Soham Shanbhag
Papers
1
Total Citations
11
H-Index
1
About
Soham Shanbhag is a robotics researcher whose work focuses on the intersection of machine learning and autonomous systems, particularly in anomaly detection and safety-critical applications for unmanned aerial vehicles (UAVs). His most cited paper, "Model-Free Unsupervised Anomaly Detection of a General Robotic System Using a Stacked LSTM and Its Application to a Fixed-Wing Unmanned Aerial Vehicle" (2022), has garnered 11 citations and represents a significant contribution to the field. In this work, Shanbhag developed a novel anomaly detection method using stacked Long Short-Term Memory (LSTM) networks that can be applied to any robot controlled by feedback control—a model-free, unsupervised approach that eliminates the need for labeled training data. This innovation is particularly valuable for ensuring the safety of autonomous systems in real-world applications, where unexpected failures can have serious consequences. By creating a generalizable framework for detecting anomalies in robotic systems, Shanbhag has addressed a critical need as robots become increasingly prevalent in everyday life. His research demonstrates a practical understanding of both theoretical machine learning and applied robotics, making his work relevant for students and researchers interested in autonomous systems, safety engineering, and the deployment of intelligent robots in uncontrolled environments.
Research Focus
Key Achievements
Top Papers
- 1