Editorial: Re-Enacting Sensorimotor Experience for Cognition

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A note on the depth-from-defocus mechanism of jumping spiders

Jumping spiders are capable of estimating the distance to their prey relying only on the information from one of their main eyes. Recently, it has been shown that jumping spiders perform this estimation based on image defocus cues. In order to gain insight into the mechanisms involved in this blur-to-distance mapping as performed by the spider and to judge whether inspirations can be drawn from spider vision for depth-from-defocus computer vision algorithms, we constructed a three-dimensional (3D) model of the anterior median eye of the Metaphidippus aeneolus, a well studied species of jumping spider. We were able to study images of the environment as the spider would see them and to measure the performances of a well known depth-from-defocus algorithm on this dataset. We found that the algorithm performs best when using images that are averaged over the considerable thickness of the spider's receptor layers, thus pointing towards a possible functional role of the receptor thickness for the spider's depth estimation capabilities

From Human to Robot Interactions: A Circular Approach towards Trustworthy Social Robots

Human trust research uncovered important catalysts for trust building between interaction partners such as appearance or cognitive factors. The introduction of robots into social interactions calls for a reevaluation of these findings and also brings new challenges and opportunities. In this paper, we suggest approaching trust research in a circular way by drawing from human trust findings, validating them and conceptualizing them for robots, and finally using the precise manipulability of robots to explore previously less-explored areas of trust formation to generate new hypotheses for trust building between agents.Comment: In SCRITA 2023 Workshop Proceedings (arXiv:2311.05401) held in conjunction with 32nd IEEE International Conference on Robot & Human Interactive Communication, 28/08 - 31/08 2023, Busan (Korea

Social reinforcement in artificial prelinguistic development : a study using intrinsically motivated exploration architectures

This work introduces an intrinsically motivated sensorimotor exploration architecture which considers social reinforcement and motor constraint awareness. The main objective is to study the influence of social interactions during artificial early prelinguistic development. We argue that this architecture contributes to explain development from voiceless to sequence of vowels vocalizations. A cognitive developmental perspective is considered emphasizing embodied cognition and sensorimotor exploratory behaviors. For a new-born agent, motor constraints are unknown. However, the agent is endowed with a somatosensory system that indicates if a motor configuration was reached or not. This information is used to model and predict constraint violations. Furthermore, the architecture considers imitative behaviors that constrain the search space during exploration. Interaction occurs when the learner sensory production is similar to a sensory unit relevant to communication. In that case, the instructor perceives this similitude and reformulates with the relevant sensory unit. When the learner perceives an utterance by the instructor, it attempts to imitate it. Two systems are considered for experimentation: A toy example and a simulated vocal tract. In general, our results suggest that constraint awareness and social reinforcement contribute to achieve less redundant exploration, lower exploration and evaluation errors, and a clearer picture of developmental transitions.© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Peer ReviewedPostprint (author's final draft

Bringing Together Robotics, Neuroscience, and Psychology: Lessons Learned From an Interdisciplinary Project

The diversified methodology and expertise of interdisciplinary research teams provide the opportunity to overcome the limited perspectives of individual disciplines. This is particularly true at the interface of Robotics, Neuroscience, and Psychology as the three fields have quite different perspectives and approaches to offer. Nonetheless, aligning backgrounds and interdisciplinary expectations can present challenges due to varied research cultures and practices. Overcoming these challenges stands at the beginning of each productive collaboration and thus is a mandatory step in cognitive neurorobotics. In this article, we share eight lessons that we learned from our ongoing interdisciplinary project on human-robot and robot-robot interaction in social settings. These lessons provide practical advice for scientists initiating interdisciplinary research endeavors. Our advice can help to avoid early problems and deal with differences between research fields, prepare for and anticipate challenges, align project expectations, and speed up research progress, thus promoting effective interdisciplinary research across Robotics, Neuroscience, and Psychology.Peer Reviewe