Chris Agia
PhD Student
Chris is a graduate student in the Department of Computer Science advised jointly by Professors Jeannette Bohg and Marco Pavone. His research focuses on how complex robotic behavior can be learned from data and embodied interaction (reinforcement learning), along with how to use these behaviors to feasibly and efficiently plan for any task (task & motion planning).
Prior to joining Stanford, Chris graduated with honors from University of Toronto's Engineering Science program. In that time, he conducted research in robot vision, mapping, planning, and control with UofT's Robot Vision and Learning Lab, Autonomous Systems and Biomechatronics Lab, and McGill's Mobile Robotics Lab. Chris has also held internships with Microsoft Mixed Reality, Google Cloud, and Noah's Ark Research Labs.
Beyond research, Chris enjoys practicing soccer, tennis, going on trail runs, reading, and playing music's golden age on the guitar, bass and drums.
Awards:
- Stanford School of Engineering Fellowship (2021)
Publications
11 publications · full lab bibliography
- M. Ganai, R. Sinha, C. Agia, D. Morton, L. Di Lillo, and M. Pavone, “Real-Time Out-of-Distribution Failure Prevention via Multi-Modal Reasoning,” Conf. on Robot Learning, 2025. (In Press)
- J. Kwok, C. Agia, R. Sinha, M. Foutter, S. Li, I. Stoica, A. Mirhoseini, and M. Pavone, “RoboMonkey: Scaling Test-Time Sampling and Verification for Vision-Language-Action Models,” Conf. on Robot Learning, 2025. (In Press)
- C. Agia, R. Sinha, J. Yang, R. Antonova, M. Pavone, H. Nishimura, M. Itkina, and J. Bohg, “CUPID: Curating Data your Robot Loves with Influence Functions,” Conf. on Robot Learning, 2025. (In Press)
- F. Marchiori, R. Sinha, C. Agia, A. Robey, G. J. Pappas, M. Conti, and M. Pavone, “Preventing Robotic Jailbreaking via Multimodal Domain Adaptation,” Proc. IEEE Conf. on Robotics and Automation, 2025. (Submitted)
- J. Thumm, C. Agia, M. Pavone, and M. Althoff, “Text2Interaction: Establishing Safe and Preferable Human-Robot Interaction,” Conf. on Robot Learning, 2024. (In Press)
- C. Agia, R. Sinha, J. Yang, Z. Cao, R. Antonova, M. Pavone, and J. Bohg, “Unpacking Failure Modes of Generative Policies: Runtime Monitoring of Consistency and Progress,” Conf. on Robot Learning, 2024. (In Press)
- M. Bazzi, A. Shahid, C. Agia, J. Alora, M. Forgione, D. Piga, F. Braghin, M. Pavone, and L. Roveda, “RoboMorph: In-Context Meta-Learning for Robot Dynamics Modeling,” Proc. of the IFAC/AAAI Int. Conf. on Informatics in Control, Automation and Robotics, 2024.
- R. Sinha, A. Elhafsi, C. Agia, M. Foutter, E. Schmerling, and M. Pavone, “Real-Time Anomaly Detection and Planning with Large Language Models,” Robotics: Science and Systems, 2024.
- C. Agia, G. C. Vila, S. Bandyopadhyay, D. S. Bayard, K. Cheung, C. H. Lee, E. Wood, I. Aenishanslin, S. Ardito, L. Fesq, M. Pavone, and I. A. D. Nesnas, “Modeling Considerations for Developing Deep Space Autonomous Spacecraft and Simulators,” IEEE Aerospace Conference, 2024.
- K. Lin, C. Agia, T. Migimatsu, M. Pavone, and J. Bohg, “Text2Motion: From Natural Language Instructions to Feasible Plans,” Autonomous Robots, vol. 47, no. 8, pp. 1345–-1365, 2023.
- A. Elhafsi, R. Sinha, C. Agia, E. Schmerling, I. A. D. Nesnas, and M. Pavone, “Semantic Anomaly Detection with Large Language Models,” Autonomous Robots, vol. 47, no. 8, pp. 1035--1055, 2023.