Day: March 8, 2012

The movement of autonomous agents in natural environments is restricted by potentially large numbers of constraints. To generate behavior that fulfills all given constraints simultaneously, the attractor dynamics approach to movement generation represents each constraint by a dynamical system with attractors or repellors at desired or undesired values of a relevant variable. These dynamical systems … Read More “ICRA 2011: Integrating Orientation Constraints Into the Attractor Dynamics Approach for Autonomous Manipulation” »

Autonomous robots with limited computational capacity call for control approaches that generate meaningful, goal-directed behavior without using a large amount of resources. The attractor dynamics approach to movement generation is a framework that links sensor data to motor commands via coupled dynamical systems that have attractors at behaviorally desired states. The low computational demands leave … Read More “IROS 2011: Model-free local navigation for humanoid robots (submitted)” »

In the presented work we compare machine learn- ing techniques in the context of lane change behavior performed by humans in a semi-naturalistic simulated environment. We evaluate different learning approaches using differing feature combinations in order to identify appropriate feature, best fea- ture combination, and the most appropriate machine learning technique for the described task. … Read More “Robio2011: Autonomous Driving: A Comparison of Machine Learning Techniques by Means of the Prediction of Lane Change Behavior” »

Simulated reality environment incorporating humans and physically plausible behaving robots, providing nat- ural interaction channels, with the option to link simulator to real perception and motion, is gaining importance for the development of cognitive, intuitive interacting and collaborating robotic systems. In the present work we introduce a head tracking system which is utilized to incorporate … Read More “Robio2011: Simulated reality environment for development and assessment of cognitive robotic systems” »