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

• Type: Article in International Conference Proceedings Book
• Date: 2020

Authors

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

Conference proceedings International

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

Pages: 537-548

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

Porto, PORTUGAL, NOV 20-22, 2019

Indexing

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Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12