Search for: All records

Shape servoing, a robotic task dedicated to controlling objects to desired goal shapes, is a promising approach to deformable object manipulation. An issue arises, however, with the reliance on the specification of a goal shape. This goal has been obtained either by a laborious domain knowledge engineering process or by manually manipulating the object into the desired shape and capturing the goal shape at that specific moment, both of which are impractical in various robotic applications. In this paper, we solve this problem by developing a novel neural network DefGoalNet, which learns deformable object goal shapes directly from a small number of human demonstrations. We demonstrate our method’s effectiveness on various robotic tasks, both in simulation and on a physical robot. Notably, in the surgical retraction task, even when trained with as few as 10 demonstrations, our method achieves a median success percentage of nearly 90%. These results mark a substantial advancement in enabling shape servoing methods to bring deformable object manipulation closer to practical real-world applications. 

This article extends recent work in magnetic manipulation of conductive, nonmagnetic objects using rotating magnetic dipole fields. Eddy-current-based manipulation provides a contact-free way to manipulate metallic objects. We are particularly motivated by the large amount of aluminum in space debris. We previously demonstrated dexterous manipulation of solid spheres with all object parameters known a priori. This work expands the previous model, which contained three discrete modes, to a continuous model that covers all possible relative positions of the manipulated spherical object with respect to the magnetic field source. We further leverage this new model to examine manipulation of spherical objects with unknown physical parameters by applying techniques from the online-optimization and adaptive-control literature. Our experimental results validate our new dynamics model, showing that we get improved performance compared to the previously proposed model, while also solving a simpler optimization problem for control. We further demonstrate the first physical magnetic manipulation of aluminum spheres, as previous controllers were only physically validated on copper spheres. We show that our adaptive control framework can quickly acquire useful object parameters when weakly initialized. Finally, we demonstrate that the spherical-object model can be used as an approximate model for adaptive control of nonspherical objects by performing magnetic manipulation of a variety of objects for which a spherical model is not an obvious approximation.