IPEC-COMMUNITY
Top license: apache-2.0 · arxiv-cited research
HF AUTHOR INDEX
The 1,001 robotics-tagged HF records ship from 38 authors with three or more datasets each. Author clusters are the canonical destination for buyer queries like "Stanford robotics datasets" or "NVIDIA physical AI datasets" that aren't well-served by individual record pages.
DIRECT ANSWER
Each cluster aggregates every record from one author — including those demoted from the indexable surface for thin metadata or duplicate variants. Clusters surface the author’s license posture, total downloads, top modalities, and any arxiv-cited research records. 667 records across 38 authors.
38 AUTHORS
Top license: apache-2.0 · arxiv-cited research
Top license: apache-2.0 · arxiv-cited research
Top license: cc-by-4.0 · arxiv-cited research
Top license: not specified · arxiv-cited research
Top license: mit · arxiv-cited research
Top license: apache-2.0 · arxiv-cited research
Top license: not specified
Top license: apache-2.0
Top license: apache-2.0 · arxiv-cited research
Top license: apache-2.0 · arxiv-cited research
Top license: cc-by-4.0
Top license: apache-2.0 · arxiv-cited research
Top license: cc-by-nc-sa-4.0 · arxiv-cited research
Top license: cc-by-4.0 · arxiv-cited research
Top license: apache-2.0 · arxiv-cited research
Top license: mit · arxiv-cited research
Top license: apache-2.0
Top license: cc-by-4.0
Top license: cc-by-nc-4.0 · arxiv-cited research
Top license: mit · arxiv-cited research
Top license: apache-2.0
Top license: apache-2.0
Top license: apache-2.0 · arxiv-cited research
Top license: apache-2.0
Top license: cc-by-nc-4.0 · arxiv-cited research
Top license: apache-2.0
Top license: cc-by-4.0 · arxiv-cited research
Top license: apache-2.0
Top license: apache-2.0
Top license: cc-by-4.0 · arxiv-cited research
Top license: apache-2.0
Top license: apache-2.0
Top license: apache-2.0
Top license: apache-2.0
Top license: mit · arxiv-cited research
Top license: mit · arxiv-cited research
Top license: mit
Top license: apache-2.0
RESEARCH PATHS
A dataset record is only useful when it connects into the rest of the buyer workflow. The next review step is usually not another summary; it is a fit check, rights triage, source comparison, or custom bounty spec that names the missing proof.
For physical AI teams, the hard question is whether the public source can support a specific model objective under real deployment constraints. That requires adjacent dataset records, tools, comparisons, and sourcing paths, plus external references that a reviewer can open and challenge.
Use the links below to keep the review grounded. Start broad when discovery is incomplete, move into profile and comparison pages when the candidate source is known, and switch to custom collection when the blocker is rights, consent, geography, robot embodiment, or target environment coverage.
INTERNAL LINKS
Use the catalog to compare source-backed dataset profiles by modality, task, rights signal, consent risk, and deployment fit.
Scan the broader robotics dataset surface before narrowing into promoted profiles, comparisons, and custom collection specs.
Track source updates, licensing notes, and buyer-readiness changes that should trigger a renewed review.
Score whether a public source is enough for the model, rights path, modalities, and target environment.
Separate source license language from contributor consent, redistribution, private-space risk, and model-use assumptions.
Turn a public-source gap into a scoped capture request with sample QA, metadata, and delivery requirements.
Compare data providers when the answer is not another public dataset but a better sourcing or capture route.
Use the company index to separate annotation vendors, data engines, marketplaces, and specialist capture teams.
EXTERNAL REFERENCES
Market context for why physical AI systems need custom, enriched, real-world data beyond generic labeling workflows.
Robotics dataset and tooling context for Hugging Face based collection, sharing, conversion, and training workflows.
A cross-embodiment robotics dataset reference for comparing trajectory scale, robot diversity, and VLA training assumptions.
A large in-the-wild robot manipulation dataset reference for real-world trajectory capture and deployment transfer risk.
TRUELABEL ROUTING
The watchlist updates from upstream HF metadata. Authors with fewer than three robotics-tagged records appear under their individual dataset pages but don't get their own cluster.