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Dataset · Hugging Face

origin-data-lab/egocentric-keystep-annotation

Keystep Annotation and IMU Boundary Evaluation Origin Data Lab · Technical Field Note #05 · 24 October 2026 Short answer. Structural checks found zero reported gaps, overlaps, missing required fields or out-of-vocabulary verbs across 25 human-labelled keysteps in two egocentric cooking clips. A body-worn IMU showed measurable motion changes at some boundaries, but not all task-to-task boundaries were distinguishable from random positions. IMU motion is a useful review signal… See the full description on the dataset page: https://huggingface.co/datasets/origin-data-lab/egocentric-keystep-annota

Author / organizationorigin-data-lab
License (as declared)other
Downloads18 (as reported by Hugging Face)
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Created24 October 2026
Last updated at source24 October 2026
roboticsphysical-aiegocentricimuaction-segmentationannotationevaluation
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  • 24 Oct 2026 Description: Keystep Annotation — Evaluation Samples Origin Data Lab · Technical Field Note #05 Technical field note · GitHub · Hugging Face Limited evaluation release: one documented annotation example and four boundary-test rows. Not the complete 25-k → Keystep Annotation and IMU Boundary Evaluation Origin Data Lab · Technical Field Note #05 · 24 October 2026 Short answer. Structural checks found zero reported gaps, overlaps, missing required fields or out-of-vocabulary verbs across 25 hum
  • 24 Oct 2026 Topics added physical-ai, action-segmentation
  • 24 Oct 2026 Description: Keystep Annotation Evaluation Samples Source: https://origindatalab.io/technical-field-notes/keystep-annotation-egocentric-robot-learning.html This is a limited documentation and evaluation sample, not a release of the 25 complete keystep a → Keystep Annotation — Evaluation Samples Origin Data Lab · Technical Field Note #05 Technical field note · GitHub · Hugging Face Limited evaluation release: one documented annotation example and four boundary-test rows. Not the complete 25-k
  • 24 Oct 2026 Topics added evaluation removed task_categories:video-classification

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