# FastUMI: A Scalable and Hardware-Independent Universal Manipulation Interface with Dataset

arXiv: https://arxiv.org/abs/2409.19499 · HTML: https://mlsanity.com/paper/2409.19499/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2024.

## Built on by (most cited first, 30 of 40)

- [Cosmos 3: Omnimodal World Models for Physical AI](https://mlsanity.com/paper/2606.02800/) (2026)
- [ABot-M0: VLA Foundation Model for Robotic Manipulation with Action Manifold Learning](https://mlsanity.com/paper/2602.11236/) (2026)
- [Hume: Introducing System-2 Thinking in Visual-Language-Action Model](https://mlsanity.com/paper/2505.21432/) (2025)
- [World Action Models: The Next Frontier in Embodied AI](https://mlsanity.com/paper/2605.12090/) (2026)
- [RaC: Robot Learning for Long-Horizon Tasks by Scaling Recovery and Correction](https://mlsanity.com/paper/2509.07953/) (2025)
- [Xiaomi-Robotics-1: Scaling Vision-Language-Action Models with over 100K Hours of Real-World Trajectories](https://mlsanity.com/paper/2607.15330/) (2026)
- [ActiveUMI: Robotic Manipulation with Active Perception from Robot-Free Human Demonstrations](https://mlsanity.com/paper/2510.01607/) (2025)
- [MoMa-Kitchen: A 100K+ Benchmark for Affordance-Grounded Last-Mile Navigation in Mobile Manipulation](https://mlsanity.com/paper/2503.11081/) (2025)
- [Latent Policy Steering with Embodiment-Agnostic Pretrained World Models](https://mlsanity.com/paper/2507.13340/) (2025)
- [Genie Centurion: Accelerating Scalable Real-World Robot Training with Human Rewind-and-Refine Guidance](https://mlsanity.com/paper/2505.18793/) (2025)
- [OCRA: Object-Centric Learning with 3D and Tactile Priors for Human-to-Robot Action Transfer](https://mlsanity.com/paper/2603.14401/) (2026)
- [Data Pyramid for Embodied Manipulation: A Survey](https://mlsanity.com/paper/2607.24744/) (2026)
- [3D Equivariant Visuomotor Policy Learning via Spherical Projection](https://mlsanity.com/paper/2505.16969/) (2025)
- [AoE: Always-on Egocentric Human Video Collection for Embodied AI](https://mlsanity.com/paper/2602.23893/) (2026)
- [OpenHLM: An Empirical Recipe for Whole-Body Humanoid Loco-Manipulation](https://mlsanity.com/paper/2606.22174/) (2026)
- [HandEdit: A Unified Benchmark for Egocentric Human-to-Robot Dexterous Hand Image Editing](https://mlsanity.com/paper/2608.12122/) (2026)
- [RoboPocket: Improve Robot Policies Instantly with Your Phone](https://mlsanity.com/paper/2603.05504/) (2026)
- [UMI-3D: Extending Universal Manipulation Interface from Vision-Limited to 3D Spatial Perception](https://mlsanity.com/paper/2604.14089/) (2026)
- [YUBI: Yielding Universal Bidigital Interface for Bimanual Dexterous Manipulation at Scale](https://mlsanity.com/paper/2606.10244/) (2026)
- [HiFi-UMI: Learning Deployable Manipulation Policies from High-Fidelity UMI Data Alone](https://mlsanity.com/paper/2607.25895/) (2026)
- [Learning Panorama-Aware VLA for Mobile Manipulation with Whole-Body Teleoperation](https://mlsanity.com/paper/2608.02257/) (2026)
- [What Matters for Latent Actions in Robot Learning](https://mlsanity.com/paper/2608.19613/) (2026)
- [Closed-Loop Action Chunks with Dynamic Corrections for Training-Free Diffusion Policy](https://mlsanity.com/paper/2603.01953/) (2026)
- [VISTA: Vision-Grounded and Physics-Validated Adaptation of UMI data for VLA Training](https://mlsanity.com/paper/2606.04708/) (2026)
- [EgoAERO: Learning Dexterous Manipulation from a Single Egocentric Video without Object Assets](https://mlsanity.com/paper/2606.08057/) (2026)
- [CMP: Robust Whole-Body Tracking for Loco-Manipulation via Competence Manifold Projection](https://mlsanity.com/paper/2604.07457/) (2026)
- [Spatially Prompted Visual Trajectory Prediction for Egocentric Manipulation](https://mlsanity.com/paper/2605.20085/) (2026)
- [HIL-UMI: Bringing Human-in-the-Loop Post-Training of Vision-Language-Action Models to Universal Manipulation Interface](https://mlsanity.com/paper/2609.20659/) (2026)
- [In-Context Learning for Robots: Methods and Applications](https://mlsanity.com/paper/2609.36012/) (2026)
- [Sim-and-Human Co-training for Data-Efficient and Scene-Generalizable Bimanual Manipulation](https://mlsanity.com/paper/2601.19406/) (2026)

## Abstract (arXiv)

Real-world manipulation data involving robotic arms is crucial for developing generalist action policies, yet such data remains scarce since existing data collection methods are hindered by high costs, hardware dependencies, and complex setup requirements. In this work, we introduce FastUMI, a substantial redesign of the Universal Manipulation Interface (UMI) system that addresses these challenges by enabling rapid deployment, simplifying hardware-software integration, and delivering robust performance in real-world data acquisition. Compared with UMI, FastUMI has several advantages: 1) It adopts a decoupled hardware design and incorporates extensive mechanical modifications, removing dependencies on specialized robotic components while preserving consistent observation perspectives. 2) It also refines the algorithmic pipeline by replacing complex Visual-Inertial Odometry (VIO) implementations with an off-the-shelf tracking module, significantly reducing deployment complexity while maintaining accuracy. 3) FastUMI includes an ecosystem for data collection, verification, and integration with both established and newly developed imitation learning algorithms, accelerating policy learning advancement. Additionally, we have open-sourced a high-quality dataset of over 10,000 real-world demonstration trajectories spanning 22 everyday tasks, forming one of the most diverse UMI-like datasets to date. Experimental results confirm that FastUMI facilitates rapid deployment, reduces operational costs and labor demands, and maintains robust performance across diverse manipulation scenarios, thereby advancing scalable data-driven robotic learning.