# EMPM: Embodied MPM for Modeling and Simulation of Deformable Objects

arXiv: https://arxiv.org/abs/2601.17251 · HTML: https://mlsanity.com/paper/2601.17251/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2026.

## Built on by (most cited first, 9 of 9)

- [LLM-as-a-Verifier: A General-Purpose Verification Framework](https://mlsanity.com/paper/2607.05391/) (2026)
- [Agentic Real2Sim: Physics-based World Modeling with Vision-Language Agents](https://mlsanity.com/paper/2607.19190/) (2026)
- [DeformMaster: An Interactive Physics-Neural World Model for Deformable Objects from Videos](https://mlsanity.com/paper/2605.09586/) (2026)
- [SIM1: Physics-Aligned Simulator as Zero-Shot Data Scaler in Deformable Worlds](https://mlsanity.com/paper/2604.08544/) (2026)
- [MatPhys: Learning Material-Aware Physics Parameters for Deformable Object Simulation from Videos](https://mlsanity.com/paper/2605.19386/) (2026)
- [PhysCoRe: Physics-Corrected Residual World Models for Material-Aware Deformable Dynamics](https://mlsanity.com/paper/2607.20653/) (2026)
- [DiagGen: Agentic Generation of Deformable Assets with Sim-based Diagnostics for Robotic Simulation](https://mlsanity.com/paper/2609.23103/) (2026)
- [Deform360: A Massive Multi-view Visuotactile Dataset for Deformable World Models](https://mlsanity.com/paper/2607.05390/) (2026)
- [PhysReal: Learning Real-World Deformable Object Physics via Hybrid Constitutive Modeling](https://mlsanity.com/paper/2609.07532/) (2026)

## Abstract (arXiv)

Modeling deformable objects - especially continuum materials - in a way that is physically plausible, generalizable, and data-efficient remains challenging across 3D vision, graphics, and robotic manipulation. Many existing methods oversimplify the rich dynamics of deformable objects or require large training sets, which often limits generalization. We introduce embodied MPM (EMPM), a deformable object modeling and simulation framework built on a differentiable Material Point Method (MPM) simulator that captures the dynamics of challenging materials. From multi-view RGB-D videos, our approach reconstructs geometry and appearance, then uses an MPM physics engine to simulate object behavior by minimizing the mismatch between predicted and observed visual data. We further optimize MPM parameters online using sensory feedback, enabling adaptive, robust, and physics-aware object representations that open new possibilities for robotic manipulation of complex deformables. Experiments show that EMPM outperforms spring-mass baseline models. Project website: https://embodied-mpm.github.io.