Differentiable simulators provide an avenue for closing the sim-to-real gap by enabling the
use of efficient, gradient-based optimization algorithms to find the simulation parameters that …

In recent years, the increasing complexity and safety-critical nature of robotic tasks have
highlighted the importance of accurate and reliable robot models. This trend has led to a …

We propose a method that leverages graph neural networks, multi-level message passing,
and unsupervised training to enable real-time prediction of realistic clothing dynamics …

We present a fast and feature-complete differentiable physics engine, Nimble
(nimblephysics. org), that supports Lagrangian dynamics and hard contact constraints for …

Given monocular videos, we build 3D models of articulated objects and environments
whose 3D configurations satisfy dynamics and contact constraints. At its core, our method …

The study of hand-object interaction requires generating viable grasp poses for high-
dimensional multi-finger models, often relying on analytic grasp synthesis which tends to …

Abstract Learning from Demonstration (LfD) is an efficient technique for robots to acquire
new skills through expert observation, significantly mitigating the need for laborious manual …

We present a novel, fast differentiable simulator for soft-body learning and control
applications. Existing differentiable soft-body simulators can be classified into two categories …

Simulated virtual environments serve as one of the main driving forces behind developing
and evaluating skill learning algorithms. However, existing environments typically only …

We extend the incremental potential contact (IPC) model for contacting elastodynamics to
resolve systems composed of codimensional DOFs in arbitrary combination. This enables a …