The Paderkicker Team: Autonomy in Realtime Environments

Richert, Willi; Kleinjohann, Bernd; Koch, Markus; Bruder, Alexander; Rose, Stefan; Adelt, Philipp

doi:10.1007/978-0-387-39362-9_8

Cited by 5 publications

(8 citation statements)

References 9 publications

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“…We plan to integrate also dynamic strategies in the architecture and apply it in our real-world testbed, the midsize soccer robots Paderkicker [7,12]. In our view the best solution is to integrate manually handcrafted modules for each layer for situations that are foreseeable by the designer at design time into that architecture and accompany those with learning algorithms at ever layer for situations or failures that are not foreseeable.…”

Section: Discussionmentioning

confidence: 99%

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Towards Robust Layered Learning

Richert

Kleinjohann

2007

Third International Conference on Autonomic and Autonomous Systems (ICAS'07)

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In his landmark work introducing layered learning Stone presented a new way of handling complex application domains suitable especially for mobile robots. We extend his framework by introducing robust layered learning -a framework that is able to handle system and environmental changes at every layer. We present first results of a lower level implementation of such a framework for mobile robots and discuss how all available sources of information regarding unforeseen changes can be integrated in such a framework in order to reach maximal robustness.

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Section: Discussionmentioning

confidence: 99%

“…4). To validate that the simulation results can be transferred to the real world as well we evaluated our approach on our real Paderkicker [12] (Fig. 5).…”

Section: Experimental Settingmentioning

confidence: 97%

Towards Robust Layered Learning

Richert

Kleinjohann

2007

Third International Conference on Autonomic and Autonomous Systems (ICAS'07)

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“…c) Finally, behavior based control of the robot as well as learning and imitation of robot behavior in groups.The software architecture presented below is based on the Paderkicker behavior system design. That work has been published in [11] and [9].…”

Section: B Paderkickermentioning

confidence: 92%

“…Additional ball handling grippers and a kicker device are used in robotic soccer applications. Details of the platform have been published in [9]- [11].…”

Section: B Paderkickermentioning

confidence: 99%

Demonstrating self-optimization using a heterogeneous robot group

Adelt

Kleinjohann

Herbrechtsmeier

et al. 2010

2010 8th IEEE International Conference on Industrial Informatics

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Self-optimization is a concept for mechatronic systems to leave open the choice among system objectives as a degree of freedom until runtime to allow better adaptation to changing system and environment conditions. Demonstration and knowledge transfer of the concept is not easy as the effects of it in mechatronic systems are hard to see in a complex system. To further spread the idea of self-optimization, an intuitive anchor is needed to make it easier to talk about the concept. Also the abstraction from technical details facilitates focusing on the concept. We have developed a multi-agent heterogeneous robotic demonstrator that allows showing the process of self-optimization on a timescale of minutes. The demonstrator decomposes the roles in a mechatronic system to robotic agents. An association between a function and the behavior of the robot is achieved. After having demonstrated the setup for expert and non-expert audiences we have seen the encouraging effect that discussions spin off easily and allow to spread the idea effectively. We present the concept of self-optimization, the behavior-based demonstrator scenario implemented using BeBot miniature and Paderkicker robots in an office environment.

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“…They were performed using the Player/Stage simulation environment [4] with a Pioneer2DX robot (Fig. 2) that has a morphology similar to our soccer robots Paderkicker [9]. Player is a server that connects robots, sensors, and control programs over a network.…”

Section: Experimental Settingmentioning

confidence: 99%

Increasing the Autonomy of Mobile Robots by On-line Learning Simultaneously at Different Levels of Abstraction

Richert

Luke

Nordmeyer

et al. 2008

Fourth International Conference on Autonomic and Autonomous Systems (ICAS'08)

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We present a framework that is able to handle system and environmental changes by learning autonomously at different levels of abstraction. It is able to do so in continuous and noisy environments by 1) an active strategy learning module that uses reinforcement learning and 2) a dynamically adapting skill module that proactively explores the robot's own action capabilities and thereby providing actions to the strategy module. We present results that show the feasibility of simultaneously learning low-level skills and high-level strategies in order to reach a goal while reacting to disturbances like hardware damages. Thereby, the robot drastically increases its overall autonomy.

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The Paderkicker Team: Autonomy in Realtime Environments

Cited by 5 publications

References 9 publications

Towards Robust Layered Learning

Towards Robust Layered Learning

Demonstrating self-optimization using a heterogeneous robot group

Increasing the Autonomy of Mobile Robots by On-line Learning Simultaneously at Different Levels of Abstraction

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