Learning to Detect Slip through Tactile Estimation of the Contact Force Field and its Entropy
This work addresses the problem of improving robotic manipulation proficiency, especially with unfamiliar objects, by integrating tactile sensing for real-time slip detection, representing a novel method for a known bottleneck.
The paper tackles slip detection during robotic grasping by introducing a physics-informed, data-driven approach using tactile sensor data, achieving a high average accuracy of 95.61% in classification tests across various objects and conditions.
Detection of slip during object grasping and manipulation plays a vital role in object handling. Existing solutions primarily rely on visual information to devise a strategy for grasping. However, for robotic systems to attain a level of proficiency comparable to humans, especially in consistently handling and manipulating unfamiliar objects, integrating artificial tactile sensing is increasingly essential. We introduce a novel physics-informed, data-driven approach to detect slip continuously in real time. We employ the GelSight Mini, an optical tactile sensor, attached to custom-designed grippers to gather tactile data. Our work leverages the inhomogeneity of tactile sensor readings during slip events to develop distinctive features and formulates slip detection as a classification problem. To evaluate our approach, we test multiple data-driven models on 10 common objects under different loading conditions, textures, and materials. Our results show that the best classification algorithm achieves a high average accuracy of 95.61%. We further illustrate the practical application of our research in dynamic robotic manipulation tasks, where our real-time slip detection and prevention algorithm is implemented.