Active Vision via Extremum Seeking for Robots in Unstructured Environments: Applications in Object Recognition and Manipulation

• Published in: IEEE transactions on automation science and engineering 2018-10, Vol.15 (4), p.1810-1822
• Main Authors: Calli, Berk, Caarls, Wouter, Wisse, Martijn, Jonker, Pieter P.
• Format: Article
• Language: English
• Subjects: Active vision; Algorithms; Artificial neural networks; Dependence; Domains; extremum seeking control (ESC); Grasping; manipulation; Maxima; Object recognition; Optimization; Quality; Robot sensing systems; Robots; Robustness; Sensors; Success; Task analysis; Viewing; Vision; Vision sensors
• ISSN: 1545-5955; 1558-3783
• DOI: 10.1109/TASE.2018.2807787

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. Note to Practitioners -Vision sensors provide robots flexibility and robustness both in industrial and domestic settings by supplying required data to analyze the surroundings and the state of the task. However, the quality of these data can be very high or poor depending on the viewing angle of the vision sensor. For example, if the robot aims to recognize an object, images taken from certain angles (e.g., feature rich surfaces) can be more descriptive than the others, or if the robot's goal is to manipulate an object, observing it from a viewpoint that reveals easy-to-grasp "handles" makes the task simpler to execute. The algorithm presented in this paper aims to provide the robot high quality visual data relative to the task at hand by changing vision sensors' viewpoint. Different from other methods in the literature, our method does not require any task models (therefore, it is model free), and only utilizes a quality value that can be measured from the current viewp