5 papers · 1 filter
Controlling Fish Schools via Reinforcement Learning of Virtual Fish Movement
Yusuke Nishii, Hiroaki Kawashima
This study investigates a method to guide and control fish schools using virtual fish trained with reinforcement learning. We utilize 2D virtual fish displayed on a screen to overc…
Data-Driven Control of a Magnetically Actuated Fish-Like Robot
Akiyuki Koyama, Hiroaki Kawashima
Magnetically actuated fish-like robots offer promising solutions for underwater exploration due to their miniaturization and agility; however, precise control remains a significant…
LLM-Guided Decentralized Exploration with Self-Organizing Robot Teams
Hiroaki Kawashima, Shun Ikejima, Takeshi Takai +2
When individual robots have limited sensing capabilities or insufficient fault tolerance, it becomes necessary for multiple robots to form teams during exploration, thereby increas…
Adaptive Policy Switching of Two-Wheeled Differential Robots for Traversing over Diverse Terrains
Haruki Izawa, Takeshi Takai, Shingo Kitano +2
Exploring lunar lava tubes requires robots to traverse without human intervention. Because pre-trained policies cannot fully cover all possible terrain conditions, our goal is to e…
A Deep Reinforcement Learning Framework for Closed-loop Guidance of Fish Schools via Virtual Agents
Takato Shibayama, Hiroaki Kawashima
Guiding collective motion in biological groups is a fundamental challenge in understanding social interaction rules. In this study, we propose a deep reinforcement learning (RL) fr…