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LIBERO

This integration follows the RoboDojo-derived service conventions. LIBERO's task/init-state selection, native OSC actions and success rule remain distinct.

The libero adapter evaluates sensor-driven robot policies across five LIBERO suites (130 suite-task entries). This directory owns native environment integration, task selection and the evaluation protocol; model implementations and results belong under AgentPolicies.

Directory responsibilities 

LIBERO/
├── server.py                      # stable public service launcher
├── server.example.json            # operator configuration template
├── bench_server/
│   ├── adapter.py                 # public contract, private native session/scoring
│   └── protocol/                  # task catalog, selection plan and source inventory
├── environment/                   # installation pins and readiness tools
└── checks/                        # native-free regression checks and historical fixtures

The catalog is benchmark protocol data, not an environment deployment file or model configuration. Copy the server configuration outside the source tree to select a fixed suite/task subset, observation track, budget and GPU; configure source and asset locations through LIBERO_SOURCE and LIBERO_CONFIG_PATH. The canonical adapter lives in bench_server/adapter.py. The active service reads bench_server/protocol/libero_catalog.json and fixes its schedule at launch. The independent pinned native source inventory lives beside it in phase2_task_inventory.json. It records task identities and evaluator references, not an execution schedule or capability results. Historical first-two and first-five selection manifests are preserved unchanged under checks/fixtures/ for compatibility assertions. Historical anonymous attempt evidence lives in its existing model result record; its model/provider identity remains unverified.

This structure does not convert LIBERO into RoboDojo's EEF16 protocol. LIBERO still accepts normalized seven-dimensional OSC delta actions, with its own RGB or explicit RGB+joint-position track. A model must declare compatibility with that contract; renaming a directory cannot make a Dojo-only policy compatible.

Install the environment

Use a dedicated Python 3.10 environment. The pinned source includes BDDL tasks, meshes and released initial states; no VLA checkpoint or demonstration-action dataset is needed. Run from the RSI repository root, replacing the source path:

conda create -n starvla-rsi-libero python=3.10 pip
conda activate starvla-rsi-libero
export LIBERO_SOURCE=/path/to/LIBERO
git clone https://github.com/Lifelong-Robot-Learning/LIBERO "$LIBERO_SOURCE"
git -C "$LIBERO_SOURCE" checkout 8f1084e3132a39270c3a13ebe37270a43ece2a01
python -m pip install --no-compile -r benchmarks/simBenchmarks/LIBERO/environment/runtime-requirements.txt
python -m pip install --no-compile torch==2.5.1+cpu --index-url https://download.pytorch.org/whl/cpu
python -m pip install --no-compile --no-deps -e "$LIBERO_SOURCE"
python -m pip install --no-compile --no-deps -e .
python benchmarks/simBenchmarks/LIBERO/environment/configure.py \
  --upstream "$LIBERO_SOURCE" --config-dir "$CONDA_PREFIX/libero-config"
export LIBERO_CONFIG_PATH="$CONDA_PREFIX/libero-config"
export PYTHONPATH="$LIBERO_SOURCE${PYTHONPATH:+:$PYTHONPATH}"
export MUJOCO_GL=egl
export PYOPENGL_PLATFORM=egl
export MUJOCO_EGL_DEVICE_ID=0

The explicit upstream PYTHONPATH accommodates LIBERO's namespace-package metadata. CPU Torch deserializes released initial states; it does not run a robot policy. Runtime pins include Robosuite 1.4.0, MuJoCo 3.2.3 and NumPy 1.24.4. Select an available EGL GPU and use the same index in the adapter.

Verify and evaluate

This diagnostic performs reset, ten settling controls, rendering and one neutral control, with zero policy calls. Use a new output directory and verify source/controller metadata and complete image/video decoding.

python benchmarks/simBenchmarks/LIBERO/environment/verify_environment.py \
  --render-gpu-device-id 0 --output-dir /path/to/new-libero-diagnostic
python -B -m unittest discover -s benchmarks/simBenchmarks/LIBERO/checks -v

Run as an independent SIM server

The server owns native reset, controls and scoring. The evaluator owns the SIM client; the policy receives only allowed observations and never its endpoint, token or private artifacts. Follow the shared service protocol for evaluator-only token setup and SSH forwarding between nodes. The service listens only on loopback. Native-free launcher checks do not certify a working renderer or a native service-mode evaluation.

Copy server.example.json to an operator-owned configuration and select the tasks, observation track and GPU before launch. The example fixes LIBERO-10 task 0, released reset state 0 and a decision track of 200 calls, prefix 5, control cap 1000. It does not use the retained 520-control reference budget and is not a full native evaluation. Use the dedicated native Python and the LIBERO_CONFIG_PATH, PYTHONPATH and EGL settings from installation:

python -B -m benchmarks.simBenchmarks.LIBERO.server \
  --parameters /absolute/path/to/libero-server.json \
  --seed 20260907 --episodes 1 --port 8766 \
  --output-dir AgentPolicies/anonymous_code_agent/results/libero/raw/libero_10-task-0-basket-groceries/server

In a separate evaluator process/environment with NumPy installed and the same evaluator-only RSI_SIM_SERVER_TOKEN, connect an unused opaque mailbox actor endpoint:

python -B -m benchkit run \
  --benchmark libero --sim-server http://127.0.0.1:8766 \
  --model-profile anonymous_code_agent \
  --policy-args '{"directory":"/absolute/opaque-mailbox","timeout_s":900,"max_chunk_length":5}' \
  --seed 20260907 --episodes 1 --budget-mode decisions \
  --max-policy-calls 200 --replan-interval 5 --max-steps 1000 \
  --output-dir AgentPolicies/anonymous_code_agent/results/libero/raw/libero_10-task-0-basket-groceries/evaluator

Use new result directories and a stable source snapshot, verified before and after execution. Do not pass --benchmark-args to the remote runner. The server freezes the schedule at launch but does not check native packages/assets until the one permitted reset; startup and describe are not readiness results. Native source, controller and reset-file checks remain in the adapter, and a failed reset retains its attempt. No automatic reset/action retry is enabled. campaign prepare/run-case still uses its existing execution path, not this remote service workflow. Keep server/client evidence and media under the model result namespace; legacy artifact auditors do not yet validate this layout.

Fast mode means one attempt per preselected task, preserving failures and errors without automatic retry. The direct adapter defaults to LIBERO-10 tasks 0 and 1; the selection plan selects all 130 suite-task entries once. Neither is full native reset-state coverage. See the evaluation workflow, the catalog and protocol limitations.