| # LIBERO |
|
|
| This integration follows the [RoboDojo-derived service conventions](../SERVICE_CONVENTIONS.md). |
| 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](EVALUATION_PROTOCOL.md); model implementations and results belong under [AgentPolicies](../../../AgentPolicies/README.md). |
|
|
| ## Directory responsibilities |
|
|
| ```text |
| 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](bench_server/protocol/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](../../../AgentPolicies/anonymous_code_agent/results/libero/TASK_CHECKLIST.md); |
| 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: |
|
|
| ```sh |
| 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. |
|
|
| ```sh |
| 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](../SERVER_PROTOCOL.md) |
| 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](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: |
|
|
| ```sh |
| 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](../../../AgentPolicies/anonymous_code_agent/README.md): |
|
|
| ```sh |
| 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](bench_server/protocol/plan.py) selects all 130 suite-task entries once. Neither is full native reset-state coverage. See [the evaluation workflow](../../../docs/evaluation.md), [the catalog](bench_server/protocol/libero_catalog.json) and [protocol limitations](EVALUATION_PROTOCOL.md). |
|
|
