# Scaling Cross-Embodiment World Models for Dexterous Manipulation

arXiv: https://arxiv.org/abs/2511.01177 · HTML: https://mlsanity.com/paper/2511.01177/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Placed in Embodied and robot world models, one of 11 directions in World models.
- Builds on 9 of its 63 references that are in the map.
- First posted on arXiv 3 Nov 2025.

## Placed in

- World models / [Embodied and robot world models](https://mlsanity.com/wm/embodied-and-robot-world-models/)

## Builds on (most cited first, 9 of 9)

- [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://mlsanity.com/paper/2307.15818/) (2023)
- [OpenVLA: An Open-Source Vision-Language-Action Model](https://mlsanity.com/paper/2406.09246/) (2024)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [π0: A Vision-Language-Action Flow Model for General Robot Control](https://mlsanity.com/paper/2410.24164/) (2024)
- [SAPIEN: A SimulAted Part-based Interactive ENvironment](https://mlsanity.com/paper/2003.08515/) (2020)
- [Learning Interactive Real-World Simulators](https://mlsanity.com/paper/2310.06114/) (2023)
- [EgoVLA: Learning Vision-Language-Action Models from Egocentric Human Videos](https://mlsanity.com/paper/2507.12440/) (2025)
- [RoboCraft: Learning to See, Simulate, and Shape Elasto-Plastic Objects with Graph Networks](https://mlsanity.com/paper/2205.02909/) (2022)
- [Enhancing Generalization in Vision-Language-Action Models by Preserving Pretrained Representations](https://mlsanity.com/paper/2509.11417/) (2025)

## Built on by (most cited first, 13 of 13)

- [π0.7: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities](https://mlsanity.com/paper/2604.15483/) (2026)
- [PointWorld: Scaling 3D World Models for In-The-Wild Robotic Manipulation](https://mlsanity.com/paper/2601.03782/) (2026)
- [EgoVerse: An Egocentric Human Dataset for Robot Learning from Around the World](https://mlsanity.com/paper/2604.07607/) (2026)
- [World Models for Learning Dexterous Hand-Object Interactions from Human Videos](https://mlsanity.com/paper/2512.13644/) (2025)
- [Wow, wo, val! A Comprehensive Embodied World Model Evaluation Turing Test](https://mlsanity.com/paper/2601.04137/) (2026)
- [PhysCoRe: Physics-Corrected Residual World Models for Material-Aware Deformable Dynamics](https://mlsanity.com/paper/2607.20653/) (2026)
- [Rapid Embodiment Adaptation for Quadrupedal Locomotion](https://mlsanity.com/paper/2608.01506/) (2026)
- [SyncWorld: Visual Calibration Enables World Models as Zero-Shot Simulators](https://mlsanity.com/paper/2609.09155/) (2026)
- [BRICKS-WM: Building Reusability via Interface Composition Kinetics for Structured World Models](https://mlsanity.com/paper/2606.16489/) (2026)
- [Graph-Operator World Models for Morphology-Parameter Generalization in Continuous Control](https://mlsanity.com/paper/2608.20936/) (2026)
- [ProxiDex: Learning Dynamics-Guided Proximity Policy for Dexterous Manipulation](https://mlsanity.com/paper/2609.16586/) (2026)
- [Intelligence Across Embodiments](https://mlsanity.com/paper/2609.27095/) (2026)
- [World Motion Models: Flexible Sequence Modeling of SE(3) Trajectories](https://mlsanity.com/paper/2610.01742/) (2026)

## Abstract (arXiv)

Cross-embodiment learning seeks to build generalist robots that learn from and operate across diverse morphologies, but differences in kinematics and action spaces hinder data sharing and control transfer. We ask: What structure can be shared across embodiments despite these differences? We argue that the physical interactions they induce can be modeled in a shared geometric space, allowing world models to provide a common interface for learning and control. To realize this idea, we represent human and robot hands as sets of 3D particles and define actions as end-effector particle displacement fields. This representation abstracts away embodiment-specific joint spaces while preserving the geometry and motion relevant to physical interaction. We train a graph-based world model on random interaction data from diverse simulated robot hands and real human hands, and integrate it with model-predictive control for deployment on new hardware. Experiments on rigid and deformable manipulation reveal three findings: increasing the diversity of training embodiments improves generalization to unseen hands; appropriately combining simulated and real-world data outperforms either source alone; and the same learned model enables effective control on robotic hands with distinct kinematics and degrees of freedom. These results position particle-based world models as a shared interface for learning from and for heterogeneous embodiments.