Behavior Acquisition Based on Multi-module Learning System in Multi-agent Environment

Takahashi, Yasutake; Edazawa, Kazuhiro; Asada, Minoru

doi:10.1007/978-3-540-45135-8_39

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…The lion's share of this work involves gait development (such as [19,18]), with some work on kicking [6,32], head actuation [5] and omnidirectional velocity control [17]. Third, about sixteen papers have concerned themselves with learning higher-level behaviors (for example [26,28]). …”

Section: Machine Learning At Robocupmentioning

confidence: 99%

Towards Rapid Multi-robot Learning from Demonstration at the RoboCup Competition

Freelan

Wicke

Sullivan

et al. 2015

RoboCup 2014: Robot World Cup XVIII

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Abstract. We describe our previous and current efforts towards achieving an unusual personal RoboCup goal: to train a full team of robots directly through demonstration, on the field of play at the RoboCup venue, how to collaboratively play soccer, and then use this trained team in the competition itself. Using our method, HiTAB, we can train teams of collaborative agents via demonstration to perform nontrivial joint behaviors in the form of hierarchical finite-state automata. We discuss HiTAB, our previous efforts in using it in RoboCup 2011 and 2012, recent experimental work, and our current efforts for 2014, then suggest a new RoboCup Technical Challenge problem in learning from demonstration.Imagine that you are at an unfamiliar disaster site with a team of robots, and are faced with a previously unseen task for them to do. The robots have only rudimentary but useful utility behaviors implemented. You are not a programmer. Without coding them, you have only a few hours to get your robots doing useful collaborative work in this new environment. How would you do this?Our interest lies in rapid, real-time multi-robot training from demonstration. Here a single human trainer teaches a team of robots, via teleoperation, how to collectively perform tasks in previously unforeseen environments. This is difficult for two reasons. First, nontrivial behaviors can present a high-dimensional space to learn, yet one can only provide a few samples, as online training samples are costly to collect. This is a worst case for the so-called "curse of dimensionality". Second, when training multiple interactive robots, even if you can quantify the emergent macro-level group behavior you wish to achieve, in order to do learning, each agent needs to know the micro-level behavior he is being asked to do. One may have a micro→macro function (a simulator), but it is unlikely that one has the inverse macro→micro function, resulting in what we call the "multiagent inverse problem". These two challenges mean that real-time multi-robot learning from demonstration has proven very difficult and has a very sparse literature.Over the past several years we have participated in the Kid-Size Humanoid League with a single objective: to successfully do a personal RoboCup-style technical challenge of our own invention, independent of those offered at RoboCup: can we train multiple generic robots, through demonstration on the field, how to play collaborative soccer at RoboCup solely within the preparatory time prior to the competition itself ?

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Section: Machine Learning At Robocupmentioning

confidence: 99%

Towards Rapid Multi-robot Learning from Demonstration at the RoboCup Competition

Freelan

Wicke

Sullivan

et al. 2015

RoboCup 2014: Robot World Cup XVIII

View full text Add to dashboard Cite

show abstract

“…The keys for simultaneous learning to acquire competitive behaviors in such an environment are a modular learning system for adaptation to the policy alternation of others; and an introduction of macro actions for simultaneous learning to reduce the search space. Takahashi et al (Takahashi et al, 2005a,b;Edazawa et al, 2004;Takahashi et al, 2003aTakahashi et al, , 2002a) presented a method of modular learning in a multi-agent environment in which the learning agents can simultaneously learn their behaviors and adapt themselves to the resultant situations by the others' behaviors. …”

Section: Coping With Behavior Alternation Of Othersmentioning

confidence: 99%