Affect Recognition in Hand-Object Interaction Using Object-Sensed Tactile and Kinematic Data

We investigate the recognition of the affective states of a person performing an action with an object, by processing the object-sensed data. We focus on sequences of basic actions such as grasping and rotating, which are constituents of daily-life interactions. iCube, a 5 cm cube, was used to collect tactile and kinematics data that consist of tactile maps (without information on the pressure applied to the surface), and rotations. We conduct two studies: classification of i) emotions and ii) the vitality forms. In both, the participants perform a semi-structured task composed of basic actions. For emotion recognition, 237 trials by 11 participants associated with anger, sadness, excitement, and gratitude were used to train models using 10 hand-crafted features. The classifier accuracy reaches up to 82.7%. Interestingly, the same classifier when learned exclusively with the tactile data performs on par with its counterpart modeled with all 10 features. For the second study, 1135 trials by 10 participants were used to classify two vitality forms. The best-performing model differentiated gentle actions from rude ones with an accuracy of 84.85%. The results also confirm that people touch objects differently when performing these basic actions with different affective states and attitudes

Making New "New AI" Friends : Designing a Social Robot for Diabetic Children from an Embodied AI Perspective

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.Robin is a cognitively and motivationally autonomous affective robot toddler with "robot diabetes" that we have developed to support perceived self-efficacy and emotional wellbeing in children with diabetes by providing them with positive mastery experiences of diabetes management in a playful but realistic and natural interaction context. Underlying the design of Robin is an "Embodied" (formerly also known as "New") Artificial Intelligence approach to robotics. In this paper we discuss the rationale behind the design of Robin to meet the needs of our intended end users (both children and medical staff), and how "New AI" provides a suitable approach to developing a friendly companion that fulfills the therapeutic and affective requirements of our end users beyond other approaches commonly used in assistive robotics and child-robot interaction. Finally, we discuss how our approach permitted our robot to interact with and provide suitable experiences of diabetes management to children with very different social interaction styles.Peer reviewedFinal Published versio

Optimal perceived timing: integrating sensory information with dynamically updated expectations

The environment has a temporal structure, and knowing when a stimulus will appear translates into increased perceptual performance. Here we investigated how the human brain exploits temporal regularity in stimulus sequences for perception. We find that the timing of stimuli that occasionally deviate from a regularly paced sequence is perceptually distorted. Stimuli presented earlier than expected are perceptually delayed, whereas stimuli presented on time and later than expected are perceptually accelerated. This result suggests that the brain regularizes slightly deviant stimuli with an asymmetry that leads to the perceptual acceleration of expected stimuli. We present a Bayesian model for the combination of dynamically-updated expectations, in the form of a priori probability of encountering future stimuli, with incoming sensory information. The asymmetries in the results are accounted for by the asymmetries in the distributions involved in the computational process