We consider the problem of grasp and manipulation planning when the state of the world is only partially observable. Specifically, we address the task of picking up unknown objects from a table top. The proposed approach to object shape prediction aims at closing the knowledge gaps in the robot's understanding of the world. A completed state estimate of the environment can then be provided to a simulator in which stable grasps and collision-free movements are planned.The proposed approach is based on the observation that many objects commonly in use in a service robotic scenario possess symmetries. We search for the optimal parameters of these symmetries given visibility constraints. Once found, the point cloud is completed and a surface mesh reconstructed.Quantitative experiments show that the predictions are valid approximations of the real object shape. By demonstrating the approach on two very different robotic platforms its generality is emphasized.

For the interpretation of a visual scene, it is important for a robotic system to pay attention to the objects in the scene and segment them from their background. We focus on the segmentation of previously unseen objects in unknown scenes. The attention model therefore needs to be bottom-up and context-free. In this paper, we propose the use of symmetry, one of the Gestalt principles for figure-ground segregation, to guide the robot's attention. We show that our symmetry-saliency model outperforms the contrast-saliency model, proposed in [4]. The symmetry model performs better in finding the objects of interest and selects a fixation point closer to the center of the object. Moreover, the objects are better segmented from the background when the initial points are selected on the basis of symmetry.

Abstract. We propose a framework for detecting, extracting and modeling objects in natural scenes from multi-modal data. Our framework is iterative, exploiting different hypotheses in a complementary manner. We employ the framework in realistic scenarios, based on visual appearance and depth information. Using a robotic manipulator that interacts with the scene, object hypotheses generated using appearance information are confirmed through pushing. The framework is iterative, each generated hypothesis is feeding into the subsequent one, continuously refining the predictions about the scene. We show results that demonstrate the synergic effect of applying multiple hypotheses for real-world scene understanding. The method is efficient and performs in real-time.

For a person to feel comfortable when interacting with a robot, it is necessary for it to behave in an expected way. This should of course be the case during the actual interaction, but is in one sense even more important during the time preceding it. If someone is uncertain of what to expect from a robot when looking at it from a distance and approaching it, the robot's behavior can make the difference between that person choosing to interact or not. People's behaviors around a robot not reacting to them were observed during a field trial. Based on those observations people were classified into four groups depending on their estimated interest in interacting with the robot. People were tracked with a laser range finder based system, and their position, direction of motion and speed were estimated. A second classification based on that information was made. The two classifications were then mapped to each other. Different actions were then created for the robot to be able to react naturally to different human behaviors. In this thesis three different robot behaviors in a crowded environment are evaluated with respect to how natural they appear. With one behavior the robot actively tried to engage people, with one it passively indicated that people had been noticed and with the third behavior it made random gestures. During an experiment, test subjects were instructed to act according to the groups from the classification based on interest, and the robot's performance was evaluated with regard to how natural it appeared. Both first-and third-person evaluations made clear that the active and passive behavior were considered equally natural, while a robot randomly making gestures was considered much less natural.

No abstract