Service robots that should operate autonomously need to perform actions reliably, and be able to adapt to their changing environment using learning mechanisms. Optimally, robots should learn continuously but this approach often suffers from problems like over-fitting, drifting or dealing with incomplete data. In this paper, we propose a method to automatically validate autonomously acquired perception models. These perception models are used to localize objects in the environment with the intention of manipulating them with the robot. Our approach verifies the learned perception models by moving the robot, trying to re-detect an object and then to grasp it. From observable failures of these actions and highlevel loop-closures to validate the eventual success, we can derive certain qualities of our models and our environment. We evaluate our approach by using two different detection algorithms, one using 2D RGB data and one using 3D point clouds. We show that our system is able to improve the perception performance significantly by learning which of the models is better in a certain situation and a specific context. We show how additional validation allows for successful continuous learning. The strictest precondition for learning such perceptual models is correct segmentation of objects which is evaluated in a second experiment.

In this paper we propose a novel approach to detect and reconstruct transparent objects. This approach makes use of the fact that many transparent objects, especially the ones consisting of usual glass, absorb light in certain wavelengths [1]. Given a controlled illumination, this absorption is measurable in the intensity response by comparison to the background. We show the usage of a standard infrared emitter and the intensity sensor of a time of flight (ToF) camera to reconstruct the structure given we have a second view point. The structure can not be measured by the usual 3D measurements of the ToF camera. We take advantage of this fact by deriving this internal sensory contradiction from two ToF images and reconstruct an approximated surface of the original transparent object. Therefor we are using a perspectively invariant matching in the intensity channels from the first to the second view of initially acquired candidates. For each matched pixel in the first view a 3D movement can be predicted given their original 3D measurement and the known distance to the second camera position. If their line of sight did not pass a transparent object or suffered any other major defect, this prediction will highly correspond to the actual measured 3D points of the second view. Otherwise, if a detectable error occurs, we approximate a more exact point to point matching and reconstruct the original shape by triangulating the points in the stereo setup. We tested our approach using a mobile platform with one Swissranger SR4k. As this platform is mobile, we were able to create a stereo setup by moving it. Our results show a detection of transparent objects on tables while simultaneously identifying opaque objects that also existed in the test setup. The viability of our results is demonstrated by a successful automated manipulation of the respective transparent object.

Robotic grasping in an open environment requires both object-specific as well as general grasping skills. When the objects are previously known it is possible to employ techniques that exploit object models, like geometrical grasping simulators. On the other hand, a competent system will also be able to deal with unmodeled objects using general solutions. In this paper we present an integrated system for autonomous rigid-object pick-up tasks in domestic environments, focusing on the gripping of unmodeled objects and exploiting sensor feedback from the robot hand to monitor the grasp. We describe the perception system based on time-of-flight range data, the grasp pose optimization algorithm and the grasp execution. The performance and robustness of the system is validated by experiments including pick-up tasks on many different common kitchen items.

We propose a new method for automatically accessing an internet database of 3D models that are searchable only by their user-annotated labels, for using them for vision and robotic manipulation purposes. Instead of having only a local database containing already seen objects, we want to use shared databases available over the internet. This approach while having the potential to dramatically increase the visual recognition capability of robots, also poses certain problems, like wrong annotation due to the open nature of the database, or overwhelming amounts of data (many 3D models) or the lack of relevant data (no models matching a specified label). To solve those problems we propose the following: First, we present an outlier/inlier classification method for reducing the number of results and discarding invalid 3D models that do not match our query. Second, we utilize an approach from computer graphics, the so called ‘morphing’, to this application to specialize the models, in order to describe more objects. Third, we search for 3D models using a restricted search space, as obtained from our knowledge of the environment. We show our classification and matching results and finally show how we can recover the correct scaling with the stereo setup of our robot.

In this paper, we investigate the problem of 3D object categorization of objects typically present in kitchen environments, from data acquired using a composite sensor. Our framework combines different sensing modalities and defines descriptive features in various spaces for the purpose of learning good object models. By fusing the 3D information acquired from a composite sensor that includes a color stereo camera, a time-of-flight (TOF) camera, and a thermal camera, we augment 3D depth data with color and temperature information which helps disambiguate the object categorization process. We make use of statistical relational learning methods (Markov Logic Networks and Bayesian Logic Networks) to capture complex interactions between the different feature spaces. To show the effectiveness of our approach, we analyze and validate the proposed system for the problem of recognizing objects in table settings scenarios.

This paper introduces the assistive kitchen as a comprehensive demonstration and challenge scenario for technical cognitive systems. We describe its hardware and software infrastructure. Within the assistive kitchen application, we select particular domain activities as research subjects and identify the cognitive capabilities needed for perceiving, interpreting, analyzing, and executing these activities as research foci. We conclude by outlining open research issues that need to be solved to realize the scenarios successfully.