any of the mixtures

16. How Do Animals Detect Novelty? Habituation Reduction in synaptic efficacy when a stimulus is seen repeatedly without ill effect

17. Habituation Simple to model Gives a method of deciding how familiar an input is – how frequently has it been seen before This is what is needed for novelty detection

18. Habituation

19. Novelty Detection Using Habituation Some form of clustering algorithm Learn a model of inputs Habituate those nodes that fire frequently and therefore recognise familiar features If an input causes an unhabituated node to fire it is novel If an input is not represented by the network it is novel

20. Focus: Inspection Train a robot to recognise all of the normal features of some environment, as perceived by the robot’s sensors Any features that are not recognised by the system (novelties) are possible faults

21. Requirements for Novelty Detection Inspection On-line learning Robustness to some ‘novel’ inputs Quantification of the amount of novelty in each input Environment specificity

22. The ‘Grow When Required’ Network A self-organising neural network that can operate on-line Produces perfectly-topology preserving maps Has novelty detection capability built in through habituating synapses Is similar to, but derived independently from, FOSART – Baraldi & Alpaydin

23. The ‘Grow When Required’ Network

27. Topology Preservation

28. 5,000 inputs 15,000 inputs

29. Topology Preservation 25,000 inputs 35,000 inputs

30. Using the GWR as a Novelty Filter

33. Training a Filter Start with an empty GWR network As the robot travels down the corridor, sensory perceptions as taken as inputs to the network The network adapts to learn about each new input and assesses them for novelty It may take a few runs to learn about an environment

34. Inspection Using Novelty Detection 16 sonar sensors 16 IR sensors Bumpers Monochrome camera

35. Inspection Using Novelty Detection Environment A

36. Inspection Using Novelty Detection

39. Environment BEnvironment C

40. Inspection Using Novelty Detection

42. Forgetting

43. Why Not Just Use the Self-Organising Map?

44. Small SOM

45. Large SOM

46. GWR

47. Using Visual Input Sonar inputs are fairly low dimensional, and do not provide that much information about the environment Can also consider using a camera The camera mounted on the robot is a low resolution monochrome camera

48. The Visual Magnet Ensure that the robot faces in the same direction each time that it is one position Perform edge detection, generate a histogram and rotate the turret of the robot to centre the largest element of the histogram Works approximately 75% of the time

49. Generating an Input Vector Plenty of choice: –Edge detection histograms –Principal component filters –Raw image –‘Fingerprint’ – subset of image pixels chosen in some way All are essentially hacks

50. Visual Inspection Principal Component Filters Spiral ‘Fingerprint’

51. Visual Inspection Spiral ‘Fingerprint’

52. “…the novelty of the wife in the best friend’s bed lies neither in the wife, the friend, nor the bed, but in the conjunction of the three.” O’Keefe and Nadel, 1978 Requirements for Novelty Detection Inspection

53. Selecting Different Novelty Filters A bank of novelty filters are used, with each one trained in a different environment A set of familiarity vectors keep a record of how well each novelty filter recognises the current perceptions

54. Selecting Different Novelty Filters For each input: –For each filter: Compute the output of each novelty filter Reduce the element of the familiarity vector for the filter proportional to the output Increase the elements of the familiarity vector for every other filter so that the sum remains normalised

55. Selecting a Filter

56. Adding New Filters What if none of the filters represents an environment well? –Total novelty is very high –Several of the filters have similar familiarity scores Can then automatically add a new filter and train it in that environment

57. Adding New Filters

58. Other Applications Ball-bearings Medical Landmark selection

59. Conclusions Novelty detection is a useful capability for learning systems The GWR network enables on-line novelty detection and hence, robot inspection Several filters trained in different environments provides environment- specific novelty detection, with the system deciding which environment it is currently in The system can train new filters as required

60. On-line Novelty Detection With Application to Mobile Robotics Stephen Marsland Imaging Science and Biomedical Engineering University of Manchester stephen.marsland@man.ac.uktephen.marsland@man.ac.uk http://www.isbe.man.ac.uk/~srm/