# LIBERO-Plus direct-control protocol Status: implemented lazy adapter and standalone SIM server; native full parity and model capability scores are not established by installation or CPU tests. Benchmark ID: `libero_plus`. Native source pin: [`4976dc30028e805ff8094b55501d532c48fec182`](https://github.com/sylvestf/LIBERO-plus/tree/4976dc30028e805ff8094b55501d532c48fec182). ## Task universe and selection The pinned native task map and one-based classification IDs contain **10,030 variants in four suites**. RSI uses the native zero-based task indices with `task_order_index=0`. The unrelated 90-task `libero_90` suite is excluded. | Native perturbation category | Spatial | Object | Goal | LIBERO-10 | | --- | ---: | ---: | ---: | ---: | | Background Textures | 258 | 248 | 281 | 289 | | Camera Viewpoints | 376 | 396 | 408 | 419 | | Language Instructions | 390 | 354 | 410 | 383 | | Light Conditions | 292 | 297 | 279 | 274 | | Objects Layout | 385 | 403 | 425 | 312 | | Robot Initial States | 350 | 398 | 409 | 393 | | Sensor Noise | 351 | 422 | 379 | 449 | | Total | 2,402 | 2,518 | 2,591 | 2,519 | The four [frozen catalogs](bench_server/protocol/catalogs/) record every native name, category, difficulty level, public/native instruction, backing BDDL SHA-256 and resolved initial-state file SHA-256. `environment/freeze_catalog.py` reproduces this data read-only from the pinned upstream checkout without importing a simulator. No licensed training dataset or model weights are needed for evaluation. `evaluation_mode: fast` selects an explicit increasing, unique `task_ids` list, one predeclared seed, released state index **0**, and **one attempt per selected variant**. Failures and environment errors remain in the selected denominator; neither the client nor the adapter retries a failed task/reset. Upstream's own reset wrapper may retry internal placement randomization before returning one reset; that is retained native initialization, not a success-seeking model retry. The upstream README instructs changing original LIBERO's 50 trials to **one trial per LIBERO-Plus variant**. A `full` preset is deliberately rejected until the complete native matrix, seed/aggregation convention and runtime are verified. Selecting many task IDs is not proof of full benchmark parity. ## Native variation and reset semantics The service passes the exact **virtual** native BDDL filename to `OffScreenRenderEnv`. It must not substitute the backing filename when starting the environment: `_view_..._initstate_...` names cause the native wrapper to change camera orientation/distance, endpoint angles, Panda initialization and, when present, sensor-noise parameters. The wrapper resolves the actual BDDL by removing the view suffix. No variation is recreated by a model or harness helper. The native reset loader resolves filenames in this precedence order: 1. Language suffix: underlying pre-language reset file. 2. View suffix: underlying pre-view reset file. 3. Table/table-background suffix removal; light suffix selects its base reset. 4. Layout `_add_`/`_level` variants: `init_files/libero_newobj//...`, with the native loader's one-row reshape retained. RSI checks these exact files against the catalog before native `torch.load`. It seeds NumPy and the native environment, invokes native reset and state 0, then performs the retained evaluator's **10 fixed settle controls** `[0,0,0,0,0,0,-1]`. They are initialization, not model decisions, and are recorded separately. A native success during settling is an initialization error, not a model success. No task-solving action is supplied by the adapter. ## Public observation and direct action interface - Public task label: `/task-`, never a variant filename. - Language: the backing BDDL's `:language` field, with native BDDL tokenization (lowercase and whitespace normalization). Rewritten language variants keep their rewritten BDDL instruction. - Two 256×256 RGB uint8 views: `agentview`, `robot0_eye_in_hand`. - Fixed retained orientation: independent contiguous copies of `native_rgb[::-1, ::-1]`. No resize, crop, recoloring or reversal of native sensor-noise perturbations. - No joint/EEF/object poses, success, reward, seed, view angles, robot-init index, calibration, segmentation or simulator metadata is delivered to the policy. **Language track distinction:** upstream's non-language `task.language` is generated from the full variant filename and includes numeric view/init-state suffixes. Passing it through would disclose evaluator-private parameters. RSI uses the actual BDDL instruction instead; the original native string remains in private catalog/provenance for identity checks. This explicit direct-control track must not be reported as byte-identical legacy model-prompt parity. Each native control is a normalized 7D OSC_POSE vector in `[-1,1]` at **20 Hz**: world-frame translation deltas (component 1 → 0.05 m), world axis-angle rotation deltas (component 1 → 0.5 rad), and gripper `-1` open / `+1` close / `0` retain the native gripper target. The native controller is checked before reset. No VLA proposal, IK task planner, grasp service or fallback policy is allowed. `Policy.act` runs only through the shared observation-to-decision interface. The evaluator privately owns the service token/client. A model gets no direct reset, step, scorer or artifact-read endpoint. Idle service time causes no controls. ## Budgets, scoring and reporting The retained StarVLA evaluator references 220/280/300/520 policy controls for spatial/object/goal/LIBERO-10, excluding the 10 settle controls. These are named reference horizons, not an inferred universal engine limit. Optional `budget_mode: decisions` declares `max_policy_calls × replan_interval` as an explicit alternative control cap and changes protocol identity. The neutral runner separately enforces call/chunk limits. Calls, executed action decisions, native controls and settle controls are distinct counters. Ordinary give-up preferences belong to the policy profile, never this benchmark. `BDDLBaseDomain.step` assigns `done = _check_success()` natively. RSI uses that returned value once; it never polls the success predicate again or scores from model text. Native success terminates; exhausting the predeclared control cap without success truncates. A renderer/load/protocol error is an infrastructure error, not evidence that the policy cannot perform the task. The server and evaluator retain source captures, schedule, original failures, native versions, reset/BDDL hashes, action records, PNG frame hashes and separate camera-named videos. Selected mesh/texture references from constructed native XML have bounded hash receipts; unresolved relative references are explicit. These receipts do **not** attest the complete asset archive or loaded GPU memory. The 60 declared native source files are hash-checked before reset. Dependency versions and the controller configuration are recorded/checked, not a byte-level attestation of all third-party package source or loaded memory. Use a stable native checkout and inspect renderer diagnostics before capability work. The native import inventory rejects extra Python/extension/bytecode files and source-directory symlinks before import. Asset, BDDL and reset trees are data scopes verified separately, not a recursive import inventory of unused code. The actual ImageMagick library version, quantum depth and HDRI/features are recorded separately from Python Wand. Do not aggregate sensor-noise results across different backends; this initial integration does not claim a universal ImageMagick binary pin or complete full-suite parity. `native_events.jsonl` records evaluator-private initialization and control start/end receipts. Metrics distinguish declared settling, actually completed settle controls, policy-control attempts/completions and uncertain native calls. A missing completion receipt is not silently counted as a completed control. Native or observation errors poison the session; artifact finalization rejects further controls and close failures remain visible on repeated cleanup calls. Results, indices and video exports belong under `AgentPolicies//results/libero_plus/`, not in this benchmark directory. Historical anonymous attempts cannot be relabeled as verified named-model scores. The standalone server is supported; remote frozen campaign/full scheduling is not yet integrated. See the [shared service protocol](../SERVER_PROTOCOL.md).