Robot Model Identification and Learning: A Modern Perspective
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 …
highlighted the importance of accurate and reliable robot models. This trend has led to a …
Towards high-accuracy axial springback: Mesh-based simulation of metal tube bending via geometry/process-integrated graph neural networks
Springback has always been a stubborn defect that affects the axial accuracy of metal
bending. The finite element simulation of springback enables effective control and precise …
bending. The finite element simulation of springback enables effective control and precise …
Learning rigid dynamics with face interaction graph networks
Simulating rigid collisions among arbitrary shapes is notoriously difficult due to complex
geometry and the strong non-linearity of the interactions. While graph neural network (GNN) …
geometry and the strong non-linearity of the interactions. While graph neural network (GNN) …
Unifying predictions of deterministic and stochastic physics in mesh-reduced space with sequential flow generative model
Accurate prediction of dynamical systems in unstructured meshes has recently shown
successes in scientific simulations. Many dynamical systems have a nonnegligible level of …
successes in scientific simulations. Many dynamical systems have a nonnegligible level of …
Swarm reinforcement learning for adaptive mesh refinement
Abstract The Finite Element Method, an important technique in engineering, is aided by
Adaptive Mesh Refinement (AMR), which dynamically refines mesh regions to allow for a …
Adaptive Mesh Refinement (AMR), which dynamically refines mesh regions to allow for a …
Learning flexible body collision dynamics with hierarchical contact mesh transformer
Recently, many mesh-based graph neural network (GNN) models have been proposed for
modeling complex high-dimensional physical systems. Remarkable achievements have …
modeling complex high-dimensional physical systems. Remarkable achievements have …
Learned Neural Physics Simulation for Articulated 3D Human Pose Reconstruction
We propose a novel neural network approach to model the dynamics of articulated human
motion with contact. Our goal is to develop a faster and more convenient alternative to …
motion with contact. Our goal is to develop a faster and more convenient alternative to …
Object Dynamics Modeling with Hierarchical Point Cloud-based Representations
Modeling object dynamics with a neural network is an important problem with numerous
applications. Most recent work has been based on graph neural networks. However physics …
applications. Most recent work has been based on graph neural networks. However physics …
Learning 3D Particle-based Simulators from RGB-D Videos
Realistic simulation is critical for applications ranging from robotics to animation. Traditional
analytic simulators sometimes struggle to capture sufficiently realistic simulation which can …
analytic simulators sometimes struggle to capture sufficiently realistic simulation which can …
Simultaneous learning of contact and continuous dynamics
Robotic manipulation can greatly benefit from the data efficiency, robustness, and
predictability of model-based methods if robots can quickly generate models of novel objects …
predictability of model-based methods if robots can quickly generate models of novel objects …