Adaptive Cancelation of Self-Generated Sensory Signals in a Whisking Robot

Abstract

Article:Anderson, S.R., Pearson, M.J., Pipe, A. et ReuseUnless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version -refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher's website. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. Abstract-Sensory signals are often caused by one's own active movements. This raises a problem of discriminating between selfgenerated sensory signals and signals generated by the external world. Such discrimination is of general importance for robotic systems, where operational robustness is dependent on the correct interpretation of sensory signals. Here, we investigate this problem in the context of a whiskered robot. The whisker sensory signal comprises two components: one due to contact with an object (externally generated) and another due to active movement of the whisker (self-generated). We propose a solution to this discrimination problem based on adaptive noise cancelation, where the robot learns to predict the sensory consequences of its own movements using an adaptive filter. The filter inputs (copy of motor commands) are transformed by Laguerre functions instead of the oftenused tapped-delay line, which reduces model order and, therefore, computational complexity. Results from a contact-detection task demonstrate that false positives are significantly reduced using the proposed scheme.Index Terms-Force and tactile sensing, internal model, learning and adaptive systems, neurorobotics, noise cancelation.

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“…ÔSignal conditioningÕ indicates the importance of early processing of sensory signals to, for instance, distinguish components of the signal that may be due to the organismÕs (or robotÕs) own movement rather than to contact with the external world. Some neurobiological evidence, and our own modelling work, suggest an important role for the cerebellum in this regard (Anderson, Pearson et al, 2010;Anderson, Porrill et al, 2012).…”

“…(b) A proposed biological whisking noise cancellation scheme. See (Anderson, Pearson et al, 2010) for further details. Adaptive filter model of the cerebellum.…”

“…In this case, functional MRI data (Blakemore, Wolpert et al, 1998) also indicates a role for the cerebellum in predicting sensory signals due to self-movement thereby making them seem less amusing! The above considerations led us to the hypothesis that rats may use their cerebellum to generate a signal which cancels the effects of self-generated whisking noise on incoming sensory signals from the whiskers (see (Anderson, Pearson et al, 2010; for a review of the wider literature on the role of the cerebellum in sensory noise cancellation). Our first step in investigating this hypothesis was to determine whether our proposed mechanism would work in principle, by using it to achieve noise cancellation in a whisking robot (Anderson, Pearson et al, 2010).…”

“…The above considerations led us to the hypothesis that rats may use their cerebellum to generate a signal which cancels the effects of self-generated whisking noise on incoming sensory signals from the whiskers (see (Anderson, Pearson et al, 2010; for a review of the wider literature on the role of the cerebellum in sensory noise cancellation). Our first step in investigating this hypothesis was to determine whether our proposed mechanism would work in principle, by using it to achieve noise cancellation in a whisking robot (Anderson, Pearson et al, 2010). The step had two goals, first to solve the practical problem of noise cancellation in the robot, and secondly to provide a theoretical basis for studying noise cancellation in whisking animals.…”

“…Much of the formative work was conducted by Bernard Widrow and was linked as an application problem to the least-mean-squares (LMS) adaptive filtering algorithm. The generic noise cancellation scheme is illustrated in figure 6, for detailed explanation see (Anderson, Pearson et al, 2010). The weights of the adaptive filter in the noise cancellation scheme (figure 6a) are adjusted by removing the correlations in the clean signal from the reference noise, implemented via the LMS rule.…”