On-line expectation-based novelty detection for mobile robots.

This paper presents a recurrent neural network based novelty filter where a Scitos G5 mobile robot explored the environment and built dynamic models of observed sensory–motor values, then the acquired models of normality are used to predict the expected future values of sensory–motor inputs during patrol. Novelties could be detected whenever the prediction error between models-predicted values and actual observed values exceeded a local novelty threshold. The network is trained on-line; it grows by inserting new nodes when abnormal observation is perceived from the environment; and also shrinks when the learned information is not necessary anymore. In addition, the network is also capable of learning region-specific novelty thresholds on-line continuously. To evaluate the proposed algorithm, real-world robotic experiments were conducted by fusing sensory perceptions (vision and laser sensors) and the robot motor control outputs (translational and rotational velocities). Experimental results showed that all of the novelty cases were highlighted by the proposed algorithms and it produced reliable local novelty thresholds while the robot patrols in the noisy environment. The statistical analysis showed that there was a strong correlation between the novelty filter responses and the actual novelty status. Furthermore, the filter was also compared with another novelty filter and the results showed that the proposed system performed better novelty detection. [ABSTRACT FROM AUTHOR]