Print Email Facebook Twitter Active vision via extremum seeking for robots in unstructured environments Title Active vision via extremum seeking for robots in unstructured environments: Applications in object recognition and manipulation Author Calli, B. (Yale University) Caarls, W. (TU Delft Robot Dynamics; PUC-Rio) Wisse, M. (TU Delft Robot Dynamics) Jonker, P.P. (TU Delft Biomechatronics & Human-Machine Control) Date 2018 Abstract In this paper, a novel active vision strategy is proposed for optimizing the viewpoint of a robot's vision sensor for a given success criterion. The strategy is based on extremum seeking control (ESC), which introduces two main advantages: 1) Our approach is model free: It does not require an explicit objective function or any other task model to calculate the gradient direction for viewpoint optimization. This brings new possibilities for the use of active vision in unstructured environments, since a priori knowledge of the surroundings and the target objects is not required. 2) ESC conducts continuous optimization backed up with mechanisms to escape from local maxima. This enables an efficient execution of an active vision task. We demonstrate our approach with two applications in the object recognition and manipulation fields, where the model-free approach brings various benefits: for object recognition, our framework removes the dependence on offline training data for viewpoint optimization, and provides robustness of the system to occlusions and changing lighting conditions. In object manipulation, the model-free approach allows us to increase the success rate of a grasp synthesis algorithm without the need of an object model; the algorithm only uses continuous measurements of the objective value, i.e., the grasp quality. Our experiments show that continuous viewpoint optimization can efficiently increase the data quality for the underlying algorithm, while maintaining the robustness. Subject Active visionArtificial neural networksextremum seeking control (ESC)graspingmanipulation.Object recognitionobject recognitionOptimizationRobot sensing systemsRobustnessTask analysis To reference this document use: http://resolver.tudelft.nl/uuid:9bb61413-4e46-430b-8ced-b2c0e488f091 DOI https://doi.org/10.1109/TASE.2018.2807787 Embargo date 2019-03-08 ISSN 1545-5955 Source IEEE Transactions on Automation Science and Engineering, 15 (4), 1810-1822 Bibliographical note Accepted Author Manuscript Part of collection Institutional Repository Document type journal article Rights © 2018 B. Calli, W. Caarls, M. Wisse, P.P. Jonker Files PDF 08310020.pdf 3.54 MB Close viewer /islandora/object/uuid:9bb61413-4e46-430b-8ced-b2c0e488f091/datastream/OBJ/view