Learning to Detect Slip through Tactile Measures of the Contact Force Field and its Entropy

Slip detection in object handling is crucial and traditionally relies on visual cues. However, for optimal performance, artificial tactile sensing is needed, especially with unfamiliar objects. This study introduces a real-time, physicsinformed, data-driven method for continuous slip detection using the GelSight Mini optical tactile sensor. The sensor’s inhomogeneity during slip events helps create unique features and recasts slip detection as a classification task. Tested on ten diverse objects, the best model achieved 99% accuracy. The work’s practical use was demonstrated in a dynamic robotic manipulation task incorporating real-time slip detection and prevention.

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