mlsanityData to 14 Oct 2026

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

arXiv 2407.15002 ↗First posted 20 Jul 2024cs.ROcited by 27

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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

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

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 10; 2026: 14.

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  1. 2025FLOWER: Democratizing Generalist Robot Policies with Efficient Vision-Language-Action Flow Policiescited by 57
  2. 2024From Multimodal LLMs to Generalist Embodied Agents: Methods and Lessonscited by 42
  3. 2024Cross-Embodiment Dexterous Grasping with Reinforcement Learningcited by 27
  4. 2026LAP: Language-Action Pre-Training Enables Zero-shot Cross-Embodiment Transfercited by 18
  5. 2024The One RING: a Robotic Indoor Navigation Generalistcited by 16
  6. 2025Towards Embodiment Scaling Laws in Robot Locomotioncited by 16
  7. 2025UMI-on-Air: Embodiment-Aware Guidance for Embodiment-Agnostic Visuomotor Policiescited by 11
  8. 2025GCNT: Graph-Based Transformer Policies for Morphology-Agnostic Reinforcement Learningcited by 7
  9. 2025AnyBody: A Benchmark Suite for Cross-Embodiment Manipulationcited by 6
  10. 2025Beyond Topology: A Morphological Symmetry Graph Representation for Locomotion Policy Learningcited by 4
  11. 2025Embodied Co-Design for Rapidly Evolving Agents: Taxonomy, Frontiers, and Challengescited by 4
  12. 2025Shared Modular Recurrence in Contextual MDPs for Universal Morphology Controlcited by 3
15 more
  1. 2025MachaGrasp: Morphology-Aware Cross-Embodiment Dexterous Hand Articulation Generation for Graspingcited by 3
  2. 2026UniMorphGrasp: Diffusion Model with Morphology-Awareness for Cross-Embodiment Dexterous Grasp Generationcited by 2
  3. 2026Self-evolving Embodied AIcited by 2
  4. 2026Shape Your Body: Value Gradients for Multi-Embodiment Robot Designcited by 2
  5. 2026Grounding Generative Policies in Physics: Optimization-Guided Diffusion for Robot Controlcited by 2
  6. 2025Knowledge Diversion for Efficient Morphology Control and Policy Transfercited by 1
  7. 2026KITE: Decoupling Kinematics and Interaction for Zero-Shot Cross-Embodiment Manipulationcited by 1
  8. 2026Rapid Embodiment Adaptation for Quadrupedal Locomotioncited by 1
  9. 2026The Embodiment Gap in Robot Foundation Modelscited by 1
  10. 2026HRO: Hierarchical Room-to-Object Framework for Zero-Shot Object Goal Navigation with Large Language Modelscited by 0
  11. 2026Gripper-aware Vision Language Action Modelscited by 0
  12. 2026Intelligence Across Embodimentscited by 0
  13. 2026CrossSafe: Towards Cross-Embodiment Latent Safety Filterscited by 0
  14. 2026Bridging Body and Brain: Gene-Driven Morphology--Control Co-Designcited by 0
  15. 2026GOTT: Object-centric Dexterous Manipulation with a Reusable Cross-Embodiment Primitivecited by 0