Whisking with robots from rat vibrissae to biomimetic technology for active touch

This article summarizes some of the key features of the rat vibrissal system, including the actively controlled sweeping movements of the vibrissae known as whisking, and reviews the past and ongoing research aimed at replicating some of this functionality in biomimetic robots

Impact of Ground Truth Annotation Quality on Performance of Semantic Image Segmentation of Traffic Conditions

Preparation of high-quality datasets for the urban scene understanding is a labor-intensive task, especially, for datasets designed for the autonomous driving applications. The application of the coarse ground truth (GT) annotations of these datasets without detriment to the accuracy of semantic image segmentation (by the mean intersection over union - mIoU) could simplify and speedup the dataset preparation and model fine tuning before its practical application. Here the results of the comparative analysis for semantic segmentation accuracy obtained by PSPNet deep learning architecture are presented for fine and coarse annotated images from Cityscapes dataset. Two scenarios were investigated: scenario 1 - the fine GT images for training and prediction, and scenario 2 - the fine GT images for training and the coarse GT images for prediction. The obtained results demonstrated that for the most important classes the mean accuracy values of semantic image segmentation for coarse GT annotations are higher than for the fine GT ones, and the standard deviation values are vice versa. It means that for some applications some unimportant classes can be excluded and the model can be tuned further for some classes and specific regions on the coarse GT dataset without loss of the accuracy even. Moreover, this opens the perspectives to use deep neural networks for the preparation of such coarse GT datasets.Comment: 10 pages, 6 figures, 2 tables, The Second International Conference on Computer Science, Engineering and Education Applications (ICCSEEA2019) 26-27 January 2019, Kiev, Ukrain

How serious can the stealth bias be in gravitational wave parameter estimation?

The upcoming direct detection of gravitational waves will open a window to probing the strong-field regime of general relativity (GR). As a consequence, waveforms that include the presence of deviations from GR have been developed (e.g. in the parametrized post-Einsteinian approach). TIGER, a data analysis pipeline which builds Bayesian evidence to support or question the validity of GR, has been written and tested. In particular, it was shown recently that data from the LIGO and Virgo detectors will allow to detect deviations from GR smaller than can be probed with Solar System tests and pulsar timing measurements or not accessible with conventional tests of GR. However, evidence from several detections is required before a deviation from GR can be confidently claimed. An interesting consequence is that, should GR not be the correct theory of gravity in its strong field regime, using standard GR templates for the matched filter analysis of interferometer data will introduce biases in the gravitational wave measured parameters with potentially disastrous consequences on the astrophysical inferences, such as the coalescence rate or the mass distribution. We consider three heuristic possible deviations from GR and show that the biases introduced by assuming GR's validity manifest in various ways. The mass parameters are usually the most affected, with biases that can be as large as $30$ standard deviations for the symmetric mass ratio, and nearly one percent for the chirp mass, which is usually estimated with sub-percent accuracy. We conclude that statements about the nature of the observed sources, e.g. if both objects are neutron stars, depend critically on the explicit assumption that GR it the right theory of gravity in the strong field regime.Comment: 10 pages, 9 figures, 5 table

Adaptive cancelation of self-generated sensory signals in a whisking robot

Sensory signals are often caused by one's own active movements. This raises a problem of discriminating between self-generated 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 often-used 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

Cerebellar potentiation and learning a whisker-based object localization task with a time response window

Whisker-based object localization requires activation and plasticity of somatosensory and motor cortex. These parts of the cerebral cortex receive strong projections from the cerebellum via the thalamus, but it is unclear whether and to what extent cerebellar processing may contribute to such a sensorimotor task. Here, we subjected knock-out mice, which suffer from impaired intrinsic plasticity in their Purkinje cells and long-term potentiation at their parallel fiber-to-Purkinje cell synapses (L7-PP2B), to an object localization task with a time response window (RW). Water-deprived animals had to learn to localize an object with their whiskers, and based upon this location they were trained to lick within a particular period ("go" trial) or refrain from licking ("no-go" trial). L7-PP2B mice were not ataxic and showed proper basic motor performance during whisking and licking, but were severely impaired in learning this task compared with wild-type littermates. Significantly fewer L7-PP2B mice were able to learn the task at long RWs. Those L7-PP2B mice that eventually learned the task made unstable progress, were significantly slower in learning, and showed deficiencies in temporal tuning. These differences became greater as theRWbecame narrower. Trained wild-type mice, but not L7-PP2B mice, showed a net increase in simple spikes and complex spikes of their Purkinje cells during the task. We conclude that cerebellar processing, and potentiation in particular, can contribute to learning a whisker-based object localization task when timing is relevant. This study points toward a relevant role of cerebellum- cerebrum interaction in a sophisticated cognitive task requiring strict temporal processing