2010 |
John N Karigiannis, Theodoros I Rekatsinas, Costas S Tzafestas Hierarchical Multi-Agent Architecture employing TD ( $łambda$ ) Learning with Function Approximators for Robot Skill Acquisition Conference Architecture, 2010. @conference{36b, title = {Hierarchical Multi-Agent Architecture employing TD ( $łambda$ ) Learning with Function Approximators for Robot Skill Acquisition}, author = { John N Karigiannis and Theodoros I Rekatsinas and Costas S Tzafestas}, year = {2010}, date = {2010-01-01}, booktitle = {Architecture}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
John N. Karigiannis, Theodoros I. Rekatsinas, Costas S. Tzafestas Fuzzy rule based neuro-dynamic programming for mobile robot skill acquisition on the basis of a nested multi-agent architecture Conference 2010 IEEE International Conference on Robotics and Biomimetics, ROBIO 2010, 2010, ISBN: 9781424493173. @conference{34b, title = {Fuzzy rule based neuro-dynamic programming for mobile robot skill acquisition on the basis of a nested multi-agent architecture}, author = { John N. Karigiannis and Theodoros I. Rekatsinas and Costas S. Tzafestas}, doi = {10.1109/ROBIO.2010.5723346}, isbn = {9781424493173}, year = {2010}, date = {2010-01-01}, booktitle = {2010 IEEE International Conference on Robotics and Biomimetics, ROBIO 2010}, pages = {312--319}, 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.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } 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. |
Copyright Notice:
Some material presented is available for download to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
The work already published by the IEEE is under its copyright. Personal use of such material is permitted. However, permission to reprint/republish the material for advertising or promotional purposes, or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of the work in other works must be obtained from the IEEE.