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.

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 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:

Language suffix: underlying pre-language reset file.
View suffix: underlying pre-view reset file.
Table/table-background suffix removal; light suffix selects its base reset.
Layout _add_/_level variants: init_files/libero_newobj/<suite>/..., 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: <suite>/task-<ordinal>, 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/<profile>/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.