Siva Kailas
Papers
4
Total Citations
37
H-Index
3
About
Siva Kailas is a robotics researcher whose work bridges imitation learning, multi-robot systems, and environmental sensing. His most notable contribution is a systematic comparison of imitation learning algorithms for bimanual manipulation, which, despite being published in 2024, has already garnered 17 citations by revealing critical insights into hyperparameter sensitivity and data efficiency in high-precision industrial settings. Kailas also created the WIT-UAS dataset, the first publicly available long-wave infrared thermal dataset for detecting crew and vehicle assets during prescribed wildland fires—a vital resource for safety monitoring that has earned 11 citations. His earlier work on multiagent rollout and policy iteration for partially observable Markov decision processes (POMDPs) addresses complex multi-robot repair problems, while his research on multi-robot adaptive sampling enables supervised spatiotemporal forecasting. Together, these contributions demonstrate Kailas’s impact across manipulation, field robotics, and decision-making under uncertainty, with a growing citation record that underscores the practical relevance of his work for both industrial automation and emergency response applications.
Research Focus
Key Achievements
Top Papers
- 1A Comparison of Imitation Learning Algorithms for Bimanual Manipulation17 citations · 2024
- 2
- 3
- 4Multi-robot Adaptive Sampling for Supervised Spatiotemporal Forecasting3 citations · 2023