Stabilizing novel objects by learning to predict tactile slip

During grasping and other in-hand manipulation tasks maintaining a stable grip on the object is crucial for the task's outcome. Inherently connected to grip stability is the concept of slip. Slip occurs when the contact between the fingertip and the object is partially lost, resulting in sudden undesired changes to the objects state. While several approaches for slip detection have been proposed in the literature, they frequently rely on previous knowledge of the manipulated object. This previous knowledge may be unavailable, seeing that robots operating in real-world scenarios often must interact with previously unseen objects. In our work we explore the generalization capabilities of well known supervised learning methods, using random forest classifiers to create generalizable slip predictors. We utilize these classifiers in the feedback loop of an object stabilization controller. We show that the controller can successfully stabilize previously unknown objects by predicting and counteracting slip events.