  Grasping in cluttered scenes has always been a great challenge for robots,
due to the requirement of the ability to well understand the scene and object
information. Previous works usually assume that the geometry information of the
objects is available, or utilize a step-wise, multi-stage strategy to predict
the feasible 6-DoF grasp poses. In this work, we propose to formalize the 6-DoF
grasp pose estimation as a simultaneous multi-task learning problem. In a
unified framework, we jointly predict the feasible 6-DoF grasp poses, instance
semantic segmentation, and collision information. The whole framework is
jointly optimized and end-to-end differentiable. Our model is evaluated on
large-scale benchmarks as well as the real robot system. On the public dataset,
our method outperforms prior state-of-the-art methods by a large margin (+4.08
AP). We also demonstrate the implementation of our model on a real robotic
platform and show that the robot can accurately grasp target objects in
cluttered scenarios with a high success rate. Project link:
https://openbyterobotics.github.io/sscl
