---
title: LIBERO
description: "Lifelong robot-learning benchmark with four tabletop suites. WorldFoundry can import official results or wire closed-loop execution — no invented ranks."
---

LIBERO (NeurIPS 2023 Datasets & Benchmarks) is the University of Texas at Austin's lifelong manipulation benchmark, with Tsinghua and Sony AI. A Franka on a **robosuite / MuJoCo** table must transfer **declarative** and **procedural** knowledge across tasks. The four official suites isolate what transfers:

- **LIBERO-Spatial** — same objects, different spatial relations / layouts.
- **LIBERO-Object** — same skills, different object identities.
- **LIBERO-Goal** — same scene, different goal language.
- **LIBERO-100** — LIBERO-10 short plus LIBERO-90 longer-horizon mix.

It has become the default VLA report card. The paper is [LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning](https://arxiv.org/abs/2306.03310). Project: [libero-project.github.io](https://libero-project.github.io/). Code: [Lifelong-Robot-Learning/LIBERO](https://github.com/Lifelong-Robot-Learning/LIBERO) (MIT).

WorldFoundry supports **two** paths. Official `eval_result` trees (or structured exports) go through `worldfoundry.evaluation.tasks.embodied.normalizer`. Closed-loop execution is wired via `worldfoundry-eval embodied run` and needs the upstream simulator, suite assets, and the policy the official script expects. Dataset snapshots `yifengzhu-hf/LIBERO-datasets` and `lerobot/libero` are tracked as staged assets. Models such as [Being-H0.5](/docs/guides/supported-models/being-h05) expose a LIBERO specialist checkpoint; that is a model-card fact, not a hub leaderboard row.

<BenchmarkPageSections id="libero" locale="en" />

## How to run

Import an official result tree (no simulator):

```bash
worldfoundry-eval zoo benchmark-run \
  --benchmark-id libero \
  --mode official-validation \
  --official-results-path <official_results.json> \
  --output-dir tmp/libero/official-validation \
  --json
```

Closed-loop execution needs `WORLDFOUNDRY_LIBERO_ROOT` (optional override of the runtime/repo root), suite assets, and a matching policy. Default import path when you omit `--official-results-path` is `WORLDFOUNDRY_LIBERO_RESULTS_PATH`. Shared asset stacks: `libero_source_assets`, `libero_dataset_assets`.

In-tree pointers:

```text
worldfoundry/data/benchmarks/tasks/external/libero.yaml
cache/worldfoundry/data/hfd_datasets/yifengzhu-hf__LIBERO-datasets
cache/worldfoundry/data/hfd_datasets/lerobot__libero
cache/vla_va_wam/repos/Lifelong-Robot-Learning--LIBERO
```

Outputs under `--output-dir` include `scorecard.json` (primary key `success_rate`), `run_manifest.json`, `results.jsonl`, and `metrics/summary.json`.

[← Benchmark Hub](/docs/evaluation/benchmark-hub)