Differentiable Robotic Manipulation of Deformable Rope-like Objects Using Compliant Position-based Dynamics
This work addresses the challenge of modeling deformable materials for robotic applications such as autonomous suturing, offering a model-based method for optimization problems, though it is incremental in improving existing simulation techniques.
The paper tackled the problem of robot manipulation of rope-like objects by introducing a differentiable, compliant position-based dynamics model that handles shear/stretch and bend/twist effects, demonstrating robustness and accuracy in real-to-sim experiments with robots like Baxter and DVRK.
Robot manipulation of rope-like objects is an interesting problem that has 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 be used to solve a large variety of 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). Our work leads to a new path for robotic manipulation of the deformable rope-like object taking advantage of the ready-to-use gradients.