Tomofumi Ohshita
Papers
1
Total Citations
2
H-Index
1
About
Tomofumi Ohshita is a researcher whose work centers on multi-robot systems, cooperative behavior learning, and autonomous control. His most notable contribution, "A cooperative behavior learning control of multi-robot using trace information" (2008), explores how robots can learn to collaborate by leveraging environmental trace data—a foundational concept for decentralized swarm intelligence. Though this early work has garnered 2 citations, it reflects a pioneering approach to enabling robots to adapt and coordinate without centralized oversight, a challenge that remains central to modern robotics. Ohshita’s research has implications for applications in search-and-rescue, warehouse automation, and exploration, where teams of robots must operate efficiently in dynamic, unstructured environments. His focus on trace-based learning offers a unique perspective on how past interactions can inform future cooperative strategies, contributing to the broader field of multi-agent reinforcement learning. While his citation count is modest, the conceptual groundwork laid in his 2008 study continues to resonate with researchers tackling the complexities of scalable, adaptive robot teams.
Research Focus
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Top Papers
- 1