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.