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In Programming by Demonstration (PbD), one of the key problems for autonomous learning is to automatically extract the relevant features of a manipulation task, which has a significant impact on the generalization capabilities. In this paper, task features are encoded as constraints of a learned planning model. In order to extract the relevant constraints, the human teacher demonstrates a set of tests,...
Human Motion Capture (HMC) is an active topic of research with applications in diverse domains. The robotics community is in particular interested in methods which allow the tracking of human movements on autonomous robotic systems with their constrained perception and processing capabilities. One approach for such a tracking is based on the Iterative Closest Points (ICP) algorithm. A specific problem...
This paper presents a description logic based system to store and retrieve knowledge used in models for autonomous probabilistic decision making by multimodal service robots. These models are mainly generated by observation and analysis of humans performing tasks, the programming by demonstration methodology. As formal model representation, partially observable Markov decision processes (POMDPs) are...
In Programming by Demonstration, abstract manipulation knowledge has to be learned, that can be used by an autonomous robot system in different environments with arbitrary obstacles. In this work, manipulation strategies are learned by observation of a human teacher and represented as a flexible, constraint-based representation of the search space for motion planning. The learned manipulation strategy...
In this paper we propose a process which is able to generate abstract service robot mission representations, utilized during execution for autonomous, probabilistic decision making, by observing human demonstrations. The observation process is based on the same perceptive components as used by the robot during execution, recording dialog between humans, human motion as well as objects poses. This...
This paper presents a technique to learn flexible action selection in autonomous, multi-modal human-robot interaction (HRI) from observing multi-modal human-human interaction (HHI). A model is generated using the proposed technique with symbolic states and actions, representing the scope of the observed mission. Variations in human behavior can be learned as stochastic action effects while execution...
The planning of grasping motions is demanding due to the complexity of modern robot systems. In Programming by Demonstration, the observation of a human teacher allows to draw additional information about grasping strategies. Rosell showed, that the motion planning problem can be simplified by globally restricting the set of valid configurations to a learned subspace. In this work, the transformation...
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