Robotic Manipulation of Deformable Rope-Like Objects Using Differentiable Compliant Position-Based Dynamics

Robot manipulation of rope-like objects is an interesting problem with some critical applications, such as autonomous robotic suturing. Solving for and controlling rope is difficult due to the complexity of rope physics and the challenge of building fast and accurate models of deformable materials. While more data-driven approaches have become more popular for finding controllers that learn to do a single task, there is still a strong motivation for a model-based method that could solve many optimization problems. Towards this end, we introduced compliant position-based dynamics (XPBD) to model rope-like objects. Using geometric constraints, the model can represent the coupling of shear/stretch and bend/twist effects. Of crucial importance is that our formulation is differentiable, which can solve parameter estimation problems and improve the matching of rope physics to real-life scenarios (i.e., the real-to-sim problem). For the generality of rope-like objects, two different solvers are proposed to handle the inextensible and extensible effects of varied material stiffness for the rope. We demonstrate our framework's robustness and accuracy on real-to-sim experimental setups using the Baxter robot and the da Vinci research kit (DVRK) (D'Ettorre et al., 2021). Our work leads to a new path for robotic manipulation of the deformable rope-like object taking advantage of the ready-to-use gradients.

I. Introduction

Modeling and simulation of deformable objects have recently been of considerable interest in many robotic applications [2], [3], [4]. This includes grasping soft objects, [5] to manipulating soft tissue [6], [7], cloth [8], and even fluids [9], [10]. Among soft structures, deformable linear objects (DLOs), including rope-like objects, strings, cables, flexible beams, etc., are studied (cable routing [11], wire insertion [12], flexible rope [13], and knotting of surgical thread [14]). Recently, techniques involving visual servoing [15], latent dynamics learning [16], and adaptive estimation [17], have been of interest to the community for controlling DLOs.