Robots are widely employed in industrial settings owing to their efficiency, flexibility, and
extensive operational ranges. However, their application in high-precision scenarios is …

Process drift leads to an out-of-distribution problem between historical training data and
online deployment data, which deteriorates the performance of soft sensors. Unfortunately …

Due to their inherent characteristics, robots inevitably suffer from pose errors, and accurate
prediction is the key to error compensation, which facilitates the application of robots in high …

Accurate tool wear state prediction during machining is essential for lowering production
costs and ensuring quality. Conventional deep learning-based methods perform excellently …

While industrial robots are widely used in various fields owing to their large workspace and
high flexibility, significant errors constrain their application in high-precision scenarios …

In recent years, robotic machining has become one of the most important paradigms for the
machining of large and complex parts due to the advantages of large workspaces and …

A comprehensive understanding of blast load information is crucial for evaluating structural
response. Machine learning has demonstrated its efficiency in predicting blast loads …

Error prediction and compensation are crucial requirements for improving robot accuracy. To
this end, this paper introduces an advanced online method that integrates error-motion …