Notes

Low-temperature topotactic oxidation with all the solid-state oxidant Zr-doped CeO2.
To coordinate actions with an interaction partner requires a constant exchange of sensorimotor signals. Humans acquire these skills in infancy and early childhood mostly by imitation learning and active engagement with a skilled partner. They require the ability to predict and adapt to one's partner during an interaction. In this work we want to explore these ideas in a human-robot interaction setting in which a robot is required to learn interactive tasks from a combination of observational and kinesthetic learning. To this end, we propose a deep learning framework consisting of a number of components for (1) human and robot motion embedding, (2) motion prediction of the human partner, and (3) generation of robot joint trajectories matching the human motion. As long-term motion prediction methods often suffer from the problem of regression to the mean, our technical contribution here is a novel probabilistic latent variable model which does not predict in joint space but in latent space. To test the proposed method, we collect human-human interaction data and human-robot interaction data of four interactive tasks "hand-shake," "hand-wave," "parachute fist-bump," and "rocket fist-bump." We demonstrate experimentally the importance of predictive and adaptive components as well as low-level abstractions to successfully learn to imitate human behavior in interactive social tasks.Today, robots are studied and expected to be used in a range of social roles within classrooms. Yet, due to a number of limitations in social robots, robot interactions should be expected to occasionally suffer from troublesome situations and breakdowns. In this paper, we explore this issue by studying how children handle interaction trouble with a robot tutee in a classroom setting. The findings have implications not only for the design of robots, but also for evaluating their benefit in, and for, educational contexts. In this study, we conducted video analysis of children's group interactions with a robot tutee in a classroom setting, in order to explore the nature of these troubles in the wild. Within each group, children took turns acting as the primary interaction partner for the robot within the context of a mathematics game. Selleck ALW II-41-27 Specifically, we examined what types of situations constitute trouble in these child-robot interactions, the strategies that individual children employ to cope with this trouble, ahts on children's perspectives and expectations of social robots in classroom contexts.Niche construction is a process in which organisms modify the selection pressures on themselves and others through their ecological activities, and ecological inheritance is the consequence of niche construction inherited through generations. However, it is still unclear how such mutual interactions between robots or embodied agents and their physical environments can yield complex and divergent evolutionary processes or an open-ended evolution. Our purpose is to clarify what kind of complex and various niche-constructing behaviors evolve in a physically grounded environment under various conditions of ecological inheritance of constructed structures and spatial relationships. We focus on a predator-prey relationship, and constructed an evolutionary model in which a prey creature has to avoid predation through the construction of a structure composed of objects in a 2D physically simulated environment supported by a physics engine. We used a deep auto-encoder to extract the defining feature of adaptive structures automatically. The results in the case of no ecological inheritance revealed that the number of available resources can affect the diversity of emerging adaptive structures. Also, in the case with ecological inheritance, it was found that combinations of two types of ecological inheritance, which are the inheritance of adaptive structures and birthplace, can have strong effects on the diversity of emerging structures and the adaptivity of the population. We expect that findings in evolutionary simulations of niche-constructing behavior might contribute to evolutionary design of robotic builders or robot fabrication, especially when we assume physically simulated environments.A major goal of autonomous robot collectives is to robustly perform complex tasks in unstructured environments by leveraging hardware redundancy and the emergent ability to adapt to perturbations. In such collectives, large numbers is a major contributor to system-level robustness. Designing robot collectives, however, requires more than isolated development of hardware and software that supports large scales. Rather, to support scalability, we must also incorporate robust constituents and weigh interrelated design choices that span fabrication, operation, and control with an explicit focus on achieving system-level robustness. Following this philosophy, we present the first iteration of a new framework toward a scalable and robust, planar, modular robot collective capable of gradient tracking in cluttered environments. To support co-design, our framework consists of hardware, low-level motion primitives, and control algorithms validated through a kinematic simulation environment. We discuss how modules made hich impede progress as a result of the motion constraints, and discuss an alternative "naive" planner with improved performance in both clutter-free and cluttered environments. This dedicated focus on system-level robustness over all parts of a complete design cycle, advances the state-of-the-art robots capable of long-term exploration.Social engagement is a key indicator of an individual's socio-emotional and cognitive states. For a child with Autism Spectrum Disorder (ASD), this serves as an important factor in assessing the quality of the interactions and interventions. So far, qualitative measures of social engagement have been used extensively in research and in practice, but a reliable, objective, and quantitative measure is yet to be widely accepted and utilized. In this paper, we present our work on the development of a framework for the automated measurement of social engagement in children with ASD that can be utilized in real-world settings for the long-term clinical monitoring of a child's social behaviors as well as for the evaluation of the intervention methods being used. We present a computational modeling approach to derive the social engagement metric based on a user study with children between the ages of 4 and 12 years. The study was conducted within a child-robot interaction setting that targets sensory processing skills in children.
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