The new era with prosperous artificial intelligence (AI) and robotics technology is reshaping the materials discovery process in a more radical fashion. Here we present authentic intelligent robotics for chemistry (AIR-Chem), integrated with technological innovations in the AI and robotics fields, functionalized with modules including gradient descent-based optimization frameworks, multiple external field modulations, a real-time computer vision (CV) system, and automated guided vehicle (AGV) parts. AIR-Chem is portable and remotely controllable by cloud computing. AIR-Chem can learn the parametric procedures for given targets and carry on laboratory operations in standalone mode, with high reproducibility, precision, and availability for knowledge regeneration. Moreover, an improved nucleation theory of size focusing on inorganic perovskite quantum dots (IPQDs) is theoretically proposed and experimentally testified to by AIR-Chem. This work aims to boost the process of an unmanned chemistry laboratory from the synthesis of chemical materials to the analysis of physical chemical properties, and it provides a vivid demonstration for future chemistry reshaped by AI and robotics technology.
School-aged children with autism spectrum disorders (ASDs) have delayed gestural development, in comparison with age-matched typically developing children. In this study, an intervention program taught children with low-functioning ASD gestural comprehension and production using video modelling (VM) by a computer-generated robot animation. Six to 12-year-old children with ASD (N = 20; IQ < 70) were taught to recognize 20 gestures produced by the robot animation (phase I), to imitate these gestures (phase II) and to produce them in appropriate social contexts (phase III). Across the three phases, significant differences were found between the results of the pretest and the immediate and follow-up posttests; the results of both posttests were comparable, after controlling for the children's motor and visual memory skills. The children generalized their acquired gestural skills to a novel setting with a human researcher. These results suggest that VM by a robot animation is effective in teaching children with low-functioning ASD to recognize and produce gestures.
Battery fast charging is one of the most significant and difficult techniques affecting the commercialization of electric vehicles (EVs). In this paper, we propose a fast charge framework based on model predictive control, with the aim of simultaneously reducing the charge duration, which represents the out-of-service time of vehicles, and the increase in temperature, which represents safety and energy efficiency during the charge process. The RC model is employed to predict the future State of Charge (SOC). A single mode lumped-parameter thermal model and a neural network trained by real experimental data are also applied to predict the future temperature in simulations and experiments respectively. A genetic algorithm is then applied to find the best charge sequence under a specified fitness function, which consists of two objectives: minimizing the charging duration and minimizing the increase in temperature. Both simulation and experiment demonstrate that the Pareto front of the proposed method dominates that of the most popular constant current constant voltage (CCCV) charge method.
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