Grasp Stability Prediction with Time Series Data Based on STFT and LSTM

With the increasing demand for robots, robotic grasping will play a increasingly important role in future applications. This paper takes grasp stability prediction as the key technology for grasping and attempts to solve the problem using time series data inputs, including the force and pressure data. By applying algorithms to predict unstable grasping using time series data in more fields, we 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 tests their generalizability using a dexterous hand and a suction cup gripper. The experiments suggest good results for grasp stability prediction using the force data and generalized results using pressure data. Among the four models tested, the (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.