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| Aug. 2026 | OU | I joined the Department of Mechanical Engineering at Oakland University as an Assistant Professor. |
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| Jul. 2026 | T-ASE | “AMBUSH: Collaborative Capture in Complex Environments with Neural Acceleration” accepted to IEEE Transactions on Automation Science and Engineering (T-ASE). |
| Jun. 2025 | IROS | “Safe Navigation in Uncertain Crowded Environments Using Risk Adaptive CVaR Barrier Functions” accepted to IROS 2025. |
| Feb. 2025 | L4DC | “Multi-Constraint Safe Reinforcement Learning via Closed-Form Solution for Log-Sum-Exp Approximation of Control Barrier Functions” accepted to L4DC 2025. |
| Mar. 2024 | ICRA | “Sensor-based Multi-Robot Search and Coverage with Spatial Separation in Unstructured Environments” presented at ICRA 2024. |