UMI DAS Handheld Gripper — Kitchen-Counter Pick-and-Place: Drinking Straws, Cooking Tongs and Condiment Jars
Overview
UMI DAS Handheld Gripper — Kitchen-Counter Pick-and-Place: Drinking Straws, Cooking Tongs and Condiment Jars is a video dataset. It covers English. Typical applications include evaluation & benchmarking and robotics & embodied AI. Relevant sectors: AI & Machine Learning and Manufacturing & Industrial. The listing mentions timestamps. The provider lists it without a licence fee; terms should still be confirmed before commercial use.
Primary applications
What the listing offers
- Listing mentions timestamps.
- A sample is indicated as available from the provider.
- Version 1.0 as published by the source.
A single showcase episode of handheld-gripper tabletop manipulation, converted from raw MCAP into a HuggingFace LeRobot v2.1 dataset and shipped with the complete QC report for the capture session it came from. The episode — 'arrange the silicone strips on the table' — runs 4,197 frames (139.9 s) at 30 fps and is the strongest of the 13 captured: the longest, with the most gripper open/close cycles (13), the greatest end-effector travel (20.8 m) and the largest workspace envelope (1.2 × 1.03 × 0.78 m). Three synchronised fisheye views — cam_ego, cam_left_wrist, cam_right_wrist — all 640×360 H.264, alongside an 8-DoF state vector and 8-DoF action vector per frame. The bundled qc_report/ folder carries the QC verdict as machine-readable JSON, HTML and Markdown, plus a representative frame. QC outcome: 20/20 checks PASS, zero warnings, zero failures — readability, duration bounds, stream presence, per-stream rate, cross-camera frame match, timestamp monotonicity, inter-frame gaps and camera-to-pose sync. The episode was additionally reviewed frame-by-frame and contains no people, hands, faces or screens.
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- Language
- English
- Volume
- —
- Domain
- AI & Machine Learning +1
- Use case
- LLM pre-training