Transition from an anti-phase error-correction-mode to a synchronization mode in the mutual hand tracking

Proactive motion in hand tracking and in finger bending, in which the body motion occurs prior to the reference signal, was reported by the preceding researchers when the target signals were shown to the subjects at relatively high speed or high frequencies. These phenomena indicate that the human sensory-motor system tends to choose an anticipatory mode rather than a reactive mode, when the target motion is relatively fast. The present research was undertaken to study what kind of mode appears in the sensory-motor system when two persons were asked to track the hand position of the partner with each other at various mean tracking frequency. The experimental results showed a transition from a mutual error-correction mode to a synchronization mode occurred in the same region of the tracking frequency with that of the transition from a reactive error-correction mode to a proactive anticipatory mode in the mechanical target tracking experiments. Present research indicated that synchronization of body motion occurred only when both of the pair subjects operated in a proactive anticipatory mode. We also presented mathematical models to explain the behavior of the error-correction mode and the synchronization mode

Mechanism for synchronized motion between two humans in mutual tapping experiments: Transition from alternative mode to synchronization mode

We performed mutual tapping experiments between two humans to investigate the conditions required for synchronized motion. A transition from an alternative mode to a synchronization mode was discovered under the same conditions when a subject changed from a reactive mode to an anticipation mode in single tapping experiments. Experimental results suggest that the cycle time for each tapping motion is tuned by a proportional control that is based on synchronization errors and cycle time errors. As the tapping frequency increases, the mathematical model based on the feedback control in the sensory-motor closed loop predicts a discrete mode transition as the gain factors of the proportional control decease. The conditions of the synchronization were shown as a consequence of the coupled dynamics based on the subsequent feedback loop in the sensory-motor system

Simultaneous coupling of phototaxis and electrotaxis in Volvox algae

In nature, living creatures are affected by several stimuli simultaneously. The response of living creatures to stimuli is called taxis. In order to reveal the principles of taxis behavior in response to complex stimuli, we simultaneously applied photostimulation and electric stimulation perpendicularly to a Volvox algae solution. The probability distribution of the swimming direction showed that a large population of swimming cells moved in a direction that was the result of the composition of phototaxis and electrotaxis. More surprisingly, we uncovered the coupling of signs of taxis, i.e., coupling of phototaxis and electrotaxis induced positive electrotaxis, which did not emerge in the single stimulation experiments. We qualitatively explained the coupling of taxis based on the polarization of the swimming cells induced by the simultaneous photo- and electric stimulation

Embedded fuzzy logic controller for positive and negative pressure control in pneumatic soft robots

A key challenge in soft robotics is controlling the large deformation experienced as a result of high compliance nature of soft robots. In this work, a software control strategy for regulating the amount of internal positive and negative air pressure inside pneumatic soft robots is presented. Since the air pressure has a direct effect on the amount of deformation, the position of the robot is controlled. Pressure control was implemented with a fuzzy logic controller, which is described with its performance shown. The approach can be integrated into any specified soft robotic actuator requiring pneumatic actuation e.g. bending, triangular and muscle actuators

Anticipation from sensation: using anticipating synchronisation to stabilise a system with inherent sensory delay

We present a novel way of using a dynamical model for predictive tracking control that can adapt to a wide range of delays without parameter update. This is achieved by incorporating the paradigm of anticipating synchronisation (AS), where a `slave' system predicts a `master' via delayed self-feedback. By treating the delayed output of the plant as one half of a `sensory' AS coupling, the plant and an internal dynamical model can be synchronised such that the plant consistently leads the target's motion. We use two simulated robotic systems with differing arrangements of the plant and internal model (`parallel' and `serial') to demonstrate that this form of control adapts to a wide range of delays without requiring the parameters of the controller to be changed

A study of anticipatory non-autonomous systems

Rhythms are manifested ubiquitously in dynamical biological processes. These fundamental processes which are necessary for the survival of living organisms include metabolism, breathing, heart beat, and, above all, the circadian rhythm coupled to the diurnal cycle. Thus, in mathematical biology, biological processes are often represented as linear or nonlinear oscillators. In the framework of nonlinear and dissipative systems (ie. the flow of energy, substances, or sensory information), they generate stable internal oscillations as a response to environmental input and, in turn, utilise such output as a means of coupling with the environment

Estimation of brain dynamics under visuomotor task using functional connectivity analysis based on graph theory

Network studies of brain connectivity have demonstrated that the highly connected area, or hub, is a vital feature of human functional and structural brain organization. Hubs identify which region plays an important role in cognitive/sensorimotor tasks. In addition, a complex visuomotor learning skill causes specific changes of neuronal activation across brain regions. Accordingly, this study utilizes the hub as one of the features to map the visuomotor learning tasks and their dynamic functional connectivity (dFC). The electroencephalogram (EEG) data recorded under three different behavior conditions were investigated: motion only (MO), vision only (VO), and tracking (Tra) conditions. Here, we used the phase locking value (PLV) with a sliding window (50 ms) to calculate the dFC at four distinct frequency bands: 8-12 Hz (alpha), 18-22 Hz (low beta), 26-30 Hz (high beta) and 38-42 Hz (gamma), and the eigenvector centrality to evaluate the hub identification. The Gaussian Mixture Model (GMM) was applied to investigate the dFC patterns. The results showed that the dFC patterns with the hub feature represent the characteristic of neuronal activities under visuomotor coordination

Milliseconds matter: temporal order of visuo-tactile stimulation affects the ownership of a virtual hand

The sense of body ownership, that one’s body belongs to oneself, is a result of the integration of different sensory streams. This sense however is not error-free; in 1998 Botvinick and Cohen [3] showed the rubber hand illusion (RHI), an illusion that made a subject feel a rubber hand as their own. An important factor to induce the illusion is the timing of the applied visual and tactile stimulation to the rubber hand. Temporal delays greater than 500 ms eliminate the illusory ownership. This study investigates previously unexplored small delays between stimulation modalities and their effect for the perception of the RHI. Through a virtual reality setup of the RHI paradigm, it is shown that small delays can significantly alter the strength of the illusion. The order of the sensory modality presented plays a catalytic role to whether or not the inter-modal delay will have an effect on the illusion’s strength

Evaluation of Word Representations in Grounding Natural Language Instructions through Computational Human-Robot Interaction

International audienceIn order to interact with people in a natural way, a robot must be able to link words to objects and actions. Although previous studies in the literature have investigated grounding, they did not consider grounding of unknown synonyms. In this paper, we introduce a probabilistic model for grounding unknown synonymous object and action names using cross-situational learning. The proposed Bayesian learning model uses four different word representations to determine synonymous words. Afterwards, they are grounded through geometric characteristics of objects and kinematic features of the robot joints during action execution. The proposed model is evaluated through an interaction experiment between a human tutor and HSR robot. The results show that semantic and syntactic information enable grounding of unknown synonyms and that the combination of both achieves the best grounding

A compact low-cost electronic hardware design for actuating soft robots

A low cost, compact embedded design approach for actuating soft robots is presented. The complete fabrication procedure and mode of operation was demonstrated, and the performance of the complete system was also demonstrated by building a microcontroller based hardware system which was used to actuate a soft robot for bending motion. The actuation system including the electronic circuit board and actuation components was embedded in a 3D-printed casing to ensure a compact approach for actuating soft robots. Results show the viability of the system in actuating and controlling siliconebased soft robots to achieve bending motions. Qualitative measurements of uniaxial tensile test, bending distance and pressure were obtained. This electronic design is easy to reproduce and integrate into any specified soft robotic device requiring pneumatic actuation