arXiv (All)
open access
PhysCoRe: Physics-Corrected Residual World Models for Material-Aware Deformable Dynamics
Predicting how deformable objects evolve under robotic manipulation is a longstanding challenge. Existing approaches typically rely on per-object optimization to fit material parameters, which can be slow and cannot generalize, while end-to-end learned alternatives extrapolate poorly and often viola
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Facet-0: A Robotic Foundation Model for Contact-Rich Precise Manipulation#821617
- Generalizing Soft Tissue Deformation and Force Prediction Across Material Stiffness and Geometry#662153
- Generalizing Soft Tissue Deformation and Force Prediction Across Material Stiffness and Geometry#661103
- Efficient and Scalable Physics-Guided Fully Convolutional Spatiotemporal Learning for 3D Microstructure Evolution Prediction#1131705
- ContactWorld: What Representations Matter in Vision-Tactile World Models for Contact-Rich Manipulation#985130
Record · ID 1013629
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive