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About

Marc Rigter is a robotics researcher whose work focuses on making robot learning more efficient, practical, and autonomous. His key research areas include learning from demonstration, shared autonomy, model-based reinforcement learning, and sim-to-real transfer. Rigter’s major contributions center on minimizing human effort in robot training—his most cited paper, "A Framework for Learning From Demonstration With Minimal Human Effort" (2020, 32 citations), addresses the critical challenge of reducing costly human supervision by integrating reinforcement learning with shared autonomy. He further advances shared autonomy systems by modeling stochastic operator behavior to optimize control switching between humans and AI (2022). In sim-to-real transfer, Rigter’s work "TWIST: Teacher-Student World Model Distillation for Efficient Sim-to-Real Transfer" (2024, 5 citations) introduces a novel distillation method that bridges the reality gap for vision-based robotics, enhancing sample efficiency and generalization. His earlier research on robot path planning for multiple target regions (2019) also demonstrates his breadth in tackling practical navigation problems. With a growing citation impact and innovative approaches to human-robot interaction and model-based learning, Rigter is shaping the future of deployable, autonomous robotic systems.