Collaborative Research: Building a Collaborative Network of Researchers in Mechano-Computation

About This Grant

The rapid advancements in neuroscience, robotics, and computer systems have underscored the vital interactions between mechanical and computational systems in shaping behavior. In natural systems, such as those found in animals, the brain and body must collaborate effectively for the successful navigation of a complex environment. The brain contributes computational intelligence, while the body provides mechanical intelligence. Integrating these elements—computational and mechanical intelligence—into the concept of mechano-computation represents a frontier in both robotics and neuroscience research. Progress in this field necessitates interdisciplinary communication and collaboration across various scientific domains. To propel this promising field forward, the Mechano-computation for Expanding Scientific Horizons (MESH) Network aims to unite diverse researchers from robotics, mechanics, materials science, neuroscience, information theory, biology, engineering design, and applied mathematics. Through workshops, travel grants, and the facilitation of collaborative projects, this network seeks to stimulate interdisciplinary dialogue, develop rigorous metrics for assessing autonomous systems, train the next generation of researchers, and push the boundaries of research in all areas of mechano-computation. By establishing a centralized resource for sharing findings, benchmarks, and methodologies, this network of researchers can accelerate innovation and position the United States as a leader in this transformative field, laying the groundwork for enhanced robotic systems in healthcare, agriculture, forestry, national security, and beyond. It may be argued that the full potential of robotics will not be realized until an intelligent physical body is purposefully designed from the outset, with careful consideration of both the available computational intelligence and the affordances the body can provide—affordances that, if appropriately leveraged, can offload and simplify computational demands by enabling efficient, embodied solutions to complex tasks. The Mechano-computation for Expanding Scientific Horizons (MESH) Network will bring together leading experts to tackle these critical challenges in autonomous systems through the integration of mechanical and computational intelligence. Creating intentional mechano-computation will enhance the design and control of autonomous systems, making them more efficient and explainable, and it will contribute to the development of innovative materials, mechanisms, and control strategies, pushing the boundaries of current research. We anticipate five key outcomes as a result of the formation of the MESH Network: (1) A comprehensive theoretical framework and standardized metrics for mechano-computation; (2) Improved interdisciplinary collaboration and communication among researchers; (3) Long-term interactions among network members and early-career researchers, including nurturing graduate students trained at the intersection of disciplines; (4) Sharing of innovative materials, mechanisms, and control strategies; (5) Practical demonstrations by network participants of mechano-computation systems addressing societal and environmental challenges. The network will accomplish these outcomes through tasks that build online repositories of network critical technical and organizational information, in-person events to broaden discussion and collaboration, online communities, and targeted support for bringing in new collaborative research areas. This project is supported by the Dynamics, Control, and System Diagnostics (DCSD), the Engineering Design and Systems Engineering (EDSE) and the Mechanics of Materials (MoMs) programs of the Division of Civil, Mechanical, and Manufacturing Innovation (CMMI) in the Directorate for Engineering. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.