Papers
17
Total Citations
127
H-Index
6
About
Shreyas Kousik is a robotics researcher whose work sits at the intersection of safe motion planning, reachability analysis, and real-time control for autonomous systems. His research addresses one of the field's most pressing challenges: enabling robots to operate safely and efficiently in uncertain, dynamic environments without sacrificing computational tractability. Kousik's most influential contributions center on reachability-based trajectory design for mobile robots and autonomous vehicles, where his receding-horizon planning frameworks bridge the longstanding gap between formal safety guarantees and real-time performance—work that has accumulated over two dozen citations and influenced subsequent approaches to provably safe autonomy. His 2022 paper on safe reinforcement learning using black-box reachability analysis (28 citations) demonstrates his ability to extend these safety guarantees even when robot and environment models are unknown, a significant advance for deploying deep RL in safety-critical settings. Beyond ground robots, Kousik has applied these ideas to quadrotor flight, robotic manipulators under uncertainty, and soft robotics control. More recently, he has expanded into semantic mapping and socially aware bipedal navigation, reflecting a broadening vision of robots coexisting intelligently with humans. Across his portfolio, Kousik consistently prioritizes rigorous safety verification paired with practical deployability—making his work valuable reading for students tackling autonomous systems research.
Research Focus
Key Achievements
Top Papers
- 1Safe Reinforcement Learning Using Black-Box Reachability Analysis28 citations · 2022
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