Steffen Knoop

Human activity recognition is an essential ability for service robots and other robotic systems which are in interaction with human beings. To be proactive, the system must be able to evaluate the current state of the user it is dealing with. Also future surveillance systems will benefit from robust activity recognition if realtime constraints are met, allowing to automate tasks that have to be fulfilled by humans yet. In this paper, a thorough analysis of features and classifiers aimed at human activity recognition is presented. Based on a set of 10 activities, the use of different feature selection algorithms is evaluated, as well as the results different classifiers (SVMs, Neural Networks, Bayesian Classifiers) provide in this context. Also the interdependency between feature selection method and chosen classifier is investigated. Furthermore, the optimal number of features to be used for an activity is examined.

In this paper we present a framework for grasp planning with a humanoid robot arm and a five-fingered hand. The aim is to provide the humanoid robot with the ability of grasping objects that appear in a kitchen environment. Our approach is based on the use of an object model database that contains the description of all the objects that can appear in the robot workspace. This database is completed with two modules that make use of this object representation: an exhaustive offline grasp analysis system and a real-time stereo vision system. The offline grasp analysis system determines the best grasp for the objects by employing a simulation system, together with CAD models of the objects and the five-fingered hand. The results of this analysis are added to the object database using a description suited to the requirements of the grasp execution modules. A stereo camera system is used for a real-time object localization using a combination of appearance-based and model-based methods. The different components are integrated in a controller architecture to achieve manipulation task goals for the humanoid robot

This paper presents an approach for human activity recognition focusing on gestures in a teaching scenario, together with the setup and results of user studies on human gestures exhibited in unconstrained human-robot interaction (HRI). The user studies analyze several aspects: the distribution of gestures, relations, and characteristics of these gestures, and the acceptability of different gesture types in a human-robot teaching scenario. The results are then evaluated with regard to the activity recognition approach. The main effort is to bridge the gap between human activity recognition methods on the one hand and naturally occuring or at least acceptable gestures for HRI on the other. The goal is two-fold: to provide recognition methods with information and requirements on the characteristics and features of human activities in HRI, and to identify human preferences and requirements for the recognition of gestures in human-robot teaching scenarios

In this paper, we present a new approach for fusion of different measurements and sensors for 3D model based tracking. The underlying model of the tracked body is defined geometrically with generalized cylinders, which can hierarchically be connected by different kinds of joints. This results in an articulated body model with constrained kinematic degrees of freedom. The fusion approach incorporates this model knowledge together with the measurements, and tracks the target body iteratively with an extended iterative closest point approach. The resulting tracking system named VooDoo is used to track humans in a human-robot interaction (HRI) context. We only rely on sensors on board the robot, i.e. a color camera, a 3D time-of-flight camera and a laser range finder. The system runs in realtime (~ 20 Hz) and is able to robustly track a human in the vicinity of the robot. The pose and trajectory of the human interaction partner can then be used for haptic interaction like hand-overs, and for activity and gesture recognition