Perceiving and handling deformable objects is an integral part of everyday life for humans.
Automating tasks such as food handling, garment sorting, or assistive dressing requires …

The use of simulators in robotics research is widespread, underpinning the majority of recent
advances in the field. There are now more options available to researchers than ever before …

Large language models (LLMs) are shown to possess a wealth of actionable knowledge that
can be extracted for robot manipulation in the form of reasoning and planning. Despite the …

Mesh-based simulations are central to modeling complex physical systems in many
disciplines across science and engineering. Mesh representations support powerful …

Here we present a machine learning framework and model implementation that can learn to
simulate a wide variety of challenging physical domains, involving fluids, rigid solids, and …

We develop a general approach to distill symbolic representations of a learned deep model
by introducing strong inductive biases. We focus on Graph Neural Networks (GNNs). The …

Understanding crowd motion dynamics is critical to real-world applications, eg, surveillance
systems and autonomous driving. This is challenging because it requires effectively …

Particle physics is a branch of science aiming at discovering the fundamental laws of matter
and forces. Graph neural networks are trainable functions which operate on graphs—sets of …

Representing robotic manipulation tasks as constraints that associate the robot and the
environment is a promising way to encode desired robot behaviors. However, it remains …

One of the main challenges in using deep learning-based methods for simulating physical
systems and solving partial differential equations (PDEs) is formulating physics-based data …