  With an increasing demand for robots, robotic grasping will has a more
important role in future applications. This paper takes grasp stability
prediction as the key technology for grasping and tries to solve the problem
with time series data inputs including the force and pressure data. Widely
applied to more fields to predict unstable grasping with time series data,
algorithms can significantly promote the application of artificial intelligence
in traditional industries. This research investigates models that combine
short-time Fourier transform (STFT) and long short-term memory (LSTM) and then
tested generalizability with dexterous hand and suction cup gripper. The
experiments suggest good results for grasp stability prediction with the force
data and the generalized results in the pressure data. Among the 4 models,
(Data + STFT) & LSTM delivers the best performance. We plan to perform more
work on grasp stability prediction, generalize the findings to different types
of sensors, and apply the grasp stability prediction in more grasping use cases
in real life.
