Fear Learning for Flexible Decision Making in RoboCup: A Discussion

Rizzi, Caroline; Johnson, Colin G.; Vargas, Patrícia A.

doi:10.1007/978-3-030-00308-1_5

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…A number of general learning approaches have been proposed. Rizzi et al (109) proposed a situation-aware fear learning (SAFEL) model and discussed specific scenarios where SAFEL's associative learning could help to increase the positive outcomes of a team during a soccer match through contextual adaptation. SAFEL enables NAO robots in the SPL to learn the behavioral profile of the opposing team at runtime.…”

Section: Robot Cognition and Learningmentioning

confidence: 99%

Scientific and Technological Challenges in RoboCup

Asada

Stryk

2020

Annu. Rev. Control Robot. Auton. Syst.

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Since its inception in 1997, RoboCup has developed into a truly unique and long-standing research community advancing robotics and artificial intelligence through various challenges, benchmarks, and test fields. The main purposes of this article are to evaluate the research and development achievements so far and to identify new challenges and related new research issues. Unlike other robot competitions and research conferences, RoboCup eliminates the boundaries between pure research activities and the development of full system designs with hardware and software implementations at a site open to the public. It also creates specific scientific and technological research and development challenges to be addressed. In this article, we provide an overview of RoboCup, including its league structure and related research issues. We also review recent studies across several research categories to show how participants (called RoboCuppers) address the research and development challenges before, during, and after the annual competitions. Among the diversity of research issues, we highlight two unique aspects of the challenges: the platform design of the robots and the game evaluations. Both of these aspects contribute to solving the research and development challenges of RoboCup and verifying the results from a common perspective (i.e., a more objective view). Finally, we provide concluding remarks and discuss future research directions.

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Section: Robot Cognition and Learningmentioning

confidence: 99%