Grasp stability prediction with time series data based on STFT and LSTM

TL;DR

This paper proposes a novel approach combining STFT and LSTM to predict grasp stability using force and pressure time series data, demonstrating promising results and generalizability across different grippers.

Contribution

It introduces a combined STFT and LSTM model for grasp stability prediction from time series data, showing improved performance and generalizability over existing methods.

Findings

Best model (Data + STFT) & LSTM outperforms others

Good generalization with pressure data

Effective in different grasping tools

Abstract

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.