5 papers
Improving Long-Range Interactions in Graph Neural Simulators via Hamiltonian Dynamics
Tai Hoang, Alessandro Trenta, Alessio Gravina +4
Learning to simulate complex physical systems from data has emerged as a promising way to overcome the limitations of traditional numerical solvers, which often require prohibitive…
Context-aware Learned Mesh-based Simulation via Trajectory-Level Meta-Learning
Philipp Dahlinger, Niklas Freymuth, Tai Hoang +4
Simulating object deformations is a critical challenge across many scientific domains, including robotics, manufacturing, and structural mechanics. Learned Graph Network Simulators…
MaNGO - Adaptable Graph Network Simulators via Meta-Learning
Philipp Dahlinger, Tai Hoang, Denis Blessing +2
Accurately simulating physics is crucial across scientific domains, with applications spanning from robotics to materials science. While traditional mesh-based simulations are prec…
Geometry-aware RL for Manipulation of Varying Shapes and Deformable Objects
Tai Hoang, Huy Le, Philipp Becker +2
Manipulating objects with varying geometries and deformable objects is a major challenge in robotics. Tasks such as insertion with different objects or cloth hanging require precis…
Enhancing Exploration with Diffusion Policies in Hybrid Off-Policy RL: Application to Non-Prehensile Manipulation
Huy Le, Tai Hoang, Miroslav Gabriel +2
Learning diverse policies for non-prehensile manipulation is essential for improving skill transfer and generalization to out-of-distribution scenarios. In this work, we enhance ex…