Artificial Life and Robotics
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Published By Springer-Verlag

1614-7456, 1433-5298
Latest Documents Most Cited Documents Contributed Authors Related Sources Related Keywords

Herd guidance by multiple sheepdog agents with repulsive force

2022 ◽  
Author(s):  
Masao Kubo ◽  
Midori Tashiro ◽  
Hiroshi Sato ◽  
Akihiro Yamaguchi
Keyword(s):  
Repulsive Force

Stereo-vision-based AUV navigation system for resetting the inertial navigation system error

2022 ◽  
Author(s):  
Horng Yi Hsu ◽  
Yuichiro Toda ◽  
Kohei Yamashita ◽  
Keigo Watanabe ◽  
Masahiko Sasano ◽  
...  
Keyword(s):  
Stereo Vision ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
System Error

Evolving collective step-climbing behavior in multi-legged robotic swarm

2022 ◽  
Author(s):  
Daichi Morimoto ◽  
Motoaki Hiraga ◽  
Naoya Shiozaki ◽  
Kazuhiro Ohkura ◽  
Masaharu Munetomo
Keyword(s):  
Climbing Behavior ◽  
Robotic Swarm ◽  
Step Climbing

Using sim-to-real transfer learning to close gaps between simulation and real environments through reinforcement learning

2021 ◽  
Author(s):  
Yuto Ushida ◽  
Hafiyanda Razan ◽  
Shunta Ishizuya ◽  
Takuto Sakuma ◽  
Shohei Kato
Keyword(s):  
Reinforcement Learning ◽  
Transfer Learning

An evaluation of fluidics for tsunamis in pedestrian’s flood evacuations

2021 ◽  
Author(s):  
Yuichi Hirokawa ◽  
Daisuke Matsuoka ◽  
Noriaki Nishikawa ◽  
Toshiyuki Asano

A hierarchical training method of generating collective foraging behavior for a robotic swarm

2021 ◽  
Author(s):  
Boyin Jin ◽  
Yupeng Liang ◽  
Ziyao Han ◽  
Motoaki Hiraga ◽  
Kazuhiro Ohkura
Keyword(s):  
Foraging Behavior ◽  
Training Method ◽  
Robotic Swarm ◽  
Collective Foraging

scholarly journals Application of neural ordinary differential equations to the prediction of multi-agent systems

2021 ◽  
Author(s):  
Sebastian Herzog ◽  
Florentin Wörgötter
Keyword(s):  
Differential Equations ◽  
Dynamic Systems ◽  
Data Driven ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Detailed Understanding ◽  
Agent Systems ◽  
Data Driven Approach ◽  
Multi Agent ◽  
High Level

AbstractDynamic systems are usually described by differential equations, but formulating these equations requires a high level of expertise and a detailed understanding of the observed system to be modelled. In this work, we present a data-driven approach, which tries to find a parameterization of neural differential equations system to describe the underlying dynamic of the observed data. The presented method is applied to a multi-agent system with thousand agents.

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