Bimanual Integration of Position and Curvature in Haptic Perception

Panday, Virjanand; Tiest, Wouter M. Bergmann; Kappers, Astrid M. L.

doi:10.1109/toh.2013.8

Cited by 7 publications

(4 citation statements)

References 20 publications

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“…On the motor coordination side, it has been shown that there is not only tight coupling between the movements of paired limbs (34)(35)(36)(37), but also peripheral (38) and central (39) activation of the opposite limb. On the perceptual side, some studies have shown at least partial combination of signals across the two hands (45,46), whereas others (47, 48) have found no evidence for optimal signal combination (46). Here we have demonstrated a truly sensorimotor form of interlimb coordination, in which motor or kinesthestic signals from one limb are combined with sensory signals from the other limb into one unified haptic percept.…”

Section: Discussionmentioning

confidence: 51%

Direct coupling of haptic signals between hands

Dupin

Hayward

Wexler

2014

Proc. Natl. Acad. Sci. U.S.A.

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Although motor actions can profoundly affect the perceptual interpretation of sensory inputs, it is not known whether the combination of sensory and movement signals occurs only for sensory surfaces undergoing movement or whether it is a more general phenomenon. In the haptic modality, the independent movement of multiple sensory surfaces poses a challenge to the nervous system when combining the tactile and kinesthetic signals into a coherent percept. When exploring a stationary object, the tactile and kinesthetic signals come from the same hand. Here we probe the internal structure of haptic combination by directing the two signal streams to separate hands: one hand moves but receives no tactile stimulation, while the other hand feels the consequences of the first hand's movement but remains still. We find that both discrete and continuous tactile and kinesthetic signals are combined as if they came from the same hand. This combination proceeds by direct coupling or transfer of the kinesthetic signal from the moving to the feeling hand, rather than assuming the displacement of a mediating object. The combination of signals is due to perception rather than inference, because a small temporal offset between the signals significantly degrades performance. These results suggest that the brain simplifies the complex coordinate transformation task of remapping sensory inputs to take into account the movements of multiple body parts in haptic perception, and they show that the effects of action are not limited to moving sensors.touch | haptics | perception | sensorimotor integration | kinesthesis M otor and kinesthetic signals arising from the movement of the eyes in the head, and translation of the eyes and ears in space due to head and body movements, have been shown to play an important role in visual (1-3) and auditory (4-6) perception. However, because of the small number of sensory surfaces in these modalities, and the rigid constraints on their movement, the number of kinesthetic degrees of freedom is limited. In active touch or the haptic modality (7-13), the large number of sensory surfaces, and the nearly unlimited ways these surfaces can move, lead to the question of how movement can be represented and associated with the cutaneous or tactile signals. To study how tactile and kinesthetic cues are combined to haptically perceive object shape and size, we created a novel haptic stimulus in which these cues were completely dissociated. This stimulus consisted of simulated triangles felt through a narrow slit, as in anorthoscopic perception in vision (14-16) or haptic perception (17), and as illustrated in Fig.

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Section: Discussionmentioning

confidence: 51%

Direct coupling of haptic signals between hands

Dupin

Hayward

Wexler

2014

Proc. Natl. Acad. Sci. U.S.A.

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“…The most famous example is the experiment in which a study participant was asked to light up a match with anesthetized fingers [4] and encounters extreme difficulties doing so. Unlike humans, who, after years of developmental process [15], seamlessly handle a complex mixture of haptic features [9] and profit from their integration over space and time [6], even the most advanced robots are strongly constrained in performing contact-rich interaction tasks [2]. This is due to several reasons.…”

Section: Introductionmentioning

confidence: 99%

Attention-Based Robot Learning of Haptic Interaction

Moringen

Fleer

Walck

et al. 2020

Haptics: Science, Technology, Applications

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Haptic interaction involved in almost any physical interaction with the environment performed by humans is a highly sophisticated and to a large extent a computationally unmodelled process. Unlike humans, who seamlessly handle a complex mixture of haptic features and profit from their integration over space and time, even the most advanced robots are strongly constrained in performing contact-rich interaction tasks. In this work we approach the described problem by demonstrating the success of our online haptic interaction learning approach on an example task: haptic identification of four unknown objects. Building upon our previous work performed with a floating haptic sensor array, here we show functionality of our approach within a fully-fledged robot simulation. To this end, we utilize the haptic attention model (HAM), a meta-controller neural network architecture trained with reinforcement learning. HAM is able to learn to optimally parameterize a sequence of so-called haptic glances, primitive actions of haptic control derived from elementary human haptic interaction. By coupling a simulated KUKA robot arm with the haptic attention model, we pursue to mimic the functionality of a finger. Our modeling strategy allowed us to arrive at a tactile reinforcement learning architecture and characterize some of its advantages. Owing to a rudimentary experimental setting and an easy acquisition of simulated data, we believe our approach to be particularly useful for both time-efficient robot training and a flexible algorithm prototyping.

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“…Hence, meeting these demands and achieving realistic perception of shape, curvature, 18,19 hardness texture and friction characteristics of virtual objects 20–22 have long been an active area of research. In various studies, 23–25 it is even shown that people perceive the local shape of virtual surfaces (convex and concave regions) differently. This fact implies that haptic perception is in general a function of curvature and may be easily misjudged by people.…”

Section: Introductionmentioning

confidence: 99%

Haptic exploration of highly curved compliant surfaces

Zafer

Yilmaz

2015

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this article, a constraint-based controller is developed for two-manifold oriented viscoelastic surfaces that guarantees smooth and compliant haptic rendering. The approach is direct and uses a pseudo-inverse projection of the haptic device end effector onto the virtual surface. The rendering algorithm enables the user to freely move a soft fingertip model along a highly curved deformable surface and perceive surface roughness in the normal direction by feeling a nonlinear contact force. The normal force is governed by a physically based three-dimensional constitutive model, which accounts for both geometric and material nonlinearities. In experimental implementation, the soft fingertip model acts as the compliant haptic interface (robot) and the user manually guides the manipulator to explore the deformable surface properties in real time. The (nonlinear) reaction force normal to the surface is applied at the soft fingertip edge of the fingertip bone (distal phalanx). The robustness of the approach is illustrated by experimental data in rendering highly curved compliant surfaces.

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Bimanual Integration of Position and Curvature in Haptic Perception

Cited by 7 publications

References 20 publications

Direct coupling of haptic signals between hands

Direct coupling of haptic signals between hands

Attention-Based Robot Learning of Haptic Interaction

Haptic exploration of highly curved compliant surfaces

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