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About

Hemanth Sarabu is a robotics and AI researcher whose work bridges autonomous systems, agricultural robotics, and safe control under uncertainty. His key research areas include machine learning for planetary rovers, cooperative manipulation in unstructured environments, and risk-aware path planning. Sarabu’s most impactful contribution is the MAARS project (29 citations), a JPL-led initiative applying self-driving AI technologies to Mars and Moon rovers, leveraging the High Performance Spaceflight Computing (HPSC) to bring terrestrial AI advances to space exploration. In agricultural robotics, he pioneered a graph-based cooperative path planning method for dual-arm apple-picking robots (19 citations), using RGB-D cameras in an eye-in-hand configuration to enable coordinated grasping and manipulation in orchards. His related work on leveraging deep learning for cooperative apple-picking arms (9 citations) further demonstrates his ability to integrate perception and control for real-world tasks. Sarabu also addresses fundamental safety challenges in robotics through his work on safe optimal control under parametric uncertainties (4 citations), introducing a regularizer that trades off optimality for collision avoidance. His research has direct implications for space exploration, precision agriculture, and autonomous systems operating in uncertain environments, making him a rising figure in applied robotics and AI.