9 papers
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…
Tracking Feral Horses in Aerial Video Using Oriented Bounding Boxes
Saeko Takizawa, Tamao Maeda, Shinya Yamamoto +1
The social structures of group-living animals such as feral horses are diverse and remain insufficiently understood, even within a single species. To investigate group dynamics, ae…
Detection and Identification of Penguins Using Appearance and Motion Features
Kasumi Seko, Hiroki Kinoshita, Raj Rajeshwar Malinda +1
In animal facilities, continuous surveillance of penguins is essential yet technically challenging due to their homogeneous visual characteristics, rapid and frequent posture chang…
Modeling and Analysis of Fish Interaction Networks under Projected Visual Stimuli
Hiroaki Kawashima, Raj Rajeshwar Malinda, Saeko Takizawa
This paper addresses the estimation of a dynamic interaction network, a network of influence among individuals, under projected visual stimuli to quantify the influences of inter-i…
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…
Fish Tracking Challenge 2024: A Multi-Object Tracking Competition with Sweetfish Schooling Data
Makoto M. Itoh, Qingrui Hu, Takayuki Niizato +2
The study of collective animal behavior, especially in aquatic environments, presents unique challenges and opportunities for understanding movement and interaction patterns in the…