Playing back vibrotactile signals through actuators is commonly used to simulate tactile feelings of virtual textured surfaces. However, there is often a small mismatch between the simulated tactile feelings and intended tactile feelings by tactile designers. Thus, a method of modulating the vibrotactile perception is required. We focus on fine roughness perception and we propose a method using a pseudo-haptic effect to modulate fine roughness perception of vibrotactile texture. Specifically, we visually modify the pointer's position on the screen slightly, which indicates the touch position on textured surfaces. We hypothesized that if users receive vibrational feedback watching the pointer visually oscillating back forth and left right, users would believe the vibrotactile surfaces more uneven. We also hypothesized that as the size of visual oscillation is getting larger, the amount of modification of roughness perception of vibrotactile surfaces would be larger. We conducted user studies to test the hypotheses. Results of first user study suggested that users felt vibrotactile texture with our method rougher than they did without our method at a high probability. Results of second user study suggested that users felt different roughness for vibrational texture in response to the size of visual oscillation. These results confirmed our hypotheses and they suggested that our method was effective. Also, the same effect could potentially be applied to the visual movement of virtual hands or fingertips when users are interacting with virtual surfaces using their hands.
Cross-modal visuo-haptic interaction has been studied for a long time. Studies on it are based on the key idea that when visual and other senses conflict, vision often dominates in multisensory integration, so sensory input can be distorted in favor of vision. For example, in their classic experiment, Rock and Victor @cite_10 asked users to look at and touch an object. They created a conflict between vision and touch by distorting the visually perceived shape from the actual shape perceived by touch. As a result, users reported that the object felt the way it looked, suggesting that the conflict between vision and touch was completely resolved in favor of vision, and users were unaware of the conflict.
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Vision and touch are two of the important sensing modalities for humans and they offer complementary information for sensing the environment. Robots could also benefit from such multi-modal sensing ability. In this paper, addressing for the first time (to the best of our knowledge) texture recognition from tactile images and vision, we propose a new fusion method named Deep Maximum Covariance Analysis (DMCA) to learn a joint latent space for sharing features through vision and tactile sensing. The features of camera images and tactile data acquired from a GelSight sensor are learned by deep neural networks. But the learned features are of a high dimensionality and are redundant due to the differences between the two sensing modalities, which deteriorates the perception performance. To address this, the learned features are paired using maximum covariance analysis. Results of the algorithm on a newly collected dataset of paired visual and tactile data relating to cloth textures show that a good recognition performance of greater than 90 can be achieved by using the proposed DMCA framework. In addition, we find that the perception performance of either vision or tactile sensing can be improved by employing the shared representation space, compared to learning from unimodal data. Cross-modal audio-visual perception has been a long-lasting topic in psychology and neurology, and various studies have discovered strong correlations in human perception of auditory and visual stimuli. Despite works in computational multimodal modeling, the problem of cross-modal audio-visual generation has not been systematically studied in the literature. In this paper, we make the first attempt to solve this cross-modal generation problem leveraging the power of deep generative adversarial training. Specifically, we use conditional generative adversarial networks to achieve cross-modal audio-visual generation of musical performances. We explore different encoding methods for audio and visual signals, and work on two scenarios: instrument-oriented generation and pose-oriented generation. Being the first to explore this new problem, we compose two new datasets with pairs of images and sounds of musical performances of different instruments. Our experiments using both classification and human evaluations demonstrate that our model has the ability to generate one modality, i.e., audio visual, from the other modality, i.e., visual audio, to a good extent. Our experiments on various design choices along with the datasets will facilitate future research in this new problem space. In this work, we propose a framework to deal with cross-modal visuo-tactile object recognition. By cross-modal visuo-tactile object recognition, we mean that the object recognition algorithm is trained only with visual data and is able to recognize objects leveraging only tactile perception. The proposed cross-modal framework is constituted by three main elements. The first is a unified representation of visual and tactile data, which is suitable for cross-modal perception. The second is a set of features able to encode the chosen representation for classification applications. The third is a supervised learning algorithm, which takes advantage of the chosen descriptor. In order to show the results of our approach, we performed experiments with 15 objects common in domestic and industrial environments. Moreover, we compare the performance of the proposed framework with the performance of 10 humans in a simple cross-modal recognition task. One of the most disconcertingly unnatural properties of most virtual environments (VEs) is the ability of the user to pass through objects. I hypothesize that passive haptics, augmenting a high-fidelity visual virtual environment with low-fidelity physical objects, will markedly improve both sense of presence and spatial knowledge training transfer. The low-fidelity physical models can be constructed from cheap, easy-to-assemble materials such as styrofoam, plywood, and particle board. The first study investigated the effects of augmenting a visual-cliff environment with a slight physical ledge on participants' sense of presence. I found when participants experienced passive haptics in the VE, they exhibited significantly more behaviors associated with pit avoidance than when experiencing the non-augmented VE. Changes in heart rate and skin conductivity were significantly higher than when they experienced the VE without passive haptics. The second study investigated passive haptics' effects on performance of a real-world navigation task after training in a virtual environment. Half of the participants trained on maze navigation in a VE augmented with a styrofoam physical model, while half trained in a non-augmented VE but were given visual and audio contact cues. The task was to gain as much information as possible about the layout of the environment. Participants knew before the VE session that their training would be tested by navigating an identical real maze environment while blindfolded. Significant differences in the time to complete the blindfolded navigation task and significant differences in the number of collisions with objects were found between the participants trained in an augmented VE and the participants trained in a non-augmented VE. 11 of 15 participants trained without passive haptics bumped into the next-to-last obstacle encountered in the testing session and turned the wrong direction to navigate around it: only 2 of 15 participants trained with passive haptics made the same navigation error. On the other hand, the assessment of the participants' cognitive maps of the virtual environment did not find significant differences between groups as measured by sketch maps and object dimension estimation.