ModelFeatured
Octo
Generalist robot policy Transformer with multi-modal inputs and multi-embodiment finetuning.
93MFully openData: Open X-EmbodimentUpdated 2024-07-31
ModelFeaturedFully open
HPT (Heterogeneous Pre-trained Transformers)
Cross-embodiment pretraining architecture for robot policies with open research code.
Why: Open cross-embodiment pretrain architecture
Overview
HPT (Heterogeneous Pre-trained Transformers) is catalogued on Embodied AI Hub as a curated model for embodied AI builders.
Cross-embodiment pretraining architecture for robot policies with open research code.
Why it matters: Open cross-embodiment pretrain architecture
Use the official repository and docs as the source of truth for install pins, licenses, and hardware requirements.
Who it is for
Researchers, students, and builders comparing open embodied stacks.
Key highlights
- Cross-embodiment pretraining architecture for robot policies with open research code.
- Type: model; categories: model
- Marked recommended on the hub
When to use
- You are mapping the open embodied AI landscape
- You need a starting URL and judgment note before deep-diving
When not to use
- You need production SLAs or certified industrial support only
- License or hardware constraints are incompatible with your deployment
Getting started
- 1Open the official URL and read README install pins.
- 2Skim license and citation requirements.
- 3Run the smallest official example before scaling experiments.
- 4Cross-link related hub resources (sim / data / policy) for a full stack.
Papers & reading
Caveats & pitfalls
- Hub cards are curated summaries — always verify upstream docs.
- Stars and dates drift; treat extra.stars as approximate.
Content reviewed 2026-07-28