Alisa Allaire

Papers

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About

Alisa Allaire is a roboticist whose research focuses on dynamic, contact-rich locomotion, exploring how robots can physically interact with their environments to move more fluidly and adaptively. Her most-cited work, "Learning to Navigate by Pushing" (2022), introduces a novel reflex-based control framework that enables robots to push off from obstacles—such as walls or furniture—to navigate cluttered, unstructured spaces. By switching between optimized, hand-crafted reflex controllers, her approach produces smooth, predictable motions that mimic the efficiency of biological movement. This work, with 2 citations to date, lays a foundation for more agile and resilient robotic systems in real-world settings, from search-and-rescue to assistive robotics. Allaire’s contributions stand out for bridging classical control theory with adaptive, learning-based strategies, offering a practical path toward robots that can leverage their surroundings rather than avoid them. Her research is particularly notable for its emphasis on simplicity and reliability, making it accessible for further development in both academic and applied robotics contexts.