The brain's response to pleasant touch: an EEG investigation of tactile caressing

Somatosensation as a proximal sense can have a strong impact on our attitude toward physical objects and other human beings. However, relatively little is known about how hedonic valence of touch is processed at the cortical level. Here we investigated the electrophysiological correlates of affective tactile sensation during caressing of the right forearm with pleasant and unpleasant textile fabrics. We show dissociation between more physically driven differential brain responses to the different fabrics in early somatosensory cortex - the well-known mu-suppression (10-20 Hz) - and a beta-band response (25-30 Hz) in presumably higher-order somatosensory areas in the right hemisphere that correlated well with the subjective valence of tactile caressing. Importantly, when using single trial classification techniques, beta-power significantly distinguished between pleasant and unpleasant stimulation on a single trial basis with high accuracy. Our results therefore suggest a dissociation of the sensory and affective aspects of touch in the somatosensory system and may provide features that may be used for single trial decoding of affective mental states from simple electroencephalographic measurements

Feedback Control as a Framework for Understanding Tradeoffs in Biology

Control theory arose from a need to control synthetic systems. From regulating steam engines to tuning radios to devices capable of autonomous movement, it provided a formal mathematical basis for understanding the role of feedback in the stability (or change) of dynamical systems. It provides a framework for understanding any system with feedback regulation, including biological ones such as regulatory gene networks, cellular metabolic systems, sensorimotor dynamics of moving animals, and even ecological or evolutionary dynamics of organisms and populations. Here we focus on four case studies of the sensorimotor dynamics of animals, each of which involves the application of principles from control theory to probe stability and feedback in an organism's response to perturbations. We use examples from aquatic (electric fish station keeping and jamming avoidance), terrestrial (cockroach wall following) and aerial environments (flight control in moths) to highlight how one can use control theory to understand how feedback mechanisms interact with the physical dynamics of animals to determine their stability and response to sensory inputs and perturbations. Each case study is cast as a control problem with sensory input, neural processing, and motor dynamics, the output of which feeds back to the sensory inputs. Collectively, the interaction of these systems in a closed loop determines the behavior of the entire system.Comment: Submitted to Integr Comp Bio

GRAINS: Proximity Sensing of Objects in Granular Materials

Proximity sensing detects an object's presence without contact. However, research has rarely explored proximity sensing in granular materials (GM) due to GM's lack of visual and complex properties. In this paper, we propose a granular-material-embedded autonomous proximity sensing system (GRAINS) based on three granular phenomena (fluidization, jamming, and failure wedge zone). GRAINS can automatically sense buried objects beneath GM in real-time manner (at least ~20 hertz) and perceive them 0.5 ~ 7 centimeters ahead in different granules without the use of vision or touch. We introduce a new spiral trajectory for the probe raking in GM, combining linear and circular motions, inspired by a common granular fluidization technique. Based on the observation of force-raising when granular jamming occurs in the failure wedge zone in front of the probe during its raking, we employ Gaussian process regression to constantly learn and predict the force patterns and detect the force anomaly resulting from granular jamming to identify the proximity sensing of buried objects. Finally, we apply GRAINS to a Bayesian-optimization-algorithm-guided exploration strategy to successfully localize underground objects and outline their distribution using proximity sensing without contact or digging. This work offers a simple yet reliable method with potential for safe operation in building habitation infrastructure on an alien planet without human intervention.Comment: 35 pages, 5 figures,2 tables. Videos available at https://sites.google.com/view/grains2/hom

SI-Lab Annual Research Report 2020

The Signal & Images Laboratory (http://si.isti.cnr.it/) is an interdisciplinary research group in computer vision, signal analysis, smart vision systems and multimedia data understanding. It is part of the Institute for Information Science and Technologies of the National Research Council of Italy. This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2020

Of Priors and Particles: Structured and Distributed Approaches to Robot Perception and Control

Applications of robotic systems have expanded significantly in their scope, moving beyond the caged predictability of industrial automation and towards more open, unstructured environments. These agents must learn to reliably perceive their surroundings, efficiently integrate new information and quickly adapt to dynamic perturbations. To accomplish this, we require solutions which can effectively incorporate prior knowledge while maintaining the generality of learned representations. These systems must also contend with uncertainty in both their perception of the world and in predicting possible future outcomes. Efficient methods for probabilistic inference are then key to realizing robust, adaptive behavior. This thesis will first examine data-driven approaches for learning and combining perceptual models for both visual and tactile sensor modalities, common in robotics. Modern variational inference methods will then be examined in the context of online optimization and stochastic optimal control. Specifically, this thesis will contribute (1) data-driven visual and tactile perceptual models leveraging kinematic and dynamic priors, (2) a framework for joint inference with visuo-tactile sensing, (3) a family of particle-based, variational model predictive control and planning algorithms, and (4) a distributed inference scheme for online model adaptation.Ph.D