  Grasp verification is advantageous for autonomous manipulation robots as they
provide the feedback required for higher level planning components about
successful task completion. However, a major obstacle in doing grasp
verification is sensor selection. In this paper, we propose a vision based
grasp verification system using machine vision cameras, with the verification
problem formulated as an image classification task. Machine vision cameras
consist of a camera and a processing unit capable of on-board deep learning
inference. The inference in these low-power hardware are done near the data
source, reducing the robot's dependence on a centralized server, leading to
reduced latency, and improved reliability. Machine vision cameras provide the
deep learning inference capabilities using different neural accelerators.
Although, it is not clear from the documentation of these cameras what is the
effect of these neural accelerators on performance metrics such as latency and
throughput. To systematically benchmark these machine vision cameras, we
propose a parameterized model generator that generates end to end models of
Convolutional Neural Networks(CNN). Using these generated models we benchmark
latency and throughput of two machine vision cameras, JeVois A33 and Sipeed
Maix Bit. Our experiments demonstrate that the selected machine vision camera
and the deep learning models can robustly verify grasp with 97% per frame
accuracy.
