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Abstract

Robots usually carry out object segmentation and modeling passively. Sensors such as cameras are actuated by a robot without disturbing objects in the scene. In this paper, we present an intelligent robotic system that physically moves objects in an active manner to perform segmentation and modeling using vision. By visually detecting bilateral symmetry, our robot is able to segment and model objects through controlled physical interactions. Extensive experiments show that our robot is able to accurately segment new objects autonomously. We also show that our robot is able to leverage segmentation results to autonomously learn visual models of new objects by physically grasping and rotating them. Object recognition experiments confirm that the robot-learned models allow robust recognition. Videos of the robotic experiments are also made available.

Keywords

Autonomous computer vision fast symmetry grasping interactive learning object manipulation object recognition real time robotics segmentation SIFT

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Segmentation and modeling of visually symmetric objects by robot actions

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