Papers
11
Total Citations
45
H-Index
3
About
Rolif Lima is an emerging robotics researcher whose work spans space robotics, teleoperation, and autonomous manipulation systems. His most significant contributions lie at the intersection of machine learning and robot control, particularly in developing intelligent controllers for complex, real-world robotic applications. Lima's most impactful research focuses on floating space robots for proximity operations, where he has pioneered both deep reinforcement learning-based and model-dependent learning methodologies for whole-body control of orbiting satellite manipulators — a notoriously challenging domain due to highly nonlinear dynamics. These papers have each garnered 10 citations since 2023, demonstrating rapid recognition within the space robotics community. Equally notable is his work on teleoperation, where he has addressed the persistent challenge of network-induced delays through model-free predictors and model-mediated approaches, while also advancing shared autonomy frameworks that reduce operator cognitive load. His research extends into practical retail robotics, developing dual-arm mobile manipulation systems and autonomous shelf management solutions for real-world deployment. With publications spanning deep reinforcement learning, model predictive control, hardware-in-the-loop verification, and neural fields, Lima demonstrates impressive breadth across theoretical and applied robotics. His growing citation record reflects a researcher rapidly establishing himself at the forefront of autonomous robotic systems research.
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