AIM: Adaptive Interaction Modeling Networks for Real-to-Sim Soft-Body Simulation
cs.RO
Submitted: 2026-10-05
Updated: 2026-10-05
Sources
- Relational inductive biases, deep learning, and graph networks
- Message Passing Neural PDE Solvers
- MultiScale MeshGraphNets
- DiffTaichi: Differentiable Programming for Physical Simulation
- SoMA: A Real-to-Sim Neural Simulator for Robotic Soft-body Manipulation
- DeformMaster: An Interactive Physics-Neural World Model for Deformable Objects from Videos
- Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids
- Learning Physics-Guided Residual Dynamics for Deformable Object Simulation
- Learning Mesh-Based Simulation with Graph Networks
- AdaptiGraph: Material-Adaptive Graph-Based Neural Dynamics for Robotic Manipulation
- Particle-Grid Neural Dynamics for Learning Deformable Object Models from RGB-D Videos
- Dynamic 3D Gaussian Tracking for Graph-Based Neural Dynamics Modeling
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