Shvetank Prakash
Papers
2
Total Citations
42
H-Index
2
About
Shvetank Prakash is a leading researcher at the intersection of machine learning and robotics, with a focused expertise in **tiny robot learning**—the deployment of ML on resource-constrained, low-cost autonomous robots. His work addresses the critical challenge of enabling intelligent behavior in systems with severe limitations in computation, memory, and power. Prakash’s major contribution is defining and advancing this emerging field, which lies at the nexus of embedded systems, robotics, and efficient machine learning. His seminal paper, "Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots" (2022), has garnered over 38 citations, establishing a foundational roadmap for the community. This work systematically identifies the unique obstacles—from hardware bottlenecks to algorithmic constraints—and proposes forward-looking directions for research. By tackling these challenges, Prakash is enabling a new generation of affordable, autonomous robots for applications in environmental monitoring, disaster response, and ubiquitous sensing. His research is pivotal for students and engineers aiming to push the boundaries of what is possible with minimal resources, making intelligent robotics more accessible and practical than ever before.
Research Focus
Key Achievements
Top Papers
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