Neuromorphic Event-Based Slip Detection and suppression in Robotic Grasping and Manipulation
This addresses slip detection for robots in precision manipulation tasks, such as in industrial manufacturing and household services, but appears incremental as it builds on existing vision-based methods with new features like real-time noise sampling.
The paper tackles slip detection in robotic grasping by proposing a dynamic vision-based finger system that detects incipient slip events at a 2kHz sampling rate and suppresses them before gross slip occurs, using a fuzzy-based strategy with incipient slip feedback.
Slip detection is essential for robots to make robust grasping and fine manipulation. In this paper, a novel dynamic vision-based finger system for slip detection and suppression is proposed. We also present a baseline and feature based approach to detect object slips under illumination and vibration uncertainty. A threshold method is devised to autonomously sample noise in real-time to improve slip detection. Moreover, a fuzzy based suppression strategy using incipient slip feedback is proposed for regulating the grip force. A comprehensive experimental study of our proposed approaches under uncertainty and system for high-performance precision manipulation are presented. We also propose a slip metric to evaluate such performance quantitatively. Results indicate that the system can effectively detect incipient slip events at a sampling rate of 2kHz ($Δt = 500μs$) and suppress them before a gross slip occurs. The event-based approach holds promises to high precision manipulation task requirement in industrial manufacturing and household services.