Manipulation data
Hand-Object Interaction Data for Robotics
Hand-object interaction data captures how hands approach, grasp, move, use, and release objects. For robotics, egocentric video can help document interaction context, but useful datasets also need task boundaries, object state, annotations, quality controls, and governance review.
Comparison
| Annotation | Model use | Quality failure to catch |
|---|---|---|
| Hand visibility | Grasp and approach context | Hands leave frame or occlude object |
| Object state | Before/after task understanding | State changes are missing or ambiguous |
| Action phase | Temporal segmentation | Task boundaries are inconsistent |
| Failure label | Robustness and eval design | Only clean successes are captured |
Why egocentric video is useful but not sufficient
Egocentric video can show hands, tools, object contact, and occlusion from the actor's perspective. It does not by itself solve manipulation: buyers still need annotations, object-state definitions, task boundaries, metadata, and a QA rubric [1].
Public dataset examples and limitations
Ego4D, Ego-Exo4D, and EPIC-KITCHENS can help frame hand-object interaction tasks, skilled activities, and kitchen activity recognition. They should be treated as public research references unless the intended use, access terms, consent posture, and license are separately verified [2] [3] [4].
Data quality and failure modes
A robotics collection plan should include failure cases, not only successful demonstrations. Watch for unstable cameras, missing object state, unlabelled task interruptions, poor lighting, incomplete consent documentation, and samples that cannot be aligned to the model's input format.
Related pages
Use these to move from category-level context into specific task, dataset, format, and comparison detail.
External references and source context
- Ego4D: Around the World in 3,000 Hours of Egocentric Video
The Ego4D paper is the source-backed reference for first-person daily-life activity video and benchmark design.
arXiv ↩ - Egocentric video remains useful but incomplete for robot data buyers
Ego4D is an official public reference for egocentric video dataset scope, access, and dataset documentation.
ego4d-data.org ↩ - Ego-Exo4D project site
Ego-Exo4D is the official project source for paired first-person and third-person skilled-activity capture.
ego-exo4d-data.org ↩ - EPIC-KITCHENS-100 annotations license
The EPIC-KITCHENS-100 annotation license is a visible source for non-commercial licensing caveats.
GitHub ↩ - Ego-Exo4D annotations documentation
Ego-Exo4D annotation documentation supports dataset-structure and skilled-activity-label discussion.
docs.ego-exo4d-data.org - Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
The Ego-Exo4D paper describes skilled human activity from first- and third-person perspectives.
arXiv - EPIC-KITCHENS project site
EPIC-KITCHENS is an official project reference for egocentric kitchen-activity data.
epic-kitchens.github.io - Rescaling Egocentric Vision: Collection, Pipeline and Challenges for EPIC-KITCHENS-100
The EPIC-KITCHENS-100 paper supports public kitchen-activity benchmark facts and caveats.
arXiv
More glossary terms
FAQ
What is hand-object interaction data?
It is data that captures hands interacting with objects, including approach, grasp, manipulation, tool use, release, and task outcome.
Why is egocentric video useful for hand-object interaction?
The actor viewpoint often keeps hands, tools, and object contact in the frame, which can make interaction context easier to inspect.
What annotations are needed for hand-object interaction datasets?
Common annotations include action phase, object identity, object state, hand visibility, grasp type, success or failure, and task boundaries.
What are common quality failures in robotics manipulation data?
Common failures include occluded objects, inconsistent task boundaries, missing failures, motion blur, poor synchronization, and incomplete metadata or consent evidence.
Find datasets covering hand-object interaction data
Truelabel surfaces vetted datasets and capture partners working with hand-object interaction data. Send the modality, scale, and rights you need and we route you to the closest match.
Map your robotics data requirement to capture, consent, and QA constraints