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Multi-task learning has shown considerable promise for improving theperformance of deep learning-driven vision systems for the purpose of roboticgrasping. However, high architectural and computational complexity can resultin poor suitability for deployment on embedded devices that are typicallyleveraged in robotic arms for real-world manufacturing and warehouseenvironments. As such, the design of highly efficient multi-task deep neuralnetwork architectures tailored for computer vision tasks for robotic graspingon the edge is highly desired for widespread adoption in manufacturingenvironments. Motivated by this, we propose Fast GraspNeXt, a fastself-attention neural network architecture tailored for embedded multi-tasklearning in computer vision tasks for robotic grasping. To build FastGraspNeXt, we leverage a generative network architecture search strategy with aset of architectural constraints customized to achieve a strong balance betweenmulti-task learning performance and embedded inference efficiency. Experimentalresults on the MetaGraspNet benchmark dataset show that the Fast GraspNeXtnetwork design achieves the highest performance (average precision (AP),accuracy, and mean squared error (MSE)) across multiple computer vision taskswhen compared to other efficient multi-task network architecture designs, whilehaving only 17.8M parameters (about >5x smaller), 259 GFLOPs (as much as >5xlower) and as much as >3.15x faster on a NVIDIA Jetson TX2 embedded processor.

Fast GraspNeXt: A Fast Self-Attention Neural Network Architecture for Multi-task Learning in Computer Vision Tasks for Robotic Grasping on the Edge