Marc R. Schlichting

Papers

Total Citations

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About

Marc R. Schlichting is a researcher advancing the frontiers of safety and reliability in autonomous robotic systems. His work centers on two critical, interconnected areas: state estimation for multi-agent environments and rigorous validation of safety-critical autonomous behavior. Schlichting’s major contributions include pioneering the use of deep normalizing flows for state estimation, enabling robots to more accurately infer the intentions and trajectories of other agents—a fundamental requirement for safe, cooperative motion planning. Complementing this, he has developed diffusion-based failure sampling techniques that dramatically improve the efficiency and effectiveness of testing autonomous systems in high-dimensional spaces, overcoming the limitations of traditional Markov chain Monte Carlo and importance sampling methods. While his most-cited papers, including "Deep Normalizing Flows for State Estimation" (2023) and "Diffusion-Based Failure Sampling for Evaluating Safety-Critical Autonomous Systems" (2025), each hold 2 citations, these early works represent foundational contributions to a rapidly evolving field. Schlichting’s research directly addresses the core challenge of ensuring that next-generation robotic systems can operate safely and reliably alongside humans, making his work essential reading for anyone interested in the practical deployment of autonomous technology.