# GET-Zero: Graph Embodiment Transformer for Zero-shot Embodiment Generalization

arXiv: https://arxiv.org/abs/2407.15002 · HTML: https://mlsanity.com/paper/2407.15002/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2024.

## Built on by (most cited first, 27 of 27)

- [FLOWER: Democratizing Generalist Robot Policies with Efficient Vision-Language-Action Flow Policies](https://mlsanity.com/paper/2509.04996/) (2025)
- [From Multimodal LLMs to Generalist Embodied Agents: Methods and Lessons](https://mlsanity.com/paper/2412.08442/) (2024)
- [Cross-Embodiment Dexterous Grasping with Reinforcement Learning](https://mlsanity.com/paper/2410.02479/) (2024)
- [LAP: Language-Action Pre-Training Enables Zero-shot Cross-Embodiment Transfer](https://mlsanity.com/paper/2602.10556/) (2026)
- [The One RING: a Robotic Indoor Navigation Generalist](https://mlsanity.com/paper/2412.14401/) (2024)
- [Towards Embodiment Scaling Laws in Robot Locomotion](https://mlsanity.com/paper/2505.05753/) (2025)
- [UMI-on-Air: Embodiment-Aware Guidance for Embodiment-Agnostic Visuomotor Policies](https://mlsanity.com/paper/2510.02614/) (2025)
- [GCNT: Graph-Based Transformer Policies for Morphology-Agnostic Reinforcement Learning](https://mlsanity.com/paper/2505.15211/) (2025)
- [AnyBody: A Benchmark Suite for Cross-Embodiment Manipulation](https://mlsanity.com/paper/2505.14986/) (2025)
- [Beyond Topology: A Morphological Symmetry Graph Representation for Locomotion Policy Learning](https://mlsanity.com/paper/2512.00727/) (2025)
- [Embodied Co-Design for Rapidly Evolving Agents: Taxonomy, Frontiers, and Challenges](https://mlsanity.com/paper/2512.04770/) (2025)
- [Shared Modular Recurrence in Contextual MDPs for Universal Morphology Control](https://mlsanity.com/paper/2506.08630/) (2025)
- [MachaGrasp: Morphology-Aware Cross-Embodiment Dexterous Hand Articulation Generation for Grasping](https://mlsanity.com/paper/2510.06068/) (2025)
- [UniMorphGrasp: Diffusion Model with Morphology-Awareness for Cross-Embodiment Dexterous Grasp Generation](https://mlsanity.com/paper/2602.00915/) (2026)
- [Self-evolving Embodied AI](https://mlsanity.com/paper/2602.04411/) (2026)
- [Shape Your Body: Value Gradients for Multi-Embodiment Robot Design](https://mlsanity.com/paper/2606.00702/) (2026)
- [Grounding Generative Policies in Physics: Optimization-Guided Diffusion for Robot Control](https://mlsanity.com/paper/2606.24208/) (2026)
- [Knowledge Diversion for Efficient Morphology Control and Policy Transfer](https://mlsanity.com/paper/2512.09796/) (2025)
- [KITE: Decoupling Kinematics and Interaction for Zero-Shot Cross-Embodiment Manipulation](https://mlsanity.com/paper/2606.22113/) (2026)
- [Rapid Embodiment Adaptation for Quadrupedal Locomotion](https://mlsanity.com/paper/2608.01506/) (2026)
- [The Embodiment Gap in Robot Foundation Models](https://mlsanity.com/paper/2608.18433/) (2026)
- [HRO: Hierarchical Room-to-Object Framework for Zero-Shot Object Goal Navigation with Large Language Models](https://mlsanity.com/paper/2607.13072/) (2026)
- [Gripper-aware Vision Language Action Models](https://mlsanity.com/paper/2608.24603/) (2026)
- [Intelligence Across Embodiments](https://mlsanity.com/paper/2609.27095/) (2026)
- [CrossSafe: Towards Cross-Embodiment Latent Safety Filters](https://mlsanity.com/paper/2609.28984/) (2026)
- [Bridging Body and Brain: Gene-Driven Morphology--Control Co-Design](https://mlsanity.com/paper/2609.31329/) (2026)
- [GOTT: Object-centric Dexterous Manipulation with a Reusable Cross-Embodiment Primitive](https://mlsanity.com/paper/2610.03861/) (2026)

## Abstract (arXiv)

This paper introduces GET-Zero, a model architecture and training procedure for learning an embodiment-aware control policy that can immediately adapt to new hardware changes without retraining. To do so, we present Graph Embodiment Transformer (GET), a transformer model that leverages the embodiment graph connectivity as a learned structural bias in the attention mechanism. We use behavior cloning to distill demonstration data from embodiment-specific expert policies into an embodiment-aware GET model that conditions on the hardware configuration of the robot to make control decisions. We conduct a case study on a dexterous in-hand object rotation task using different configurations of a four-fingered robot hand with joints removed and with link length extensions. Using the GET model along with a self-modeling loss enables GET-Zero to zero-shot generalize to unseen variation in graph structure and link length, yielding a 20% improvement over baseline methods. All code and qualitative video results are on https://get-zero-paper.github.io