Kristian Ceder
Papers
2
Total Citations
13
H-Index
2
About
Kristian Ceder is a robotics researcher focused on advancing autonomous navigation and multi-robot coordination in complex environments. His primary contributions lie at the intersection of deep reinforcement learning and model predictive control, where he develops hybrid frameworks for real-time, collision-free trajectory planning. Ceder’s 2023 work on integrating these techniques for single mobile robots achieved 7 citations, demonstrating a practical balance between computational efficiency and robust obstacle avoidance. Building on this, his 2024 paper extended the approach to multi-robot systems using Bird’s-Eye-View vision, enabling scalable coordination for industrial automation and indoor logistics—a contribution that has already garnered 6 citations. By combining continuous learning with predictive control, Ceder addresses critical challenges in dynamic settings, such as trajectory generation and real-time collision avoidance. His research is notable for its direct applicability to real-world robotics, bridging the gap between simulation and deployment. With a growing citation record and a focus on practical, efficient solutions, Ceder is a rising voice in autonomous systems, offering valuable insights for students and researchers working on intelligent, multi-agent navigation.
Research Focus
Key Achievements
Top Papers
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- 2