The Two Faces of Innovation Adoption: How Envy Affects Consumers' Evaluation of Innovative Products

Employing a dual-process model, four experiments demonstrate that when consumers experience envy, those who are more inclined to attend to their feelings (vs. cognition) are driven by a self-enhancement (vs. self-protection) motive. Accordingly, these envious consumers are more likely to exhibit positive (vs. negative) attitudes toward innovation adoption. [to cite]

Nonverbal Social Behavior Generation for Social Robots Using End-to-End Learning

To provide effective and enjoyable human-robot interaction, it is important for social robots to exhibit nonverbal behaviors, such as a handshake or a hug. However, the traditional approach of reproducing pre-coded motions allows users to easily predict the reaction of the robot, giving the impression that the robot is a machine rather than a real agent. Therefore, we propose a neural network architecture based on the Seq2Seq model that learns social behaviors from human-human interactions in an end-to-end manner. We adopted a generative adversarial network to prevent invalid pose sequences from occurring when generating long-term behavior. To verify the proposed method, experiments were performed using the humanoid robot Pepper in a simulated environment. Because it is difficult to determine success or failure in social behavior generation, we propose new metrics to calculate the difference between the generated behavior and the ground-truth behavior. We used these metrics to show how different network architectural choices affect the performance of behavior generation, and we compared the performance of learning multiple behaviors and that of learning a single behavior. We expect that our proposed method can be used not only with home service robots, but also for guide robots, delivery robots, educational robots, and virtual robots, enabling the users to enjoy and effectively interact with the robots.Comment: 10 pages, 7 figures, 3 tables, submitted to the International Journal of Robotics Research (IJRR

Visually Grounding Instruction for History-Dependent Manipulation

This paper emphasizes the importance of robot's ability to refer its task history, when it executes a series of pick-and-place manipulations by following text instructions given one by one. The advantage of referring the manipulation history can be categorized into two folds: (1) the instructions omitting details or using co-referential expressions can be interpreted, and (2) the visual information of objects occluded by previous manipulations can be inferred. For this challenge, we introduce the task of history-dependent manipulation which is to visually ground a series of text instructions for proper manipulations depending on the task history. We also suggest a relevant dataset and a methodology based on the deep neural network, and show that our network trained with a synthetic dataset can be applied to the real world based on images transferred into synthetic-style based on the CycleGAN.Comment: 8 pages, 6 figure

Like a Second Skin: Understanding How Epidermal Devices Affect Human Tactile Perception

The emerging class of epidermal devices opens up new opportunities for skin-based sensing, computing, and interaction. Future design of these devices requires an understanding of how skin-worn devices affect the natural tactile perception. In this study, we approach this research challenge by proposing a novel classification system for epidermal devices based on flexural rigidity and by testing advanced adhesive materials, including tattoo paper and thin films of poly (dimethylsiloxane) (PDMS). We report on the results of three psychophysical experiments that investigated the effect of epidermal devices of different rigidity on passive and active tactile perception. We analyzed human tactile sensitivity thresholds, two-point discrimination thresholds, and roughness discrimination abilities on three different body locations (fingertip, hand, forearm). Generally, a correlation was found between device rigidity and tactile sensitivity thresholds as well as roughness discrimination ability. Surprisingly, thin epidermal devices based on PDMS with a hundred times the rigidity of commonly used tattoo paper resulted in comparable levels of tactile acuity. The material offers the benefit of increased robustness against wear and the option to re-use the device. Based on our findings, we derive design recommendations for epidermal devices that combine tactile perception with device robustness