Describes a psychophysical experiment designed to study the phenomenon of illusion which occurs with pseudo-haptic feedback, and to identify the moment when this illusion occurs: the "boundary of illusion". The subjects were given the task of deciding which of two virtual springs is the stiffer, these springs being simulated with a PHANToM/sup TM/ force feedback device and displayed on a monoscopic computer screen. The first spring had a realistic behavior, since its visual and haptic displacements were identical. The second spring-the pseudo-haptic one-was stiffer on a haptic basis, but sometimes less stiff on a visual basis. The data collected allowed us to calculate the visual point of subjective equality (PSE) between the two springs, which represents the boundary of the sensory illusion. On average, a high value of PSE turned out to be -24%. This value increased monotonically when the haptic difference between the springs increased. This implies that more visual deformation is necessary to compensate for large haptic differences and qualifies the notion of visual dominance. However, this boundary varies greatly depending on the subjects and their sensory integration strategy. The subjects were sensitive to this illusion to varying degrees. They were divided into different populations-from those who were "haptically oriented" to those who were "visually oriented".

This paper considers whether a passive isometric input device, such as a Spaceball/sup TM/, used together with visual feedback, could provide the operator with a pseudo-haptic feedback. For this aim, two psychophysical experiments have been conducted. The first experiment consisted of a compliance discrimination, between two virtual springs hand-operated by means of the Spaceball/sup TM/. In this experiment, the stiffness (or compliance) JND turned out to be 6%. The second experiment assessed stiffness discrimination between a virtual spring and the equivalent spring in reality. In this case, the stiffness (or compliance) JND was found to be 13.4%. These results are consistent with previous outcomes on manual discrimination of compliance. Consequently, this consistency reveals that the passive apparatus that was used can, to some extent, simulate haptic information. In addition, a final test indicated that the proprioceptive sense of the subjects was blurred by visual feedback. This gave them the illusion of using a nonisometric device.

While active work is being achieved in order to conceive very transparent, stable and user-friendly human-machine haptic interfaces, few works address the human factors and performance evaluation of haptic devices while being used in an actual operator interaction with a virtual environment. The article addresses the above aspects.

Teleoperators using an intermediate functional representation of the remote real environment (RE), suffer the lack of accurate synthetic modeling. Discrepancies will always occur between the RE and its artificial representation by means of virtual environment (VE). A strategy to deal with VE/RE uncertainties based mainly on sensory level interpretation is presented. It is directed to avoid the use of task knowledge by providing a stream of virtual sensors values. Within the remote site, a supervisor is in charge of recovering from the VE/RE discrepancies by a continuous simulated and real states comparison. The states are derived from simulated and real sensor interpretations respectively. A simple experiment with the proposed approach is presented using only position (velocity) and force sensors. Limitations of the proposed approach are also discussed.

This paper focuses on the problem of force-feedback for the human-operator hand when manipulating virtual objects. We propose a method for the computation of feedback-forces that have to be applied on each individual phalanx and finger of the human hand in order to display pertinent, kinesthetic information about static or dynamic characteristics of objects present in the virtual scene. External forces and moments of the manipulated virtual objects heave to be mapped on the contact-forces space of the virtual grasp. The method is based on the solution of a nonlinear programming problem, formulated by performing a static analysis of a general, multiple contact points virtual grasp. A methodology for modelling interactions within a virtual environment, and performing realistic grasping and manipulation, is also presented.

This paper discusses the development of new teleoperator systems. While many innovations during the last decade made teleoperation technology progress, some severe well known lacks that we enumerate still persist. With respect to some attractive solutions proposed for coping with these problems we designed a bilateral control scheme based on what we called the hidden robot concept. The teleoperator achieves tasks manually in a natural way within a virtual environment (VE). Thanks to suitable bilateral transformations, the virtual tasks are being reproduced by any slave robot within the remote site. Mainly task based, our approach is not considered like a high level task knowledge based control. Rather, we consider it like a more refined shared autonomy control with a high level abstraction interface. Three main components are developed: (i) supervision loop, (ii) bilateral transformation layer, (iii) execution loop. The approach has been validated experimentally and preliminary results as well as further work are discussed.

Virtual reality (VR) involves multimodal interactions with computer‐simulated worlds through visual, auditory, and haptic feedback. This article reviews the state of the art in special‐purpose input‐output devices, such as trackers, sensing gloves, 3‐D audio cards, stereo displays, and haptic feedback masters. The integration of these devices in local and network‐distributed VR simulation systems is subsequently discussed. Finally, we present human‐factor studies that quantify the benefits of several feedback modalities on simulation realism and sensorial immersion. Specifically, we consider tracking and dextrous manipulation task performance in terms of error rates and learning times when graphics, audio, and haptic feedback are provided.