# OpenMOSS/Awesome-WAM

A curated, continuously updated reading list, paper blogs, and resources for World Action Models (WAMs) in embodied AI.

Repository: https://github.com/OpenMOSS/Awesome-WAM
Canonical: https://ross.abutalabs.com/products/awesome-wam
Language: HTML
License: MIT
License Family: permissive
Last push: 2026-08-26T06:09:02+00:00

## Health v2 (maintenance only)
Score: 59/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 35, longevity 9
- inputs: {"age_days": 139, "days_push": 7, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, young
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1336, forks 37 (observed 2026-08-28T04:04:24.860542+00:00)

## What it is
A curated, continuously updated reading list and survey resource for World Action Models (WAMs) in embodied AI, accompanying a systematic survey paper. It includes paper summaries, blogs, benchmarks, and a leaderboard for the field.

## Use cases
- find papers on world action models
- learn about embodied AI world models
- get a survey of WAM architectures
- track new research on VLA and world models
- find benchmarks for embodied AI models
- read structured summaries of robotics AI papers

## When to choose
- you need a curated, up-to-date overview of World Action Models research
- you want structured summaries and blogs to quickly understand papers
- you are surveying embodied AI, world models, or VLA learning

## When to avoid
- you need runnable code or a software library rather than a reading list
- you need a mature, finalized reference rather than a continuously evolving resource

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation, developer-tools
- domain: artificial-intelligence, robotics, machine-learning, tutorials, awesome-lists
- platform: -
- tags: awesome-list, world-action-models, embodied-ai, survey, reading-list, world-models, vla, paper-summaries, web-server

## Member repositories
- OpenMOSS/Awesome-WAM (main) score 59

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:24.860542+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:44:06.654116+00:00, confidence not recorded.
  - readme: https://github.com/OpenMOSS/Awesome-WAM (fetched 2026-08-28T04:04:24.860542+00:00, sha 6a507e6eb70e)
- Data as of 2026-08-30T08:39:29.467469+00:00.