Learning Low-Level Behaviors and High-Level Strategies in Humanoid Soccer

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2020

Autores

Simoes, D (Autor) Outra; Amaro, P (Autor) Outra; Maria Teresa Andrade (Autor) FEUP; lau, n (Autor) Outra; reis, lp (Autor) FEUP

Ata de Conferência Internacional

Título: FOURTH IBERIAN ROBOTICS CONFERENCE: ADVANCES IN ROBOTICS, ROBOT 2019, VOL 2

Páginas: 537-548

4th Iberian Robotics Conference (Robot) - Advances in Robotics

Porto, PORTUGAL, NOV 20-22, 2019

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00R-QN5

DOI: 10.1007/978-3-030-36150-1_44

Abstract (EN): This paper investigates the learning of both low-level behaviors for humanoid robot controllers and of high-level coordination strategies for teams of robots engaged in simulated soccer. Regarding controllers, current approaches typically hand-tune behaviors or optimize them without realistic constraints, for example allowing parts of the robot to intersect with others. This level of optimization often leads to low-performance behaviors. Regarding strategies, most are hand-tuned with arbitrary parameters (like agents moving to pre-defined positions on the field such that eventually they can score a goal) and the thorough analysis of learned strategies is often disregarded. This paper demonstrates how it is possible to use a distributed framework to learn both low-level behaviors, like sprinting and getting up, and high-level strategies, like a kick-off scenario, outperforming previous approaches in the FCPortugal3D Simulated Soccer team.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12