Get a free one-hour sample of the EgoChores dataset: novel, first-person video of real household chores, captured and annotated for dexterous hand-object manipulation. Commission a dataset to your own spec.
Precisely annotated in Label Studio, every clip is segmented into timed actions and resolved against a hierarchical object taxonomy created under the supervision of robotics researchers. Commission custom collection runs in the same schema to extend and adapt the dataset.
EgoChores ships with a fully published, machine-readable annotation schema.
EgoChores is a sample dataset With household chores performed in a variety of global home environments. Each segment has been precisely annotated inside of Label Studio via custom-trained models and human annotators.
A slice of the EgoChores corpus with basic clip-level tags and metadata, unannotated — the capture on its own terms.
Judging framing, lighting, household diversity, and whether the raw footage clears the bar for your own annotation pipeline.
The same clips with a label schema applied to capture action segmentation, dexterous hand-object interaction, and object taxonomy.
Measuring annotation density, boundary precision, and taxonomy fit against what your VLA training loops actually consume.
Signal can help your team establish a POC with clips annotated to a schema you specify: custom object classes, edge cases, and proprietary schema configurations.
Seeing your own schema executed on real footage before committing to a full collection and annotation program.
No, EgoChores is a sample. It exists so you can evaluate how we capture egocentric footage and how we annotate it, at whatever depth matters to your team, without a procurement cycle. Once it clears your bar, we scale the same protocol and taxonomy into a collection program sized and scoped to your models: your task families, your environments, your schema.
While Ego4D provides unparalleled unconstrained scale, it lacks the dense task-family specificity needed for highly repeatable robotic policy learning. EgoChores provides tight, standardized taxonomies across identical task categories, minimizing background noise and maximizing target interaction density.
Yes. Our human data collection networks span multiple global regions, allowing us to capture diverse architectural styles, appliance form factors, and cultural variations in chore performance.
EgoChores ships natively in standard JSON / COCO-style formats for vision models. Our internal annotation tooling also supports custom schema compilation to format outputs directly for your team's proprietary data loaders.
Let's design your proprietary dataset program today.