Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks

Rearranging and manipulating deformable objects such as cables, fabrics, and bags is a long-standing challenge in robotic manipulation. The complex dynamics and high-dimensional configuration spaces of deformables, compared to rigid objects, make manipulation difficult not only for multi-step planning, but even for goal specification. Goals cannot be as easily specified as rigid object poses, and may involve complex relative spatial relations such as "place the item inside the bag". In this work, we develop a suite of simulated benchmarks with 1D, 2D, and 3D deformable structures, including tasks that involve image-based goal-conditioning and multi-step deformable manipulation. We propose embedding goal-conditioning into Transporter Networks, a recently proposed model architecture for learning robotic manipulation that rearranges deep features to infer displacements that can represent pick and place actions. We demonstrate that goal-conditioned Transporter Networks enable agents to manipulate deformable structures into flexibly specified configurations without test-time visual anchors for target locations. We also significantly extend prior results using Transporter Networks for manipulating deformable objects by testing on tasks with 2D and 3D deformables. Supplementary material is available at https://berkeleyautomation.github.io/bags/.Comment: See https://berkeleyautomation.github.io/bags/ for project website and code; v2 corrects some BibTeX entries, v3 is ICRA 2021 version (minor revisions

Signorini conditions for inviscid fluids

In this thesis, we present a new type of boundary condition for the simulation of inviscid fluids – the Signorini boundary condition. The new condition models the non-sticky contact of a fluid with other fluids or solids. Euler equations with Signorini boundary conditions are analyzed using variational inequalities. We derived the weak form of the PDEs, as well as an equivalent optimization based formulation. We proposed a finite element method to numerically solve the Signorini problems. Our method is based on a staggered grid and a level set representation of the fluid surfaces, which may be plugged into an existing fluid solver. We implemented our algorithm and tested it with some 2D fluid simulations. Our results show that the Signorini boundary cpndition successfully models some interesting contact behavior of fluids, such as the hydrophobic contact and the non-coalescence phenomenon