Tomoaki Oiki
Papers
2
Total Citations
35
H-Index
2
About
Tomoaki Oiki is a robotics researcher whose work sits at the intersection of control theory, reinforcement learning, and trajectory optimization. His primary research focus is on enabling autonomous systems to navigate and operate in environments where their own dynamics are unknown or poorly modeled—a critical challenge for real-world robotics. Oiki’s major contribution is a novel reinforcement learning-based algorithm for trajectory optimization in constrained dynamical systems. This approach allows robots to generate smooth, dynamically feasible paths without requiring an explicit model of their own motion, making it highly applicable to field robotics and systems with complex, hard-to-identify dynamics. His most-cited paper on this topic has accumulated 31 citations, demonstrating its relevance to the community. By tackling the fundamental problem of unknown dynamics, Oiki’s work bridges the gap between model-based planning and data-driven learning, offering a practical solution for robots that must adapt to changing conditions. His research is particularly notable for its potential to improve the safety and efficiency of autonomous vehicles, drones, and manipulators operating in unstructured environments.
Research Focus
Key Achievements
Top Papers
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