# Cloak: Zero-Shot Cross-Embodiment Manipulation by Masking the End-Effector from the VLA arXiv: https://arxiv.org/abs/2606.22836 · HTML: https://mlsanity.com/paper/2606.22836/ ## Key facts - Cited by 5 papers in the map (data through 14 Oct 2026). - Placed in Human-video and latent-action pretraining, one of 13 directions in Vision-language-action models. - Builds on 31 of its 37 references that are in the map. - First posted on arXiv 22 Jun 2026. ## Placed in - Vision-language-action models / [Human-video and latent-action pretraining](https://mlsanity.com/vla/human-video-and-latent-action-pretraining/) ## Builds on (most cited first, 30 of 31) - [OpenVLA: An Open-Source Vision-Language-Action Model](https://mlsanity.com/paper/2406.09246/) (2024) - [π0: A Vision-Language-Action Flow Model for General Robot Control](https://mlsanity.com/paper/2410.24164/) (2024) - [π0.5: a Vision-Language-Action Model with Open-World Generalization](https://mlsanity.com/paper/2504.16054/) (2025) - [Octo: An Open-Source Generalist Robot Policy](https://mlsanity.com/paper/2405.12213/) (2024) - [GR00T N1: An Open Foundation Model for Generalist Humanoid Robots](https://mlsanity.com/paper/2503.14734/) (2025) - [RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation](https://mlsanity.com/paper/2410.07864/) (2024) - [Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots](https://mlsanity.com/paper/2402.10329/) (2024) - [X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model](https://mlsanity.com/paper/2510.10274/) (2025) - [EgoMimic: Scaling Imitation Learning via Egocentric Video](https://mlsanity.com/paper/2410.24221/) (2024) - [Human-to-Robot Imitation in the Wild](https://mlsanity.com/paper/2207.09450/) (2022) - [AnyTeleop: A General Vision-Based Dexterous Robot Arm-Hand Teleoperation System](https://mlsanity.com/paper/2307.04577/) (2023) - [Scaling Proprioceptive-Visual Learning with Heterogeneous Pre-trained Transformers](https://mlsanity.com/paper/2409.20537/) (2024) - [DexPilot: Vision Based Teleoperation of Dexterous Robotic Hand-Arm System](https://mlsanity.com/paper/1910.03135/) (2019) - [Scaling Cross-Embodied Learning: One Policy for Manipulation, Navigation, Locomotion and Aviation](https://mlsanity.com/paper/2408.11812/) (2024) - [Phantom: Training Robots Without Robots Using Only Human Videos](https://mlsanity.com/paper/2503.00779/) (2025) - [DexUMI: Using Human Hand as the Universal Manipulation Interface for Dexterous Manipulation](https://mlsanity.com/paper/2505.21864/) (2025) - [RoVi-Aug: Robot and Viewpoint Augmentation for Cross-Embodiment Robot Learning](https://mlsanity.com/paper/2409.03403/) (2024) - [Masquerade: Learning from In-the-wild Human Videos using Data-Editing](https://mlsanity.com/paper/2508.09976/) (2025) - [RoboEngine: Plug-and-Play Robot Data Augmentation with Semantic Robot Segmentation and Background Generation](https://mlsanity.com/paper/2503.18738/) (2025) - [Mirage: Cross-Embodiment Zero-Shot Policy Transfer with Cross-Painting](https://mlsanity.com/paper/2402.19249/) (2024) - [RDT2: Exploring the Scaling Limit of UMI Data Towards Zero-Shot Cross-Embodiment Generalization](https://mlsanity.com/paper/2602.03310/) (2026) - [X-Sim: Cross-Embodiment Learning via Real-to-Sim-to-Real](https://mlsanity.com/paper/2505.07096/) (2025) - [Cross-Embodiment Dexterous Grasping with Reinforcement Learning](https://mlsanity.com/paper/2410.02479/) (2024) - [H2R: A Human-to-Robot Data Augmentation for Robot Pre-training from Videos](https://mlsanity.com/paper/2505.11920/) (2025) - [Latent Action Diffusion for Cross-Embodiment Manipulation](https://mlsanity.com/paper/2506.14608/) (2025) - [OXE-AugE: A Large-Scale Robot Augmentation of OXE for Scaling Cross-Embodiment Policy Learning](https://mlsanity.com/paper/2512.13100/) (2025) - [Augmented Reality for RObots (ARRO): Pointing Visuomotor Policies Towards Visual Robustness](https://mlsanity.com/paper/2505.08627/) (2025) - [One Hand to Rule Them All: Canonical Representations for Unified Dexterous Manipulation](https://mlsanity.com/paper/2602.16712/) (2026) - [One-Policy-Fits-All: Geometry-Aware Action Latents for Cross-Embodiment Manipulation](https://mlsanity.com/paper/2603.14522/) (2026) - [EmbodiSwap for Zero-Shot Robot Imitation Learning](https://mlsanity.com/paper/2510.03706/) (2025) ## Built on by (most cited first, 5 of 5) - [ZETA: A Controlled Study of Zero-Shot Cross-Embodiment VLA Transfer for Tabletop Manipulation](https://mlsanity.com/paper/2609.02546/) (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) - [FoveaVLA: Foveation Actions Replace the Wrist Camera in VLAs](https://mlsanity.com/paper/2610.20064/) (2026) - [Human Demonstrations as Post-Training Data for Any Robot Body: Retargeting the Object, Not the Hand](https://mlsanity.com/paper/2610.20351/) (2026) ## Abstract (arXiv) 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.