Cloak: Zero-Shot Cross-Embodiment Manipulation by Masking the End-Effector from the VLA
cs.RO
Submitted: 2026-06-22
Updated: 2026-09-25
Code: https://github.com/kevinzakka/mink
License: http://creativecommons.org/licenses/by/4.0/
The gist: We present Cloak, a training recipe that endows a Vision-Language-Action (VLA) model with zero-shot cross-embodiment transfer by cloaking the end-effector from its own wrist camera.
Terminology
Abstract
We present Cloak, a training recipe that endows a Vision-Language-Action (VLA) model with zero-shot cross-embodiment transfer by cloaking the end-effector from its own wrist camera. The end-effector occupies a large and consistent region of the wrist view and masking it allows for embodiment-agnostic visual reasoning. Cloak renders a mask in simulation from the robot's known geometry, accurately and in real time, with no segmentation or generative models. During training, we augment the mask so the model generalizes to embodiments unseen at training time. We demonstrate the recipe with Cloak-VLA, a VLA trained with Cloak on a single parallel-jaw gripper dataset. No data of new embodiments is ever collected. Cloak-VLA transfers zero-shot to various unseen embodiments, including another gripper, another arm, and a five-fingered hand, while preserving the source embodiment's performance. By decoupling the wrist view from its own embodiment, Cloak allows data to outlive the hardware it was collected on.
Sources
- Open X-Embodiment: Robotic Learning Datasets and RT-X Models
- OpenVLA: An Open-Source Vision-Language-Action Model
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model
- Scaling Cross-Embodied Learning: One Policy for Manipulation, Navigation, Locomotion and Aviation
- RDT2: Exploring the Scaling Limit of UMI Data Towards Zero-Shot Cross-Embodiment Generalization
- LAP: Language-Action Pre-Training Enables Zero-shot Cross-Embodiment Transfer
- EmbodiSwap for Zero-Shot Robot Imitation Learning
- OXE-AugE: A Large-Scale Robot Augmentation of OXE for Scaling Cross-Embodiment Policy Learning
- Augmented Reality for RObots (ARRO): Pointing Visuomotor Policies Towards Visual Robustness
- One Hand to Rule Them All: Canonical Representations for Unified Dexterous Manipulation
- One-Policy-Fits-All: Geometry-Aware Action Latents for Cross-Embodiment Manipulation
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