Conditions for Viewpoint Dependent Face Recognition

Poggio and Vetter (1992) showed that learning one view of a bilaterally symmetric object could be sufficient for its recognition, if this view allows the computation of a symmetric, "virtual," view. Faces are roughly bilaterally symmetric objects. Learning a side-view--which always has a symmetric view--should allow for better generalization performances than learning the frontal view. Two psychophysical experiments tested these predictions. Stimuli were views of shaded 3D models of laser-scanned faces. The first experiment tested whether a particular view of a face was canonical. The second experiment tested which single views of a face give rise to best generalization performances. The results were compatible with the symmetry hypothesis: Learning a side view allowed better generalization performances than learning the frontal view

Application of a differentiator-based adaptive super-twisting controller for a redundant cable-driven parallel robot

In this paper we present preliminary, experimental results of an Adaptive Super-Twisting Sliding-Mode Controller with time-varying gains for redundant Cable-Driven Parallel Robots. The sliding-mode controller is paired with a feed-forward action based on dynamics inversion. An exact sliding-mode differentiator is implemented to retrieve the velocity of the end-effector using only encoder measurements with the properties of finite-time convergence, robustness against perturbations and noise filtering. The platform used to validate the controller is a robot with eight cables and six degrees of freedom powered by 940 W compact servo drives. The proposed experiment demonstrates the performance of the controller, finite-time convergence and robustness in tracking a trajectory while subject to external disturbances up to approximately 400% the mass of the end-effector

Semi-Autonomous trajectory generation for mobile robots with integral haptic shared control

A new framework for semi-Autonomous path planning for mobile robots that extends the classical paradigm of bilateral shared control is presented. The path is represented as a B-spline and the human operator can modify its shape by controlling the motion of a finite number of control points. An autonomous algorithm corrects in real time the human directives in order to facilitate path tracking for the mobile robot and ensures i) collision avoidance, ii) path regularity, and iii) attraction to nearby points of interest. A haptic feedback algorithm processes both human's and autonomous control terms, and their integrals, to provide an information of the mismatch between the path specified by the operator and the one corrected by the autonomous algorithm. The framework is validated with extensive experiments using a quadrotor UAV and a human in the loop with two haptic interfaces

Effects of anxiety and cognitive load on instrument scanning behavior in a flight simulation

Previous research has rarely examined the combined influence of anxiety and cognitive load on gaze behavior and performance whilst undertaking complex perceptual-motor tasks. In the current study, participants performed an aviation instrument landing task in neutral and anxiety conditions, while performing a low or high cognitive load auditory n-back task. Both self-reported anxiety and heart rate increased from neutral conditions indicating that anxiety was successfully manipulated. Response accuracy and reaction time for the auditory task indicated that cognitive load was also successfully manipulated. Cognitive load negatively impacted flight performance and the frequency of gaze transitions between areas of interest. Performance was maintained in anxious conditions, with a concomitant decrease in n-back reaction time suggesting that this was due to an increase in mental effort. Analyses of individual responses to the anxiety manipulation revealed that changes in anxiety levels from neutral to anxiety conditions were positively correlated with changes in visual scanning entropy, which is a measure of the randomness of gaze behavior, but only when cognitive load was high. This finding lends support for an interactive effect of cognitive anxiety and cognitive load on attentional control

Modeling and control of UAV bearing formations with bilateral high-level steering

In this paper we address the problem of controlling the motion of a group of unmanned aerial vehicles (UAVs) bound to keep a formation defined in terms of only relative angles (i.e. a bearing formation). This problem can naturally arise within the context of several multi-robot applications such as, e.g. exploration, coverage, and surveillance. First, we introduce and thoroughly analyze the concept and properties of bearing formations, and provide a class of minimally linear sets of bearings sufficient to uniquely define such formations. We then propose a bearing-only formation controller requiring only bearing measurements, converging almost globally, and maintaining bounded inter-agent distances despite the lack of direct metric information.The controller still leaves the possibility of imposing group motions tangent to the current bearing formation. These can be either autonomously chosen by the robots because of any additional task (e.g. exploration), or exploited by an assisting human co-operator. For this latter 'human-in-the-loop' case, we propose a multi-master/multi-slave bilateral shared control system providing the co-operator with some suitable force cues informative of the UAV performance. The proposed theoretical framework is extensively validated by means of simulations and experiments with quadrotor UAVs equipped with onboard cameras. Practical limitations, e.g. limited field-of-view, are also considered. © The Author(s) 2012

A novel framework for closed-loop robotic motion simulation - Part II: motion cueing design and experimental validation

This paper, divided in two Parts, considers the problem of realizing a 6-DOF closed-loop motion simulator by exploiting an anthropomorphic serial manipulator as motion platform. After having proposed a suitable inverse kinematics scheme in Part I [1], we address here the other key issue, i.e., devising a motion cueing algorithm tailored to the specific robot motion envelope. An extension of the well-known classical washout filter designed in cylindrical coordinates will provide an effective solution to this problem. The paper will then present a thorough experimental evaluation of the overall architecture (inverse kinematics + motion cueing) on the chosen scenario: closed-loop simulation of a Formula 1 racing car. This will prove the feasibility of our approach in fully exploiting the robot motion capabilities as a motion simulator

Methods for Multiloop Identification of Visual and Neuromuscular Pilot Responses

In this paper, identification methods are proposed to estimate the neuromuscular and visual responses of a multiloop pilot model. A conventional and widely used technique for simultaneous identification of the neuromuscular and visual systems makes use of cross-spectral density estimates. This paper shows that this technique requires a specific noninterference hypothesis, often implicitly assumed, that may be difficult to meet during actual experimental designs. A mathematical justification of the necessity of the noninterference hypothesis is given. Furthermore, two methods are proposed that do not have the same limitations. The first method is based on autoregressive models with exogenous inputs, whereas the second one combines cross-spectral estimators with interpolation in the frequency domain. The two identification methods are validated by offline simulations and contrasted to the classic method. The results reveal that the classic method fails when the noninterference hypothesis is not fulfilled; on the contrary, the two proposed techniques give reliable estimates. Finally, the three identification methods are applied to experimental data from a closed-loop control task with pilots. The two proposed techniques give comparable estimates, different from those obtained by the classic method. The differences match those found with the simulations. Thus, the two identification methods provide a good alternative to the classic method and make it possible to simultaneously estimate human's neuromuscular and visual responses in cases where the classic method fails

Phenomenal competition for poses of the human head

Abstract. We show a cylindrical projection of the human head. This projection is ambiguous with respect to head pose. Viewing such a projection produces perceptual competition for a few discrete views. In a number of studies it is suggested that the brain may represent head pose in terms of a discrete set of preferred views. Exactly what these views are and how their representations enable visual face recognition and pose estimation is not entirely clear. On the one hand, it is easier to find neurons in the primate inferotemporal cortex that are more selective for head-on, profile, or back views than other angles (Perrett et al 1991). On the other hand, psychophysical studies have shown that human face recognition generalizes better from a learned view near 45° about the vertical axis than from other views (Bruce and Valentine 1987; Troje and BulthofF, in press). This latter observation is consistent with theoretical predictions based on virtual views for symmetric objects (Vetter et al 1993). In either case, one might expect that if an image of a human head is presented in such a way as to make pose assignment ambiguous, we might visually experience a competition for preferred poses