Yuma Shida
Papers
1
Total Citations
6
H-Index
1
About
Yuma Shida is a rising researcher in multi-robot systems and reinforcement learning, whose work addresses critical challenges in cooperative robotics. His primary research focuses on multi-robot task allocation for complex object transportation, particularly when tasks involve unknown or infeasible elements. Shida’s most-cited paper, "Reinforcement Learning of Multi-robot Task Allocation for Multi-object Transportation with Infeasible Tasks" (2025, 6 citations), introduces a novel framework that enables robot teams to dynamically allocate transportation tasks for objects with unknown weights—a significant hurdle in real-world applications like automated delivery services. By integrating reinforcement learning with adaptive task assignment, Shida’s approach enhances both individual efficiency and scalable cooperation among robots, even when some tasks are initially impossible. This work stands out for its practical relevance, offering a pathway to more robust and flexible multi-robot systems in logistics and beyond. Though early in his career, Shida’s contributions are already shaping the future of autonomous multi-agent coordination, promising impactful advances in robotics and artificial intelligence.
Research Focus
Key Achievements
Top Papers
- 1