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About

Emily Scheide is a robotics researcher whose work spans autonomous systems, behavior tree learning, and multi-robot coordination. Her most influential contribution, "Resilient and Modular Subterranean Exploration with a Team of Roving and Flying Robots" (2022, 50 citations), demonstrates her capacity for tackling complex real-world robotics challenges, addressing critical issues of mobility, communication, and navigation in subterranean environments through diverse, autonomous robot teams. Complementing this, her 2021 paper on behavior tree learning via Monte Carlo DAG search over formal grammars (19 citations) introduced an elegant algorithmic solution to the labor-intensive problem of manually designing robot control architectures, significantly advancing automated task planning methodology. Scheide's more recent work reveals a broadening research vision. Her 2024 paper on decentralized multi-robot coordination introduces novel intent-communication frameworks, improving collaboration efficiency in challenging scenarios. Meanwhile, her 2025 paper on GoBot showcases a socially meaningful application—an assistive robot designed to encourage mobility in children during rehabilitation, blending autonomous behavior with human-centered design goals. Across her career, Scheide has consistently demonstrated a talent for bridging theoretical rigor with practical impact, making her an emerging voice in autonomous robotics and human-robot interaction research.