Abstract-If robots are to succeed in novel tasks, they must be able to learn from humans. To improve such humanrobot interaction, a system is presented that provides dialog structure and engages the human in an exploratory teaching scenario. Thereby, we specifically target untrained users, who are supported by mixed-initiative interaction using verbal and non-verbal modalities. We present the principles of dialog structuring based on an object learning and manipulation scenario. System development is following an interactive evaluation approach and we will present both an extensible, eventbased interaction architecture to realize mixed-initiative and evaluation results based on a video-study of the system. We show that users benefit from the provided dialog structure to result in predictable and successful human-robot interaction.

Grasping and manual interaction for robots so far has largely been approached with an emphasis on physics and control aspects. Given the richness of human manual interaction, we argue for the consideration of the wider field of "manual intelligence" as a perspective for manual action research that brings the cognitive nature of human manual skills to the foreground. We briefly sketch part of a research agenda along these lines, argue for the creation of a manual interaction database as an important cornerstone of such an agenda, and describe the manual interaction lab recently set up at CITEC to realize this goal and to connect the efforts of robotics and cognitive science researchers towards making progress for a more integrated understanding of manual intelligence. From Robots to Manual IntelligenceProgress in mechatronics, sensing and control has made sophisticated robot hands possible whose potential for dexterous operation is at least beginning to approach the superb performance of human hands [1][2][3]. The increasing availability of these hands, together with sophisticated, physicsbased simulation software, has spurred a revival of the field of anthropomorphic hand control in robotics, whose ultimate goal is to replicate the abilities of human hands to handle everyday objects in flexible ways and in unprepared environments.The authors are cooperating within the Bielefeld Excellence Cluster Cognitive Interaction Technology (CITEC) and the Bielefeld Institute for Cognition and Robotics (CoR-Lab).

Abstract-We present an algorithm to segment an unstructured table top scene. Operating on the depth image of a Kinect camera, the algorithm robustly separates objects of previously unknown shape in cluttered scenes of stacked and partially occluded objects. The model-free algorithm finds smooth surface patches which are subsequently combined to form object hypotheses. We evaluate the algorithm regarding its robustness and real-time capabilities and discuss its advantages compared to existing approaches as well as its weak spots to be addressed in future work. We also report on an autonomous grasping experiment with the Shadow Robot Hand which employs the estimated shape and pose of segmented objects.

Abstract-The ability to manipulate deformable objects, such as textiles or paper, is a major prerequisite to bringing the capabilities of articulated robot hands closer to the level of manual intelligence exhibited by humans. We concentrate on the manipulation of paper, which affords us a rich interaction domain that has not yet been solved for anthropomorphic robot hands. Robust tracking and physically plausible modeling of the paper as well as feedback based robot control are crucial components for this task. This paper makes two novel contributions to this area. The first concerns real-time modeling and visual tracking. Our technique not only models the bending of a sheet of paper, but also paper crease lines which allows us to monitor deformations. The second contribution concerns enabling an anthropomorphic robot to fold paper, and is accomplished by introducing a set of tactile-and vision-based closed loop controllers.

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