Brian Elder

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About

Brian Elder is a rising leader in the fields of soft robotics and responsive metamaterials, with a research focus on creating intelligent, untethered systems that can autonomously adapt to complex environments. His most cited work, “Soft multistable magnetic-responsive metamaterials” (2025, 19 citations), tackles a fundamental challenge in soft robotics: achieving and maintaining complex, reconfigurable geometries without continuous energy input. By wirelessly actuating magnetic soft architectures, Elder’s design enables devices to lock into multiple stable states, offering transformative potential for biomedical implants and deployable soft robots that must resist environmental stresses. In parallel, his pioneering work on a “3D‐Printed Self‐Learning Three‐Linked‐Sphere Robot” (2021, 6 and 4 citations) demonstrates a compact, additively manufactured robot that uses reinforcement learning to autonomously navigate confined, unknown spaces. This breakthrough reduces the need for extensive modeling and sensing, allowing the robot to discover effective crawling gaits through trial and error. Together, Elder’s contributions advance the frontier of embodied intelligence, merging material innovation with machine learning to create resilient, energy-efficient robots poised for real-world deployment in search-and-rescue and medical applications.