  Recognizing objects in dense clutter accurately plays an important role to a
wide variety of robotic manipulation tasks including grasping, packing,
rearranging and many others. However, conventional visual recognition models
usually miss objects because of the significant occlusion among instances and
causes incorrect prediction due to the visual ambiguity with the high object
crowdedness. In this paper, we propose an interactive exploration framework
called Smart Explorer for recognizing all objects in dense clutters. Our Smart
Explorer physically interacts with the clutter to maximize the recognition
performance while minimize the number of motions, where the false positives and
negatives can be alleviated effectively with the optimal accuracy-efficiency
trade-offs. Specifically, we first collect the multi-view RGB-D images of the
clutter and reconstruct the corresponding point cloud. By aggregating the
instance segmentation of RGB images across views, we acquire the instance-wise
point cloud partition of the clutter through which the existed classes and the
number of objects for each class are predicted. The pushing actions for
effective physical interaction are generated to sizably reduce the recognition
uncertainty that consists of the instance segmentation entropy and multi-view
object disagreement. Therefore, the optimal accuracy-efficiency trade-off of
object recognition in dense clutter is achieved via iterative instance
prediction and physical interaction. Extensive experiments demonstrate that our
Smart Explorer acquires promising recognition accuracy with only a few actions,
which also outperforms the random pushing by a large margin.
