Learning to Walk with Model Assisted Evolution Strategies

Many algorithms in robotics contain parameterized models. The setting of the parameters in general has a strong impact on the quality of the model. Finding a parameter set which optimizes the quality of the model typically is a challenging task, especially if the structure of the problem is unknown and can not be specified mathematically, i.e. the only way to ge

06251 Executive Summary -- Multi-Robot Systems: Perception, Behaviors, Learning, and Action

The Dagstuhl Seminar on Multi-Robot Systems (06251) was held in June 20-23, 2006. It had the goal to bring researchers together from different areas of robotics to discuss current research topics on autonomous and interacting robots. The technical focus was on perception, behaviors, learning, and action. The seminar took directly place after the RoboCup robot soccer competitions and the subsequent symposium in Bremen. Thus researchers from many different countries were able to join the seminar and address issues without taking into account upcoming competitions or events

Learning in a high dimensional space: Fast omnidirectional quadrupedal locomotion

Abstract. This paper presents an efficient way to learn fast omnidirectional quadrupedal walking gaits. We show that the common approaches to control the legs can be further improved by allowing more degrees of freedom in the trajectory generation for the legs. To achieve good omnidirectional movements, we suggest to use different parameters for different walk requests and interpolate between them. The approach has been implemented for the Sony Aibo and used by the GermanTeam in the Four-Legged-League in 2005. A standard learning strategy has been adopted, so that the optimization process of a parameter set can be done within one hour, without human intervention. The resulting walk achieved remarkable speeds, both in pure forward walking and in omnidirectional movements.

DoH!Bots Team Description for RoboCup 2007

The DoH!Bots (Dortmund Humanoid Robots) is a team of researchers and students from Dortmund University. Our group took part in the humanoid league for the first time at RoboCup 2006, as part of the team BreDoBrothers which was a cooperation with Bremen University. This year both groups want to compet

Learning Fast Walking Patterns with Reliable Odometry Information for Four-Legged Robots

In this paper we describe a way to control and learn walking patters for a four-legged robot which result in very fast and stable omnidirectional walks with accurate odometry information. The fastest forward walk which was learned on a Sony Aibo ERS 7 with this approach reaches more than 50 cm/s. This is more than 25 % faster than the fastest published walk found by any RoboCup team so far. The fast and manoeuvrable walk contributed a lot to the good overall performance of our team and helped to win all attended RoboCup competitions in 2005. I

Cooperative visual tracking in a team of autonomous mobile robots

Robot soccer is a challenging domain for sensor fusion and object tracking techniques, due to its team oriented, fast-paced, dynamic and competitive nature. Since each robot has a limited view about the world surrounding it, the sharing of information with its teammates is often crucial in order to be ready to react to situations which might involve it in the near future. In this paper we propose a Particle Filter based approach that addresses the problem of cooperative global sensor fusion by explicitly modeling the uncertainty concerning the robots’ positions, the data association about the tracked object, and the loss of information over the network

Robust color classification for robot soccer

Abstract. This paper presents an adaptive approach to improve the reliability and performance of color classification. Therefore, we transform the camera-data from YUV into a novel chrominance space. Thereby, an optimized transformation function is given by an evolutionary algorithm. The novel idea is, not to adapt the thresholds that define a specific color region, but to evolve an optimal chrominance space transformation. In the novel chrominance space, the color regions are located in easy-toseparate subspaces which reduces the algorithmic complexity of color segmentation and improves classification accuracy significantly.

Real-Time Structure Preserving Image Noise Reduction For Computer Vision On

A camera is often the main sensor of autonomous robots. As those embedded platforms o#er minor computing power only, it is a challenge to provide a fast and robust image processing. This paper describes a new real-time structure preserving noise reduction operator based on the so-called SUSAN filtering approach. We use a correlation function to determine which part of a pixel's neighborhood has to be included in the smoothing process. Thus, the smoothing process takes place only inside homogeneous regions of the image, without blurring edges and bi-dimensional features which are needed for object recognition

P.: BreDoBrothers - Team Description for RoboCup 2006

The BreDoBrothers are a joint team of researchers and students from the Universität Bremen and the Universität Dortmund. Members of both teams have a common track record in the GermanTeam [1–4], which has become world champion in the Four-Legged League twice

GermanTeam 2004 - The German National RoboCup Team

This paper gives an overview on the work done by the four sub-teams of the GermanTeam (Aibo Team Humboldt, Darmstadt Dribbling Dackels, Bremen Byters, and Microsoft Hellhounds) in the past year that is currently combined to form the code of the GermanTeam 2004. Further information can be found in this year's contributions of members of the GermanTeam to the RoboCup book: they deal with automatic color calibration [2], object recognition [3], obstacle avoidance [4] (cf. Sect. 3), collision detection [5] (cf. Sect. 4), qualitative world modeling [6, 7], behavior modeling [8], and gait optimization [9] (cf. Sect. 7