The recent advent of compliant and kinematically redundant robots poses new research challenges for human-robot interaction. While these robots provide a great degree of flexibility for the realization of complex applications, the flexibility gained generates the need for additional modeling steps and definition of criteria for redundancy resolution constraining the robot's movement generation. The explicit modeling of such criteria usually require experts to adapt the robot's movement generation subsystem. A typical way of dealing with this configuration challenge is to utilize kinesthetic teaching by guiding the robot to implicitly model the specific constraints in task and configuration space. We argue that current programming-by-demonstration approaches are not efficient for kinesthetic teaching of redundant robots and show that typical teach-in procedures are too complex for novice users. In order to enable non-experts to master the configuration and programming of a redundant robot in the presence of non-trivial constraints such as confined spaces, we propose a new interaction scheme combining kinesthetic teaching and learning within an integrated system architecture. We evaluated this approach in a user study with 49 industrial workers at HARTING, a medium-sized manufacturing company. The results show that the interaction concepts implemented on a KUKA Lightweight Robot IV are easy to handle for novice users, demonstrate the feasibility of kinesthetic teaching for implicit constraint modeling in configuration space, and yield significantly improved performance for the teach-in of trajectories in task space.

Abstract. Scene categorization is an important mechanism for providing high-level context which can guide methods for a more detailed analysis of scenes. State-of-the-art techniques like Torralba's Gist features show a good performance on categorizing outdoor scenes but have problems in categorizing indoor scenes. In contrast to object based approaches, we propose a 3D feature vector capturing general properties of the spatial layout of indoor scenes like shape and size of extracted planar patches and their orientation to each other. This idea is supported by psychological experiments which give evidence for the special role of 3D geometry in categorizing indoor scenes. In order to study the influence of the 3D geometry we introduce in this paper a novel 3D indoor database and a method for defining 3D features on planar surfaces extracted in 3D data. Additionally, we propose a voting technique to fuse 3D features and 2D Gist features and show in our experiments a significant contribution of the 3D features to the indoor scene categorization task.

The paper introduces an online user study on applications for social robots with 127 participants. The potential users proposed 570 application seLnarios based on the appearance and functionality of four robots presented (AIBO, BARTIOC, BIRON, iCat). The items were grouped into 13 categories which are interpreted and discussed by means of four dimensions: public vs. private use, intensity of interaction, complexity of interaction model, and functional vs. human-like appearance. The interpretation lead to three classes of applications for social robots according to the degree of social interaction: (1) Specialized Applications where the robot has to perform clearly defined tasks which are delegated by a user, (2) Public Applications which are directed to the communication with many users, and (3) Individual Applications with the need of a highly elaborated social model to maintain a variety of situations with few people.

This paper focuses on two aspects of a human robot interaction scenario: Detection and tracking of moving objects, e.g., persons is necessary for localizing possible interaction partners and reconstruction of the surroundings can be used for navigation purposes and room categorization. Although these processes can be addressed independent from each other, we show that using the available data in exchange enables a more exact reconstruction of the static scene. A 6D data representation consisting of 3D Time-ofFlight (ToF) Sensor data and computed 3D velocities allows segmenting the scene into clusters with consistent velocities. A weak object model is applied to localize and track objects within a particle filter framework. As a consequence, points emerging from moving objects can be neglected during reconstruction. Experiments demonstrate enhanced reconstruction results in comparison to pure bottom-up methods, especially for very short image sequences.

Human-robot collaboration requires both communicative and decision making skills of a robot. To enable flexible coordination and turn-taking between human users and a robot in joint tasks, the robot's dialog and decision making mechanism have to be synchronized in a meaningful way. In this paper, we propose a integration framework to combine the dialog and the decision making processes. With this framework, we investigate various task negotiation situations for a social robot in a fetch-and-carry scenario. For the technical realization of the framework, the interface specification between the dialog and the decision making systems is also presented. Further, we discuss several challenging issues identified in our integration effort that should be adddressed in the future.