# StanfordVL/BEHAVIOR-1K

BEHAVIOR-1K: a platform for accelerating Embodied AI research. Join our Discord for support: https://discord.gg/bccR5vGFEx

Repository: https://github.com/StanfordVL/BEHAVIOR-1K
Canonical: https://ross.abutalabs.com/products/behavior-1k
Homepage: https://behavior.stanford.edu
Language: Python
License Family: other
Topics: robotics, simulation, benchmark, embodied-ai
Last push: 2026-08-26T00:46:45+00:00

## Health v2 (maintenance only)
Score: 99/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 99, longevity 100
- inputs: {"age_days": 1720, "days_push": 8, "days_rel": 9, "gap_med": 27, "n_releases_24m": 8}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1660, forks 241 (observed 2026-08-28T04:05:18.411268+00:00)

## What it is
BEHAVIOR-1K is a simulation benchmark for embodied AI agents covering 1,000 everyday household activities across 50 interactive scenes, built on the OmniGibson simulator (NVIDIA Isaac Sim/Omniverse). The monorepo bundles the simulator, task definitions (BDDL), dataset, evaluation, and teleoperation tooling for training and evaluating agents on long-horizon mobile manipulation tasks.

## Use cases
- benchmark embodied AI agents on household tasks
- train robots for mobile manipulation in simulation
- simulate realistic physics with fluids, cloth, and thermal states
- evaluate long-horizon task planning for home robots
- research human-centered everyday activities for robotics
- run the BEHAVIOR challenge evaluation pipeline

## When to choose
- you need a realistic embodied AI benchmark grounded in real human needs
- you require rich physical simulation with fluids, deformables, and semantic object states
- you want 1,000 diverse long-horizon household tasks with reproducible evaluation
- you are targeting the annual BEHAVIOR challenge

## When to avoid
- you need a lightweight or fast simulator rather than high realism
- you lack an NVIDIA RTX GPU or 32GB+ RAM
- you only need simple tabletop manipulation benchmarks
- you need a pip-installable or Docker deployment (currently unavailable)

## Facets
- artifact type: dataset
- maturity: active
- function: simulation, benchmarking, machine-learning, robotics
- domain: robotics, artificial-intelligence, simulation, machine-learning
- platform: windows, python
- tags: embodied-ai, benchmark, omnigibson, household-tasks, mobile-manipulation, isaac-sim, monorepo, linux, gpu

## Member repositories
- StanfordVL/BEHAVIOR-1K (main) score 99

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:18.411268+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 2026-08-30T03:44:39.705483+00:00, confidence not recorded.
  - readme: https://github.com/StanfordVL/BEHAVIOR-1K (fetched 2026-08-28T04:05:18.411268+00:00, sha 79744543130b)
  - homepage: https://behavior.stanford.edu (fetched 2026-08-29T11:17:04.914060+00:00, sha 8f993a2dc8e9)
  - site_page: https://behavior.stanford.edu/getting_started/installation.html (fetched 2026-08-29T11:17:04.923066+00:00, sha 1f4b06044853)
  - site_page: https://behavior.stanford.edu/getting_started/quickstart.html (fetched 2026-08-29T11:17:04.925210+00:00, sha a9a42d8e897a)
  - site_page: https://behavior.stanford.edu/other/faq.html (fetched 2026-08-29T11:17:04.927322+00:00, sha cd661807e55f)
- Data as of 2026-08-30T08:39:29.467469+00:00.