Safe Control and Learning for Embodied Autonomy
We develop safe and trustworthy autonomous systems—from perception to planning, control, and coordination.
How can robots learn and adapt to an uncertain world while remaining safe?
Explore our research| Nov. 2026 | NeurIPS | SRL-MPC: Shape-Aware Reinforcement Learned Model Predictive Control accepted to NeurIPS 2026. |
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| Nov. 2026 | CDC | I will give an invited talk at the CDC 2026 workshop, Safe and Resilient Learning-Based Control for Robotic Systems, on December 14, 2026. |
| Sep. 2026 | Automatica | Kalman-Bucy Filtering with Randomized Sensing: Fundamental Limits and Sensor Network Design for Field Estimation accepted to Automatica. |
| Sep. 2026 | RA-L | DiffCVaR: Reinforcement Learning for Risk Adaptation via Differentiable CVaR Barrier Functions accepted to IEEE Robotics and Automation Letters (RA-L). |
| Aug. 2026 | OU | I joined the Department of Mechanical Engineering at Oakland University as an Assistant Professor. |