Search

Tactile sensing can provide access to information about the contact (i.e. slippage, surface feature, friction), which is out of reach of vision but crucial for manipulation. To access this information, a dense measurement of the deformation of soft fingertips is necessary. Recently, tactile sensors that rely on a camera looking at a deformable membrane have demonstrated that a dense measurement of the contact is possible. However, their manufacturing can be time-consuming and labor-intensive. Here, we show a new design method that uses multi-color additive manufacturing and silicone casting to efficiently manufacture soft marker-based tactile sensors that are able to capture with high-resolution the three-dimensional deformation field at the interface. Each marker is composed of two superimposed color filters. The subtractive color mixing encodes the normal deformation of the membrane, and the lateral deformation is found by centroid detection. With this manufacturing method, we can reach a density of 400 markers on a 21 mm radius hemisphere, allowing for regular and dense measurement of the deformation. We calibrated and validated the approach by finding the curvature of objects with a threefold increase in accuracy as compared to previous implementations. The results demonstrate a simple yet effective approach to manufacturing artificial fingertips for capturing a rich image of the tactile interaction at the location of contact.

Haptic feedback is a desired feature in teleoperation as it can improve dexterous manipulation. Direct force feedback to the operator’s hand and fingers requires complex hardware and therefore substituting force by for instance vibration is a relevant topic. In this experiment, we tested performance on a Box & Blocks task in a teleoperation set-up with no feedback, direct force feedback and substituted vibration feedback. Objective performance was the same in all conditions as was the learning effect over three sessions, but participants had a clear preference for haptic feedback over no haptic feedback. The preferred type of feedback (force or vibration or both) varied over participants. In general, this study showed that haptic feedback is preferred in teleoperation, the Box & Blocks task seems not sensitive enough for our (and most) current teleoperation set-up(s), and vibration feedback as substitute for direct force feedback works well and can be used intuitively.

Mediated Social Touch (MST) promises interpersonal touch over a distance through haptic or tactile displays. Tests of the efficacy of MST often involve attempts to demonstrate that effects of social touch (e.g., on affective responses or helping behavior) can be replicated with MST. Results, however, have been mixed. One possible explanation is that contextual factors have not sufficiently been taken into account in these experiments. A touch act is accompanied by other verbal and non-verbal expressions, and whom we touch, when, and in what manner is regulated through social and personal norms. Previous research demonstrated, amongst others, effects of gender and the facial expression of the toucher on the recipients’ touch experience. People can use expressions of the toucher’s emotions as a cue to anticipate the meaning of the ensuing social touch. This current study examines whether emotions expressed in text (i.e., textual tone) affects the meaning and experience of MST. As expected we found textual tone to affect both the comfortableness of the touch as well as its perceived meaning. Limitations and implications are discussed.KeywordsMediated social touch Textual tone Affective haptic devices Computer mediated communication Haptic feedback

In this study, we investigated the accuracy and precision by which vibrotactile directions on the back can be perceived. All direction stimuli consisted of two successive vibrations, the first one always on a centre point on the spine, the second in one of 12 directions equally distributed over a circle. Twelve participants were presented with 144 vibrotactile directions. They were required to match the perceived direction with an arrow they could see and feel on a frontoparallel plane. The results show a clear oblique effect: performance in terms of both precision and accuracy was better with the cardinal directions than with the oblique ones. The results partly reproduce an anisotropy in perceived vertical and horizontal distances observed in other studies.

We evaluated the convergent validity of the new language-independent EmojiGrid rating tool for the affective appraisal of perceived touch events. The EmojiGrid is a rectangular response grid, labeled with facial icons (emoji) that express different degrees of valence and arousal. We previously showed that participants can intuitively and reliably report their affective appraisal of different sensory stimuli (e.g., images, sounds, smells) by clicking on the EmojiGrid, without additional verbal instructions. However, because touch events can be bidirectional and are a dynamic expression of action, we cannot generalize previous results to the touch domain. In this study, participants (N = 65) used the EmojiGrid to report their own emotions when looking at video clips showing different touch events. The video clips were part of a validated database that provided corresponding normative ratings (obtained with a 9-point SAM scale) for each clip. The affective ratings for inter-human touch obtained with the EmojiGrid show excellent agreement with the data provided in the literature (intraclass correlations of .99 for valence and .79 for arousal). For object touch events, these values are .81 and .18, respectively. This may indicate that the EmojiGrid is more sensitive to perspective (sender versus receiver) than classic tools. Also, the relation between valence and arousal shows the classic U-shape at the group level. Thus, the EmojiGrid appears to be a valid graphical self-report instrument for the affective appraisal of perceived touch events, especially for inter-human touch.

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.