  Grasping specific objects in complex and irregularly stacked scenes is still
challenging for robotics. Because the robot is not only required to identify
the object's grasping posture but also needs to reason the manipulation
relationship between the objects. In this paper, we propose a manipulation
relationship reasoning network with a multi-scale feature aggregation (MSFA)
mechanism for robot grasping tasks. MSFA aggregates high-level semantic
information and low-level spatial information in a cross-scale connection way
to improve the generalization ability of the model. Furthermore, to improve the
accuracy, we propose to use intersection features with rich location priors for
manipulation relationship reasoning. Experiments are validated in VMRD datasets
and real environments, respectively. The experimental results demonstrate that
our proposed method can accurately predict the manipulation relationship
between objects in the scene of multi-object stacking. Compared with previous
methods, it significantly improves reasoning speed and accuracy.
