Swimming microorganisms switch between locomotory gaits to enable complex navigation strategies such as run-and-tumble to explore their environments and search for specific targets. This ability of targeted navigation via adaptive gait-switching is particularly desirable for the development of smart artificial microswimmers that can perform complex biomedical tasks such as targeted drug delivery and microsurgery in an autonomous manner. Here we use a deep reinforcement learning approach to enable a model microswimmer to self-learn effective locomotory gaits for translation, rotation and combined motions. The Artificial Intelligence (AI) powered swimmer can switch between various locomotory gaits adaptively to navigate towards target locations. The multimodal navigation strategy is reminiscent of gait-switching behaviors adopted by swimming microorganisms. We show that the strategy advised by AI is robust to flow perturbations and versatile in enabling the swimmer to perform complex tasks such as path tracing without being explicitly programmed. Taken together, our results demonstrate the vast potential of these AI-powered swimmers for applications in unpredictable, complex fluid environments.

There is growing interest in the development of artificial microscopic machines that can perform complex maneuvers like swimming microorganisms for potential biomedical applications. At the microscopic scales, the dominance of viscous over inertial forces imposes stringent constraints on locomotion. In the absence of inertia, Purcell first proposed an elegant way to generate net translation using kinematically irreversible motions [E. M. Purcell, “Life at low Reynolds number,” Am. J. Phys. 45, 3–11 (1977)]. In addition to net translation, a more recent prototype known as Purcell's “rotator” has been proposed in Dreyfus et al. [“Purcell's “rotator”: Mechanical rotation at low Reynolds number,” Eur. Phys. J. B 47, 161–164 (2005)] as a mechanical implementation of net rotation at low Reynolds numbers. These ingenious designs rely on knowledge of the surrounding environment and the physics of locomotion within the environment, which may be incomplete or unclear in more complex scenarios. More recently, reinforcement learning has been used as an alternative approach to enable a machine to learn effective locomotory gaits for net translation based on its interaction with the surroundings. In this work, we demonstrate the use of reinforcement learning to generate net mechanical rotation at low Reynolds numbers without requiring prior knowledge of locomotion. For a three-sphere configuration, the reinforcement learning recovers the strategy proposed by Dreyfus et al. As the number of spheres increases, multiple effective rotational strategies emerge from the learning process. However, given sufficiently long learning processes, all machines considered in this work converge to a single type of rotational policies that consist of traveling waves of actuation, suggesting its optimality of the strategy in generating net rotation at low Reynolds numbers.

This research design delves into a groundbreaking study that highlights the innovative use of wearables and deep reinforcement learning to enhance emotional well-being and manage stress. With a focus on leveraging wearable sensor technology, specifically the Empatica EmbracePlus device, the research unfolds at the intersection of emotional intelligence (EQ) augmentation, emotion recognition and intentional EQ practice. Leveraging the potential of wearables, participants were selected from the Master of Engineering Technical Management (METM) program at Texas A&M University, based on their involvement in studies related to emotional intelligence and management. The objective is to investigate personalized strategies for improving emotional well-being through three interconnected aims: a) exploring the relationship between emotional intelligence, stress management, and physiological indicators using wearable technology; b) evaluating the effectiveness of intentional EQ practices in improving emotional well-being and reducing stress; and c) utilizing machine learning to optimize the impact of intentional practices on overall well-being. This approach underscores the potential of wearables to illuminate the physiological responses accompanying mindfulness practices, offering fresh insights into the dynamic relationship between emotional intelligence, stress management, and intentional well-being enhancement.