A portable, high-fidelity robot-free data-production system for robot manipulation learning. Across three policy backbones evaluated in the report, policies post-trained solely on HiFi-UMI data reached success rates comparable to policies post-trained on in-domain real-robot teleoperation data.
NEW YORK, Aug. 20, 2026 /PRNewswire/ — Simple AI has published the Tech Report for HiFi-UMI, a high-fidelity robot-free data-production system for robot manipulation learning, together with HiFi-UMI-2K, a 2,000-hour open dataset released under the Creative Commons Attribution 4.0 license. The Tech Report is available on arXiv and the dataset on Hugging Face.
A portable, high-fidelity robot-free data-production system for robot manipulation learning. Across three policy backbones evaluated in the report, policies post-trained solely on HiFi-UMI data reached success rates comparable to policies post-trained on in-domain real-robot teleoperation data.
Progress in robot manipulation learning is increasingly constrained by data. Real-robot teleoperation yields accurate, directly trainable trajectories but is difficult to scale: every hour of data requires the target robot, a teleoperation rig, and a skilled operator. Robot-free handheld demonstrations are cheaper and easier to scale, but have primarily been used for pre-training. Task-specific post-training, the stage that grounds a policy for real-robot deployment, has typically continued to rely on a smaller amount of real-robot teleoperation data as an anchor.
The report examines whether raising the fidelity of robot-free demonstration data, rather than shrinking the real-robot fraction, can remove that anchor for target-task post-training.
HiFi-UMI is a portable data-production system co-designed for four fidelity properties. Pose accuracy comes from head-mounted offline stereo-inertial SLAM, which the report measures at 3mm workspace-local end-effector accuracy. Cross-sensor timing is aligned to below 40 microseconds through a shared hardware trigger across all cameras and sensors. Inter-gripper relative pose is measured natively rather than reconstructed. Per-hand sensing covers approximately 200 degrees of field of view through two non-parallel wide-angle fisheye cameras. Every captured demonstration passes through automatic trajectory reconstruction and simulation replay validation, each gate with an approximately 98% pass rate.
The report evaluates the approach across three policy backbones spanning the vision-language-action and world-action-model families, and four bimanual tabletop tasks. Policies post-trained solely on HiFi-UMI demonstrations reached success rates comparable to policies post-trained on in-domain real-robot teleoperation data, with reported differences of −2.5, +3.1, and −0.6 percentage points across the three backbones. On a precision insertion task, the strongest HiFi-UMI-only policy reached 85% success under conditions where the teleoperation baseline had the advantage of being collected in the evaluation scene. Separately, pre-training on 4,000 hours of the same corpus reduced offline action prediction error on ten unseen tasks by 41%, and increased real-robot success on one of the evaluated backbones by 18.1 percentage points at matched post-training data.
“We wanted to test whether fidelity, rather than scale alone, is what unlocks robot-free data for deployment-oriented training,” said Xiaofei Li, founder of Simple AI. “The report shows what this can look like within a specific set of tasks and models. By open-sourcing HiFi-UMI-2K, we hope to give the wider research community a shared, high-fidelity resource for continuing to study this question.”
The report characterizes these findings as approximate aggregate parity within the tested models, tasks, and experimental conditions. The deployment robot uses the same gripper and wrist-camera configuration as the capture setup, with the main embodiment difference being robot arm kinematics. The comparison is not sample-matched, with 3,200 HiFi-UMI trajectories per task set against approximately 300 teleoperation trajectories, and reflects a comparison between practical data-production pipelines rather than a claim of per-trajectory equivalence. The report does not generalize the result to all robot learning settings, and does not conclude that real-robot data is no longer required in the broader field.
HiFi-UMI-2K is distributed in a training-ready format with synchronized multi-view video, bimanual end-effector trajectories, gripper states, language annotations, and subtask boundaries. Human faces in the recordings are masked before release. The paper reached No. 1 on Hugging Face Daily Papers on July 29.
HiFi-UMI is one component of Simple AI’s work across foundation models, high-fidelity data, robotic systems, and real-world deployment. The company welcomes conversations with research groups and industry partners interested in high-fidelity data for robot learning.
About Simple AI
Simple AI is an embodied AI company developing general-purpose embodied intelligence systems for human living spaces. Its work integrates foundation models, high-fidelity data, robotic systems, and real-world deployment across the full embodied AI stack.
Resources
Tech Report: arxiv.org/abs/2607.25895
Dataset: huggingface.co/datasets/simple-world-lab/HiFi-UMI-2K
Project page: cloud.simpleai.tech/simple-world-lab/hifi-umi/
Media Contact
Grant Xin
Simple AI
