How can a robot evaluate its own behavior? A neural model for self-assessment

Allowing a robot to autonomously navigate wide and unknown environments not only requires a set of robust strategies to cope with miscellaneous situations, but also needs mechanisms of self-assessment for guiding learning and monitoring strategies. Monitoring strategies requires feedbacks on the behavior's quality, from a given fitness system, to take correct decisions. In this work, we focus on how violations of expectations of such fitness system can be detected. Following an incremental and bio-mimetic approach, we first present two different sensorimotor strategies our robot can use to navigate: a Place Cells based strategy and a road following strategy. Then, we present a neural architecture that may be able to evaluate both navigation strategies. This model is based on an online novelty detection algorithm using a neural predictor. This neural predictor learns contingencies between sensations and actions, giving the expected sensation based from the previous perception. Prediction error, coming from surprising events, provides a direct measure of the quality of the underlying sensorimotor contingencies involved. We propose that this model might be a key structure toward self-assessment. We made several experiments that can account for such properties for both strategies.