This work introduces MindfulVerse, an AI-Generated Content (AIGC)-driven application for personalized mindfulness experiences in VR. Using fNIRS to observe brain activation, the study demonstrates that generative meditation improves neural activation in self-regulation regions and positively impacts emotional regulation and user participation compared to static content.

This article presents a distributed control strategy for herding groups of evaders towards a predefined goal region using a team of robotic herders. In herding problems, evaders tend to move away from each other to increase their coverage regions. This makes it challenging to develop control solutions since the wandering evaders need to be collected while driving the herd. To address this, we propose the distributed outmost push strategy, where each robotic herder pushes the evader that is farthest from the goal region. The intuition behind this strategy is that robotic herders should focus on evaders that are further from the goal region as they are more likely to be missed during the herding process. The outmost evaders are selected from the local field of view, and the robotic herders make decisions in a decentralized manner. We also analyze the convergence of the designed dynamics and the minimum sensing range required for herders. The proposal's effectiveness and generality are validated through numerical simulations and real robotic experiments.

Homogeneous swarm robots are of significant research interest due to their robustness, flexibility, and scalability in completing complex tasks across various applications. This paper focuses on trapping heterogeneous targets using swarm robots, with emphasis on their different strengths. These targets consist of weak, strong, and group-moving individuals, where stronger targets exhibit larger body size, higher physical strength, and stronger resistance ability. Our goal is to develop an adaptive controller that enables swarm robots to self-organize and distribute themselves to trap targets, while adjusting encirclement thickness and robot group size based on target strength. We leverage local implicit information generated from density-based interaction to improve inter-robot and robot-target interactions through adaptive allocation and transformation mechanisms. The feasibility of our approach is validated through numerical simulations and experiments involving up to 50 physical robots and one human-controlled transformer.

A swarm of robots is a typical self-organizing system. Parameters of self-organizing model can regulate the interaction among the individuals to achieve the desired collective motion. Thus, it makes sense to optimize the parameters for better performance in self-organization. The evolutionary algorithm (EA) is one promising heuristic optimization algorithm. However, quantitative evaluation of a self-organizing model is still an open scientific question. In this paper, we proposed three metrics for collective motion from the perspective of grouping, polarization and spatial distribution of group. Then, we designed the fitness function with the proposed metrics to bridge the gap between EA and parameter tuning of self-organizing model. In the experimental study, we optimized two kinds of self-organizing models using the differential evolution algorithm (DE) and validated the effectiveness in simulation. Moreover, we validated the optimized parameters on a swarm of physical robots. The experimental results show that the proposed metrics are effective to quantitatively evaluate the self-organizing model so that the DE performs well for the automatic parameter tuning of controlling swarm robots.

Cooperative coevolutionary algorithms (CCEAs) divides a problem into several components and optimizes them independently. Some coevolutionary information will be lost due to the search space separation. This may lead some algorithmic pathologies, such as relative overgeneralization. In addition, according to the interactive nature of the CCEA, the coevolutionary landscapes are dynamic. In this paper, a multipopulation strategy is proposed to simultaneously search local or global optima in each dynamic landscape and provide them to the other components. Besides, a grid-based archive scheme is proposed to archive these historic collaborators for reasonable fitness evaluation. Two benchmark problems were used to test and compare the proposed algorithm to three classical CCEAs. Experimental results show that the proposed algorithm effectively counteract relative overgeneralization pathology and significantly improve the rate of converging to global optimum.