Fuzzy rule based neuro-dynamic programming for mobile robot skill acquisition on the basis of a nested multi-agent architecture

Karigiannis, JN; Rekatsinas, TI; Tzafestas, CS

dc.contributor.author	Karigiannis, JN	en
dc.contributor.author	Rekatsinas, TI	en
dc.contributor.author	Tzafestas, CS	en
dc.date.accessioned	2014-03-01T02:46:48Z
dc.date.available	2014-03-01T02:46:48Z
dc.date.issued	2010	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32864
dc.subject	Developmental robotics	en
dc.subject	Multi-agent architectures	en
dc.subject	Neuro-dynamic learning	en
dc.subject.other	A-plane	en
dc.subject.other	Biologically inspired	en
dc.subject.other	Biologically-inspired robots	en
dc.subject.other	Box-pushing	en
dc.subject.other	Collaborative control	en
dc.subject.other	Continuous spaces	en
dc.subject.other	Control framework	en
dc.subject.other	Developmental robotics	en
dc.subject.other	Distributed agents	en
dc.subject.other	Dynamic learning	en
dc.subject.other	Effective learning	en
dc.subject.other	Experimental studies	en
dc.subject.other	Function approximators	en
dc.subject.other	Fuzzy rule based	en
dc.subject.other	Independent agents	en
dc.subject.other	Intelligent behavior	en
dc.subject.other	Intrinsic behavior	en
dc.subject.other	Living organisms	en
dc.subject.other	Multi-agent approach	en
dc.subject.other	Multi-agent architectures	en
dc.subject.other	Multiagent architecture	en
dc.subject.other	Neuro dynamic programming	en
dc.subject.other	Neuro-dynamic learning	en
dc.subject.other	No-contact	en
dc.subject.other	Organizational structures	en
dc.subject.other	Research communities	en
dc.subject.other	Robotic systems	en
dc.subject.other	Self-organizations	en
dc.subject.other	Skill acquisition	en
dc.subject.other	Skill learning process	en
dc.subject.other	Skill transfer	en
dc.subject.other	Specific problems	en
dc.subject.other	State-space	en
dc.subject.other	Task models	en
dc.subject.other	Theoretical framework	en
dc.subject.other	Architecture	en
dc.subject.other	Biology	en
dc.subject.other	Biomimetics	en
dc.subject.other	Computation theory	en
dc.subject.other	Dynamic programming	en
dc.subject.other	Hierarchical systems	en
dc.subject.other	Intelligent robots	en
dc.subject.other	Machine design	en
dc.subject.other	Mobile agents	en
dc.subject.other	Mobile robots	en
dc.subject.other	Multi agent systems	en
dc.subject.other	Robot programming	en
dc.subject.other	Robotics	en
dc.subject.other	Intelligent agents	en
dc.title	Fuzzy rule based neuro-dynamic programming for mobile robot skill acquisition on the basis of a nested multi-agent architecture	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ROBIO.2010.5723346	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ROBIO.2010.5723346	en
heal.identifier.secondary	5723346	en
heal.publicationDate	2010	en
heal.abstract	Biologically inspired architectures that mimic the organizational structure of living organisms and in general frameworks that will improve the design of intelligent robots attract significant attention from the research community. Self-organization problems, intrinsic behaviors as well as effective learning and skill transfer processes in the context of robotic systems have been significantly investigated by researchers. Our work presents a new framework of developmental skill learning process by introducing a hierarchical nested multi-agent architecture. A neuro-dynamic learning mechanism employing function approximators in a fuzzified state-space is utilized, leading to a collaborative control scheme among the distributed agents engaged in a continuous space, which enables the multi-agent system to learn, over a period of time, how to perform sequences of continuous actions in a cooperative manner without any prior task model. The agents comprising the system manage to gain experience over the task that they collaboratively perform by continuously exploring and exploiting their state-to-action mapping space. For the specific problem setting, the proposed theoretical framework is employed in the case of two simulated e-Puck robots performing a collaborative box-pushing task. This task involves active cooperation between the robots in order to jointly push an object on a plane to a specified goal location. We should note that 1) there are no contact points specified for the two e-Pucks and 2) the shape of the object is indifferent. The actuated wheels of the mobile robots are considered as the independent agents that have to build up cooperative skills over time, in order for the robot to demonstrate intelligent behavior. Our goal in this experimental study is to evaluate both the proposed hierarchical multi-agent architecture, as well as the methodological control framework. Such a hierarchical multi-agent approach is envisioned to be highly scalable for the control of complex biologically inspired robot locomotion systems. © 2010 IEEE.	en
heal.journalName	2010 IEEE International Conference on Robotics and Biomimetics, ROBIO 2010	en
dc.identifier.doi	10.1109/ROBIO.2010.5723346	en
dc.identifier.spage	312	en
dc.identifier.epage	319	en