  Multi-task learning has shown considerable promise for improving the
performance of deep learning-driven vision systems for the purpose of robotic
grasping. However, high architectural and computational complexity can result
in poor suitability for deployment on embedded devices that are typically
leveraged in robotic arms for real-world manufacturing and warehouse
environments. As such, the design of highly efficient multi-task deep neural
network architectures tailored for computer vision tasks for robotic grasping
on the edge is highly desired for widespread adoption in manufacturing
environments. Motivated by this, we propose Fast GraspNeXt, a fast
self-attention neural network architecture tailored for embedded multi-task
learning in computer vision tasks for robotic grasping. To build Fast
GraspNeXt, we leverage a generative network architecture search strategy with a
set of architectural constraints customized to achieve a strong balance between
multi-task learning performance and embedded inference efficiency. Experimental
results on the MetaGraspNet benchmark dataset show that the Fast GraspNeXt
network design achieves the highest performance (average precision (AP),
accuracy, and mean squared error (MSE)) across multiple computer vision tasks
when compared to other efficient multi-task network architecture designs, while
having only 17.8M parameters (about >5x smaller), 259 GFLOPs (as much as >5x
lower) and as much as >3.15x faster on a NVIDIA Jetson TX2 embedded processor.
