# Mirage: Cross-Embodiment Zero-Shot Policy Transfer with Cross-Painting

arXiv: https://arxiv.org/abs/2402.19249 · HTML: https://mlsanity.com/paper/2402.19249/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Feb 2024.

## Built on by (most cited first, 30 of 36)

- [EgoMimic: Scaling Imitation Learning via Egocentric Video](https://mlsanity.com/paper/2410.24221/) (2024)
- [RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation](https://mlsanity.com/paper/2412.13877/) (2024)
- [Humanoid Policy ~ Human Policy](https://mlsanity.com/paper/2503.13441/) (2025)
- [View-Invariant Policy Learning via Zero-Shot Novel View Synthesis](https://mlsanity.com/paper/2409.03685/) (2024)
- [CLAP: Contrastive Latent Action Pretraining for Learning Vision-Language-Action Models from Human Videos](https://mlsanity.com/paper/2601.04061/) (2026)
- [Cross-Embodiment Dexterous Grasping with Reinforcement Learning](https://mlsanity.com/paper/2410.02479/) (2024)
- [Mitigating the Human-Robot Domain Discrepancy in Visual Pre-training for Robotic Manipulation](https://mlsanity.com/paper/2406.14235/) (2024)
- [Effective Tuning Strategies for Generalist Robot Manipulation Policies](https://mlsanity.com/paper/2410.01220/) (2024)
- [OXE-AugE: A Large-Scale Robot Augmentation of OXE for Scaling Cross-Embodiment Policy Learning](https://mlsanity.com/paper/2512.13100/) (2025)
- [Human2LocoMan: Learning Versatile Quadrupedal Manipulation with Human Pretraining](https://mlsanity.com/paper/2506.16475/) (2025)
- [MV-UMI: A Scalable Multi-View Interface for Cross-Embodiment Learning](https://mlsanity.com/paper/2509.18757/) (2025)
- [Built Different: Tactile Perception to Overcome Cross-Embodiment Capability Differences in Collaborative Manipulation](https://mlsanity.com/paper/2409.14896/) (2024)
- [See Once, Then Act: Vision-Language-Action Model with Task Learning from One-Shot Video Demonstrations](https://mlsanity.com/paper/2512.07582/) (2025)
- [Cloak: Zero-Shot Cross-Embodiment Manipulation by Masking the End-Effector from the VLA](https://mlsanity.com/paper/2606.22836/) (2026)
- [Embodied Co-Design for Rapidly Evolving Agents: Taxonomy, Frontiers, and Challenges](https://mlsanity.com/paper/2512.04770/) (2025)
- [Invariance Co-training for Robot Visual Generalization](https://mlsanity.com/paper/2512.05230/) (2025)
- [TTT-Parkour: Rapid Test-Time Training for Perceptive Robot Parkour](https://mlsanity.com/paper/2602.02331/) (2026)
- [CRAFT: Video Diffusion for Bimanual Robot Data Generation](https://mlsanity.com/paper/2604.03552/) (2026)
- [The Unreasonable Effectiveness of Discrete-Time Gaussian Process Mixtures for Robot Policy Learning](https://mlsanity.com/paper/2505.03296/) (2025)
- [Ego-Pi: VLA Fine-Tuning for Ego-Centric Human and Robot Data](https://mlsanity.com/paper/2606.08107/) (2026)
- [JoyAI-RA 0.5: Scaling Robot Manipulation Learning via Dual Action Alignment](https://mlsanity.com/paper/2608.05674/) (2026)
- [Knowledge Transfer for Cross-Domain Reinforcement Learning: A Systematic Review](https://mlsanity.com/paper/2404.17687/) (2024)
- [Inference-stage Adaptation-projection Strategy Adapts Diffusion Policy to Cross-manipulators Scenarios](https://mlsanity.com/paper/2509.11621/) (2025)
- [Human-Humanoid Robots Cross-Embodiment Behavior-Skill Transfer Using Decomposed Adversarial Learning from Demonstration](https://mlsanity.com/paper/2412.15166/) (2024)
- [X2C: A Large-Scale Benchmark for Nuanced Humanoid Facial Expression Imitation](https://mlsanity.com/paper/2505.11146/) (2025)
- [RoboSwap: A GAN-driven Video Diffusion Framework For Unsupervised Robot Arm Swapping](https://mlsanity.com/paper/2506.08632/) (2025)
- [Efficient Knowledge Transfer for Jump-Starting Control Policy Learning of Multirotors through Physics-Aware Neural Architectures](https://mlsanity.com/paper/2602.15533/) (2026)
- [Flow as Flow: Modeling Robot Velocity Fields as Probability Velocity Fields for Flow-Based Object Manipulation](https://mlsanity.com/paper/2606.23090/) (2026)
- [Cross-Embodiment Transfer via Behavior-Aligned Representations](https://mlsanity.com/paper/2607.27549/) (2026)
- [The Embodiment Gap in Robot Foundation Models](https://mlsanity.com/paper/2608.18433/) (2026)

## Abstract (arXiv)

The ability to reuse collected data and transfer trained policies between robots could alleviate the burden of additional data collection and training. While existing approaches such as pretraining plus finetuning and co-training show promise, they do not generalize to robots unseen in training. Focusing on common robot arms with similar workspaces and 2-jaw grippers, we investigate the feasibility of zero-shot transfer. Through simulation studies on 8 manipulation tasks, we find that state-based Cartesian control policies can successfully zero-shot transfer to a target robot after accounting for forward dynamics. To address robot visual disparities for vision-based policies, we introduce Mirage, which uses "cross-painting"--masking out the unseen target robot and inpainting the seen source robot--during execution in real time so that it appears to the policy as if the trained source robot were performing the task. Mirage applies to both first-person and third-person camera views and policies that take in both states and images as inputs or only images as inputs. Despite its simplicity, our extensive simulation and physical experiments provide strong evidence that Mirage can successfully zero-shot transfer between different robot arms and grippers with only minimal performance degradation on a variety of manipulation tasks such as picking, stacking, and assembly, significantly outperforming a generalist policy. Project website: https://robot-mirage.github.io/