RISeg: Robot Interactive Object Segmentation via Body Frame-Invariant
  Features
Howard H. Qian Yangxiao Lu Kejia Ren Gaotian Wang Ninad Khargonkar Yu Xiang Kaiyu Hang
In order to successfully perform manipulation tasks in new environments, such
as grasping, robots must be proficient in segmenting unseen objects from the
background and/or other objects. Previous works perform unseen object instance
segmentation (UOIS) by training deep neural networks on large-scale data to
learn RGB/RGB-D feature embeddings, where cluttered environments often result
in inaccurate segmentations. We build upon these methods and introduce a novel
approach to correct inaccurate segmentation, such as under-segmentation, of
static image-based UOIS masks by using robot interaction and a designed body
frame-invariant feature. We demonstrate that the relative linear and rotational
velocities of frames randomly attached to rigid bodies due to robot
interactions can be used to identify objects and accumulate corrected
object-level segmentation masks. By introducing motion to regions of
segmentation uncertainty, we are able to drastically improve segmentation
accuracy in an uncertainty-driven manner with minimal, non-disruptive
interactions (ca. 2-3 per scene). We demonstrate the effectiveness of our
proposed interactive perception pipeline in accurately segmenting cluttered
scenes by achieving an average object segmentation accuracy rate of 80.7%, an
increase of 28.2% when compared with other state-of-the-art UOIS methods.