Segmentation and modeling of visually symmetric objects by robot actions

Robots usually carry out object segmentation and modelling 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 modelling 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 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 robotic experiments are available from Multimedia Extensions 1, 2 and 3.