Prajwal Koirala
Cornell University. 124 Hoy Road, Ithaca, NY 14850.
I am a Ph.D. student in Robotics at Cornell University, advised by Professor Mark Campbell. My research focuses on learning and estimation for robotics, with an emphasis on developing intelligent, reliable robotic systems that operate safely in uncertain and dynamic environments. I am particularly interested in integrating machine learning with principled models for perception, decision-making, and control to improve robustness, safety, and real-world generalization.
Before joining Cornell, I received my MS from Iowa State University, where I worked on reinforcement learning and safety-critical control for autonomous systems under the guidance of Dr. Cody Fleming and in frequent collaboration with Dr. Soumik Sarkar. My research included developing offline reinforcement learning algorithms that integrate safety constraints into data-driven decision-making, resulting in publications at leading conferences in machine learning, robotics, and controls (ICLR, CoRL, CDC, among others). I also gained teaching experience as a Graduate Teaching Assistant, conducting lab sessions in system dynamics and control and received multiple Research Excellence Awards for my contributions during my time at Iowa State.
News
| Jan 26, 2026 | Flow-Based Single-Step Completion for Efficient and Expressive Policy Learning has been accepted to ICLR 2026. We propose a flow-based generative policy for one-shot action generation, enabling much faster training and inference (across offline RL, GCRL, and BC). |
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| Aug 22, 2025 | Thrilled to begin my Ph.D. in Robotics at Cornell University! Excited to explore cutting-edge research in safe and intelligent autonomy. Supported in my first semester by the Cornell Fellowship. |
| Jul 15, 2025 | Paper accepted to CDC 2025, Rio de Janerio. “FAWAC: Feasibility Informed Advantage Weighted Regression for Persistent Safety in Offline Reinforcement Learning” Coauthored with Dr. Zhanhong Jiang, Dr. Soumik Sarkar and Dr. Cody Fleming. |
| Jun 17, 2025 | Defended my MS thesis. Title - "Towards safe and efficient offline reinforcement learning - learning safety constraints and expressive policies via generative modeling" |