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

Zhaxizhuoma, Kehui Liu, Chuyue Guan, Zhongjie Jia, Ziniu Wu, Xin Liu, Tianyu Wang, Shuai Liang, Pengan Chen, Pingrui Zhang, Haoming Song, Delin Qu, Dong Wang, Zhigang Wang, Nieqing Cao, Yan Ding, Bin Zhao, Xuelong Li

arXiv:2409.19499v2cs.RO

TL;DR

First-person wrist-mounted views can leave much of the robotic arm unseen, causing ACT to produce illicit joint configurations during inference. FastUMI combines decoupled hardware, T265-based tracking, data verification, multiple trajectory formats, and compatibility with ACT and DP imitation learning. FastUMI maintains robust performance across diverse manipulation scenarios while reducing deployment complexity and operational overhead, and releases over 10,000 demonstrations across 22 everyday tasks.

  • Problem

    First-person wrist-mounted views can leave much of the robotic arm unseen, causing ACT to produce illicit joint configurations during inference.

    Source: C. Enhanced ACT for First-Person… · p. 7

  • Method

    FastUMI combines decoupled hardware, T265-based tracking, data verification, multiple trajectory formats, and compatibility with ACT and DP imitation learning.

    Source: §I · p. 2

  • Results

    FastUMI maintains robust performance across diverse manipulation scenarios while reducing deployment complexity and operational overhead, and releases over 10,000 demonstrations across 22 everyday tasks.

    Source: §I · p. 3, §IX · pp. 13–14

  • Takeaways & Limitations

    FastUMI provides a scalable, hardware-independent interface and dataset for real-world data-driven robotic learning.

    Source: §I · p. 2, §IX · pp. 13–14

    Visual sensing may be insufficient for tasks requiring precise force or tactile feedback, such as handling fragile objects.

    Source: §VII · p. 12

Abstract

from arXiv · show

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.

I. INTRODUCTION

FastUMI redesigns UMI to make real-world robotic manipulation data collection more scalable by reducing hardware dependencies, deployment complexity, and user overhead. It combines decoupled hardware, simplified tracking, and a data-and-algorithm ecosystem, supported by experiments and a dataset spanning 22 everyday tasks.

I. INTRODUCTION

  • FastUMI addresses UMI’s hardware and software limitations through an extensive redesign centered on adaptability, efficiency, and data-quality support.The redesign targets specialized-component dependencies, complex calibration, fragile tracking under occlusion, and limited algorithmic compatibility.Source: §I · pp. 1–2
  • Hardware decoupling enables integration with diverse robotic arms and grippers while preserving consistent viewpoints between handheld demonstrations and robot-mounted execution.Standardized fingertip attachments, adjustable camera mounts, and simplified mechanical structures reduce redesign and configuration overhead.Source: §I · p. 2, Figure 1 · p. 2
  • FastUMI’s ecosystem supports data verification, end-effector and joint trajectories, ACT and DP algorithms, and custom variants for challenging first-person observations.These tools address close-up views, variable scene geometry, and limited depth information.Source: §I · pp. 2–3
  • Replacing GoPro-based VIO with RealSense T265 tracking reduces calibration and integration complexity while improving robustness during partial visual occlusions.The GoPro remains available for high-resolution video, but T265 supplies the primary visual-inertial tracking function.Source: §I · pp. 1–2
  • Experiments find performance similar to original UMI with significantly lower user overhead, while the released dataset contains over 10,000 trajectories across 22 everyday tasks.The collection includes diverse manipulation scenarios and visual occlusions representative of real-world conditions.Source: §I · p. 3

A. Hardware Design Challenges

FastUMI addresses hardware-design challenges through decoupling, consistent viewpoints, and modular components for handheld and robot-mounted configurations. Its designs support broad gripper compatibility while preserving visual alignment for policy transfer.

A. Hardware Design Challenges

  • FastUMI targets hardware decoupling, visual consistency, broad gripper compatibility, plug-and-play deployment, and reliable data quality across robotic platforms.The design philosophy uses physical decoupling, uniform viewpoints, and self-contained sensing to reduce configuration overhead.Source: A. Hardware Design Challenges · p. 3

B. Handheld Device Design

  • The handheld device combines a 155-degree GoPro fisheye camera with T265 pose tracking for wide context and stable estimation under partial occlusions.The GoPro captures environmental context, while the T265 replaces UMI’s visual odometry and removes complex calibration steps.Source: B. Handheld Device Design · pp. 3–4
  • Factory-aligned fingertip attachments and marker refinements keep the camera flexible, distortion-aware, and plug-and-play across handheld configurations.The GoPro handles visual context and verification, while the T265 provides protected pose tracking under occlusion.Source: B. Handheld Device Design · p. 4

C. Robot-Mounted Device Design

  • Robot-mounted hardware uses ISO-compatible mounting, interchangeable fingertip modules, and adjustable extension arms to reproduce the handheld camera perspective across grippers.Five customized fingertip attachments cover over 90% of grippers in cited open-source datasets, although unsupported types remain adaptable rather than fully covered.Source: C. Robot-Mounted Device Design · p. 4
  • A dynamic error-compensation algorithm addresses approximately 1 centimeter of xArm effective-length variation that can otherwise disrupt transferred visual alignment.The method compensates for gripper-specific motion differences during inference.Source: C. Robot-Mounted Device Design · p. 5, Figure 4 · p. 5

D. Other Design Optimizations

FastUMI improves structural robustness while organizing multimodal acquisition through independently running, synchronized sensor streams. Drift correction combines loop closure with reinitialization when visual realignment is insufficient.

D. Other Design Optimizations

  • FastUMI strengthens the device with vibration-reducing mounts, lightweight carbon-fiber components, and standardized interfaces for adjustable extension arms.The arrangement supports a shared handheld device and adaptation of the robot-mounted device across grippers and robotic arms.Source: D. Other Design Optimizations · p. 5

A. Raw Data Acquisition

  • The acquisition pipeline separates GoPro imaging, T265 tracking, and HDF5 storage into ROS nodes, enabling multimodal recording and sensor extensibility.Independent sensor nodes permit additional modalities such as tactile sensors to be integrated through corresponding ROS nodes.Source: A. Raw Data Acquisition · p. 5
  • T265 tracking removes extensive calibration and VIO tuning but introduces dual-sensor synchronization and drift-management requirements.The trade-off shifts deployment complexity toward managing synchronized streams and accumulated pose drift.Source: A. Raw Data Acquisition · p. 5
  • 20 Hz synchronized subsampling pairs GoPro frames with temporally nearest T265 poses, achieving sub-millisecond offsets despite 60 Hz and 200 Hz sensor rates.A unified ROS clock, multithreaded buffering, and greatest-common-frequency subsampling protect alignment and data integrity.Source: A. Raw Data Acquisition · p. 5
  • Loop closure at a blue table groove typically realigns the T265 trajectory, but persistent misalignment requires reinitializing the sensor in a stationary reference pose.The groove provides a visually distinct region for revisiting previously mapped features.Source: A. Raw Data Acquisition · p. 5, Figure 5 · p. 6

B. Raw Data Quality Assessment

FastUMI establishes data-quality checks and transforms tracked handheld motion into trajectory formats suitable for diverse imitation-learning algorithms. The pipeline uses confidence and smoothness criteria, calibrated camera-to-gripper geometry, and kinematic conversion to produce absolute, relative, and joint trajectories.

B. Raw Data Quality Assessment

  • At least 95% of sampled poses must achieve High T265 confidence, while low-confidence frames are interpolated and abrupt motion is filtered using velocity, acceleration, and orientation thresholds.Low-light conditions can reduce confidence and increase drift; the checks reduce, but do not guarantee, an absolute data-quality benchmark.Source: B. Raw Data Quality Assessment · p. 6

C. Data Preparation for Training

  • FastUMI organizes training data as absolute or relative TCP trajectories and absolute joint trajectories for compatibility with diverse imitation-learning algorithms.The software supports these formats with minimal configuration.Source: C. Data Preparation for Training · p. 6
  • Trajectory generation assumes known T265 poses, robot URDF geometry, camera-to-gripper offset, and gripper-center pose, with handheld motion mirroring the robot end-effector.Absolute TCP poses are transformed into the robot base frame, while inverse kinematics converts them into joint trajectories.Source: C. Data Preparation for Training · p. 6
  • Relative TCP trajectories remove dependence on a global reference, producing more uniform data distributions and improving generalization when the robot base pose varies.Source: C. Data Preparation for Training · p. 6
  • A marker-based gripper-width method maps ArUco-marker pixel distance to physical opening width, decoupling software from specific gripper mechanisms.Missing marker observations are handled by mirroring or imputation so every frame retains a valid distance before normalization by dmax and dmin.Source: C. Data Preparation for Training · p. 7

A. Motivation: Beyond Hardware Decoupling

FastUMI must address both hardware-independent deployment and policy-learning challenges created by close-up, heterogeneous, depth-limited observations. The paper adapts ACT with temporal smoothing and pose-based prediction to improve feasibility and transfer across platforms.

A. Motivation: Beyond Hardware Decoupling

B. Data Challenges with FastUMI

  • FastUMI data combines close-up first-person views, variable robot geometry and scene layouts, and limited depth, complicating consistent policy learning and precise positioning.These characteristics motivate adaptations for ACT and DP that support kinematic feasibility and depth-aware execution.Source: B. Data Challenges with FastUMI · p. 7

C. Enhanced ACT for First-Person Perspectives

  • Depth-Enhanced DP supplements GoPro frames with post-processed Depth Anything V2 maps, adding depth awareness without dedicated depth sensors or extra hardware.Source: B. Data Challenges with FastUMI · p. 7, Figure 7 · p. 8
  • FastUMI’s first-person perspective hides much of the arm, making standard ACT prone to illicit joint configurations and extreme orientations that violate kinematic constraints.Source: C. Enhanced ACT for First-Person… · p. 7
  • Smooth-ACT adds a GRU above the Transformer decoder to refine local temporal continuity while preserving global spatiotemporal modeling.Its hierarchical regularization is intended to reduce abrupt deviations and kinematically invalid actions.Source: C. Enhanced ACT for First-Person… · p. 7
  • PoseACT predicts absolute and relative end-effector poses instead of absolute joints, then maps relative outputs back to joint angles through the robot’s kinematic model.Relative motion reduces sensitivity to base-frame and arm-geometry variation while mitigating outliers and extreme commands.Source: C. Enhanced ACT for First-Person… · pp. 7–8

D. Depth-Enhanced Diffusion Policy

The paper improves diffusion-policy precision with post-processed depth maps and compensates TCP shifts caused by non-parallel gripper jaws.

D. Depth-Enhanced Diffusion Policy

  • Baseline DP struggles with precise depth-dependent actions, including reaching targets and pressing buttons, when depth information is unavailable.The reported failures include inaccurate target reaching and premature gripper closure.Source: D. Depth-Enhanced Diffusion Policy · p. 8
  • Depth-Enhanced DP adds estimated depth without dedicated sensors or multi-view cameras, targeting precision-critical manipulation.Images are cropped and resized before Depth Anything V2 generates depth maps; RGB and pseudo-color depth are encoded together.Source: D. Depth-Enhanced Diffusion Policy · p. 8

E. Dynamic Error-Compensation Algorithm

  • The dynamic compensation method offsets TCP displacement by increasing the negative local-Z correction as gripper width decreases.It computes a width-dependent compensation distance, determines displacement direction from the TCP orientation, then solves inverse kinematics using the corrected position.Source: E. Dynamic Error-Compensation Algorithm · pp. 8–9
  • The compensation pipeline uses two stages: width-based distance computation followed by pose correction in the robot base frame.The local TCP Z-axis is transformed into the base coordinate frame to determine the correction direction while preserving the original orientation.Source: E. Dynamic Error-Compensation Algorithm · pp. 8–9

V. OPEN-SOURCE DATASET

The FastUMI Dataset provides 10,000 synchronized real-world demonstrations across diverse tasks and environments, collected with explicit sensor-quality monitoring.

A. Dataset Overview

  • The dataset contains 10,000 synchronized GoPro-video and end-effector-trajectory demonstrations spanning 22 tasks, 19 object categories, and 12 manipulation skills.Demonstrations were collected in domestic settings and typically lasted about 9 seconds.Source: A. Dataset Overview · p. 9

B. Dataset Acquisition Process

  • Five operators used three FastUMI devices while varying backgrounds, lighting, clutter, and object positions to diversify interactions and environments.Target objects remained fixed per task while surrounding conditions were randomized.Source: B. Dataset Acquisition Process · p. 9
  • RVIZ monitoring and a quality-assurance protocol verified T265 tracking confidence, with drift-affected sequences discarded or re-recorded.Source: B. Dataset Acquisition Process · p. 9

C. Dataset Storage and Format

The paper evaluates FastUMI’s dataset and sensing pipeline across storage, tracking quality, baseline policy performance, and diverse manipulation conditions.

C. Dataset Storage and Format

  • The dataset stores observations and actions in HDF5, provides joint and TCP trajectories, and supports conversion to Zarr for flexible access.Source: C. Dataset Storage and Format · p. 9
  • The evaluation covers data quality, baseline performance, algorithmic enhancements, and additional factors including camera configuration and dataset size.Experiments use 12 real-world manipulation tasks, generally on an xArm 6 platform.Source: §VI · p. 9

A. Data Quality

  • T265 averages 10.5 mm error on Pick Cup and 17.7 mm on Open Container, while MINI averages 15.2 mm and 11.2 mm respectively.Ground truth comes from optical motion capture of four reflective markers across ten synchronized trajectories per sensor and task.Source: A. Data Quality · p. 9, Table I · p. 11
  • Figure 8 summarizes representative demonstrations, task proportions, and manipulation-skill distributions in the dataset.Source: Figure 8 · p. 10
  • MINI remains more consistent across occlusion levels, whereas T265 performs better with unobstructed views but develops pronounced error peaks near occluding surfaces.T265 errors recover toward initial levels when the gripper returns to a feature-rich viewpoint.Source: A. Data Quality · p. 10, Figure 11 · p. 11, Figure 10 · p. 11

B. Baseline Performance

  • ACT and DP achieve high success rates across most of 12 tasks, including partially occluded Rearrange Coke and Open Container scenarios.The results support the dataset’s use with different policy representations and partial-visibility conditions.Source: B. Baseline Performance · pp. 10–11, Table II · p. 11
  • DP is limited by precise depth requirements, while ACT can produce unseen or unreachable joint configurations under absolute-coordinate prediction.DP struggles with pressing and vertical alignment; ACT exhibits inverted gripper postures and workspace mismatches, while DP partly alleviates the latter through relative positioning.Source: B. Baseline Performance · pp. 11–12

C. Algorithmic Enhancements

The paper enhances policy inference with depth cues, temporal and pose-aware ACT variants, and studies camera, data-volume, and deployment limitations.

C. Algorithmic Enhancements

  • Depth-Enhanced DP raises success by 26.67% on Pick Lid and 73.33% on Open Ricecooker relative to baseline DP.The gains target tasks requiring accurate vertical alignment or force application.Source: C. Algorithmic Enhancements · p. 12, Table III · p. 12
  • Smooth-ACT and PoseACT improve original ACT, while relative TCP representations help extended trajectories but can reduce height accuracy on Pick Bear.The comparison exposes a trade-off between relative trajectory dynamics and absolute pose information.Source: C. Algorithmic Enhancements · p. 12, Table IV · p. 12

D. Additional Factors

VII. LIMITATIONS

  • FastUMI currently relies on visual sensing, limiting support for tasks requiring precise force or tactile feedback.The paper identifies tactile or force sensing as a route toward richer representations for delicate interactions.Source: §VII · pp. 12–13
  • FastUMI does not yet support complex morphologies such as mobile manipulators requiring whole-body control, and wired transfer limits portability.These boundaries constrain deployment on advanced platforms and in mobile or standalone field settings.Source: §VII · p. 13

VIII. RELATED WORK

FastUMI addresses hardware and software barriers in robotic demonstration collection while supporting imitation-learning evaluation and scalable real-world datasets.

A. Data Collection Methods

  • FastUMI enables portable data collection with a handheld device, avoiding the real-robot dependence and cross-platform modifications required by teleoperated systems.It directly captures action information and aligns wrist-mounted observations between handheld and on-robot devices, reducing embodiment mismatch relative to video-only demonstrations.Source: A. Data Collection Methods · p. 13
  • T265 tracking provides direct pose estimates without cumbersome SLAM or motion-capture infrastructure, while the wrist-mounted GoPro supplies high-resolution visual data.This design targets efficient acquisition of precise action and observation data for policy learning.Source: A. Data Collection Methods · p. 13

B. Imitation Learning

  • FastUMI data is evaluated with ACT and diffusion policy, with algorithmic adaptations designed to improve performance on the collected demonstrations.The related-work discussion distinguishes ACT’s temporal action ensembling from diffusion policy’s multimodal action modeling and stable training.Source: B. Imitation Learning · p. 13

IX. CONCLUSION

  • FastUMI performs similarly to the original UMI while reducing user overhead, and its released dataset contains 10,000 real-world demonstrations across 22 everyday tasks.The conclusion reports lower costs, simpler deployment, and support for data-driven policy learning.Source: §IX · p. 14

X. APPENDIX

The appendix compares the T265 and RoboBaton MINI devices and documents the FastUMI dataset’s file organization, image and pose representations, and action storage.

A. RealSense T265 vs. RoboBaton MINI

  • The appendix presents a device-specification comparison between the RealSense T265 and RoboBaton MINI, accompanied by a product image.The supplied appendix materials identify the comparison table but do not provide its specification values.Source: Table VII · p. 16, Figure 12 · p. 16

B. FastUMI Dataset

  • The FastUMI dataset contains more than 10,000 demonstrations from 22 daily tasks and is distributed across task-specific files that users merge after downloading.Each file contains no more than 50 HDF5 files, with each HDF5 file representing one episode.Source: B. FastUMI Dataset · p. 16
  • Each episode stores observations, camera images, qpos trajectories, actions, and attributes in a hierarchical HDF5 structure.Images are stored as uint8 arrays, while qpos contains seven pose dimensions and actions mirror qpos in the described script.Source: B. FastUMI Dataset · pp. 16–17
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