In a cluttered scene, an object is often occluded by other objects, and a robot cannot figure out what the object is and perceive its pose exactly. We assume that the robot is equipped with a depth sensor and given a database of 3D object models and their grasping poses, but yet there is uncertainty about object's class and pose. In this paper, we study the problem of how to predict the class and pose of an occluded object by carefully taking a sequence of observations. To find the best sequence of viewpoints by the robot, we construct hypotheses of the states of the target and occluding objects, and update our belief state as new observations come in. Every time selecting the next robot pose, we greedily choose the one that is expected to reduce the uncertainty the most. Based on the theoretical analysis of adaptive submodular maximization problems, this process is guaranteed to find a near-optimal sequence of robot poses in terms of observation and traverse costs. To validate the proposed method, we present simulation and robot experiments using a PR2.
['Sung-Kyun Kim', 'Maxim Likhachev']
Planning for grasp selection of partially occluded objects