Robotic cutting of soft materials is critical for applications such as food processing,
household automation, and surgical manipulation. As in other areas of robotics, simulators …

Abstract We introduce Breaking Bad, a large-scale dataset of fractured objects. Our dataset
consists of over one million fractured objects simulated from ten thousand base models. The …

We introduce RoboNinja, a learning-based cutting system for multi-material objects (ie, soft
objects with rigid cores such as avocados or mangos). In contrast to prior works using open …

Automated shape repair approaches currently lack access to datasets that describe real-
world damaged geometry. We present Fantastic Breaks (and Where to Find Them …

We propose a hybrid neural network and physics framework for reduced-order modeling of
elastoplasticity and fracture. State-of-the-art scientific computing models like the Material …

In this paper, we propose a neural network-based approach for learning to represent the
behavior of plastic solid materials ranging from rubber and metal to sand and snow. Unlike …

High-accuracy, high-efficiency physics-based fluid-solid interaction is essential for reality
modeling and computer animation in online games or real-time Virtual Reality (VR) systems …

Recent years have witnessed the rapid deployment of numerous physics-based modeling
and simulation algorithms and techniques for fluids, solids, and their delicate coupling in …

Large-scale topological changes play a key role in capturing the fine debris of fracturing
virtual brittle material. Real-world, tough brittle fractures have dynamic branching behaviour …

We present a novel hybrid Lagrangian/Eulerian method for simulating inelastic flows that
generates high-quality particle distributions with adaptive volumes. At its core, our approach …