Benjamin Riviere

Benjamin Riviere is an Assistant Professor at New York University with a dual appointment in the Department of Mechanical and Aerospace Engineering at the Tandon School of Engineering and the Department of Computer Science at the Courant Institute of Mathematical Sciences. His research focuses on integrating machine learning models and real-time optimization, with a special application focus on space robotics. He is broadly interested in discrete search, continuous optimization, dynamical systems, and machine learning and their application to physical systems. Dr. Riviere leads the Riviere Robot Lab and has received multiple awards including the Balhaus Prize, Honorable Mention for Best Paper IEEE RA-L 2020, and best Graduate Student Paper in Guidance, Navigation, and Control at AIAA Scitech. His work has been featured as the cover of Science Robotics and covered by diverse and international media outlets.

Benjamin Riviere is a researcher at New York University with expertise in robotics, planning, and machine learning. His work focuses on developing safe and efficient autonomy synthesis for multi-robot systems, integrating end-to-end learning with motion planning. Riviere's research includes neural tree expansion for non-cooperative environments, Monte Carlo tree search with spectral expansion for dynamical systems, and optimal routing for autonomous taxis using distributed reinforcement learning. He also explores Bayesian active sensing for fault estimation and collision avoidance in spacecraft systems. His contributions span both robotics and artificial intelligence, emphasizing safety, scalability, and adaptability in complex environments.