A transformer with prediction-correction and hierarchical super-token merging unifies simulation of six physical dynamics categories on Lagrangian particles and generalizes to unseen conditions.
, author Grinspun, E
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SIM1 converts sparse real demonstrations into high-fidelity synthetic data through physics-aligned simulation, yielding policies that match real-data performance at a 1:15 ratio with 90% zero-shot success on deformable manipulation.
A new geometric optimization method generates synchronized deployment trajectories for elastic geodesic grids by approximating node paths from inverse tracing and solving a non-smooth polyline problem to drive stable finite element simulations.
A systematic literature survey that categorizes deep learning architectures for point cloud classification, part segmentation, and semantic segmentation, evaluates them on benchmarks, and discusses innovations, limitations, and future directions.
citing papers explorer
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WorldParticle: Unified World Simulation of Lagrangian Particle Dynamics via Transformer
A transformer with prediction-correction and hierarchical super-token merging unifies simulation of six physical dynamics categories on Lagrangian particles and generalizes to unseen conditions.
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SIM1: Physics-Aligned Simulator as Zero-Shot Data Scaler in Deformable Worlds
SIM1 converts sparse real demonstrations into high-fidelity synthetic data through physics-aligned simulation, yielding policies that match real-data performance at a 1:15 ratio with 90% zero-shot success on deformable manipulation.
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Geometric Guidance for Globally Synchronized Deployment of Elastic Geodesic Grids
A new geometric optimization method generates synchronized deployment trajectories for elastic geodesic grids by approximating node paths from inverse tracing and solving a non-smooth polyline problem to drive stable finite element simulations.
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A Systematic Survey on Deep Learning Architectures for Point Cloud Classification and Segmentation
A systematic literature survey that categorizes deep learning architectures for point cloud classification, part segmentation, and semantic segmentation, evaluates them on benchmarks, and discusses innovations, limitations, and future directions.