common/source/starBenchserver/benchmarks/simBenchmarks/SERVICE_CONVENTIONS.md

Benchmark service conventions

RoboDojo is the reference for service organization and acceptance, not a universal robot controller. New integrations extend that validated design rather than introduce a separate model/simulator workflow. All implemented services use this layout; draft integrations are listed in SERVICE_SUPPORT.md.

Stable responsibilities

benchmarks/simBenchmarks/<benchmark>/
├── README.md                  # preparation, service launch, supported scope
├── EVALUATION_PROTOCOL.md     # native semantics, tracks, scores and limits
├── server.py                  # import-safe factory + main(argv=None)
├── server.example.json        # explicit operator paths and selected track
├── bench_server/
│   ├── adapter.py             # declared observation/action and native bridge
│   ├── protocol/              # dependency-light catalogs and interface rules
│   └── native_worker_client.py  # only when a private worker is needed
├── environment/               # native workers, setup, pins and readiness
└── checks/                    # benchmark-specific conformance regressions

AgentPolicies/<agent>/                # model-owned decisions, prompts, config, results
benchkit/sim_server/             # shared communication and session mechanics
benchkit/                       # also records, source capture, audits, reports

Use the existing transport under benchkit/sim_server/. The model actor receives only the declared observation and execution feedback. The trusted driver holds service credentials and administrative/result methods; it must not give these capabilities to the model. Only benchmark-owned code touches the SIM. The service must not import a model implementation or legacy campaign entrypoint.

Register implemented services in benchkit/sim_server/cli.py: rsi-bench list and rsi-bench serve --benchmark <id> ... delegate to the existing wrapper. Do not construct a second factory path that bypasses its source inventory, cache exclusions or complete-schedule rules. Availability is not native readiness. Public reports identify the complete method and its outcomes; optional histories show recorded inputs, outputs, actions and acknowledged feedback. Recording is default on, not a participation/publication gate. Hash/source checks remain internal support; recorded upstream/environment/asset provenance can support reproducibility.

An in-process native adapter inside the service is permitted; a second worker process is needed only for runtime isolation. Do not add empty layers or duplicate servers merely to make file lists identical. Migrated integrations use only bench_server/adapter.py and, when needed, bench_server/protocol/plan.py. Do not retain old root adapter shims or duplicate evaluation/ packages solely for compatibility. Update current callers, packaging and source inventories together; frozen historical packages retain their own original sources.

Invariants shared with RoboDojo

1. server.main(argv=None) supplies its benchmark factory and declared source inventory to the common launcher. Import, construction and describe must not reset an environment, call a model or move a robot.
2. Configuration is validated before native initialization. Select only supported tracks/tasks; freeze the resolved schedule and budgets before the first claim. Deployment paths are operator inputs, not unreviewed protocol overrides.
3. Each selected case is claimed once, including failed initialization. No hidden retries, reset search, branch rollouts or replay after an uncertain control ACK.
4. The declared public observation is an allowlist. Robot state is permitted when specified; object truth, reset metadata, native scoring and private logs stay evaluator-side. Models select actions; fixed low-level execution never solves the task on their behalf.
5. Record calls, executed action decisions and native controls separately. Invalid actions never execute. Declare feedback versus fail-fast behavior per versioned track; uncertainty about native execution remains terminal.
6. Native scoring remains authoritative. Keep task failures, policy stops and infrastructure errors distinct; preserve all attempts and unknown accounting.
7. Source inventories include the service, protocol and required worker code. Shared service capture is scoped separately from campaign/agent capture; declare the selected benchmark's complete supplemental inventory without relying on another benchmark or model file being incidentally captured. Paired client/server records, native outcomes and media belong under the model's results namespace. Only closed artifacts are exported and audited.

These are responsibility and protocol boundaries, not an OS sandbox or provider identity attestation. Apply deployment isolation separately.

Differences that must remain benchmark-owned

Aspect	Common rule	Do not copy blindly from RoboDojo
Observation/action	Declared, versioned, validated	EEF16, three cameras, robot ACK payload
Native runtime	Service-owned and lazily initialized	Isaac Sim, worker topology, fixed IK
Schedule	Frozen selected cases, each once	Five-layout fast / 50-layout full panels
Budget	Explicit units and resolved caps	1100 controls, number of ticks per control
Scoring	Native rule, never model self-report	Dojo's success conjunction or 0–100 score
Rejection	Never execute invalid input	A track's feedback/fail-fast selection

Dojo requires its complete named panel. Other services currently allow an explicit fast subset fixed at startup; this is not native-full support. Do not enable require_complete_schedule without defining the benchmark's complete schedule. SimplerEnv retains final-step scoring and separately selected rejection feedback; LIBERO retains its selected task/init-state contract; SIMPLE retains its native 36-value robot/controller commands. No cleanup silently changes these conditions.

For future real-robot backends, reuse these declared capability boundaries, not the simulator's paused-time/reset assumptions. Hardware adapters require their own timing, watchdog, safety, calibration and scoring validation. See real-robot status.

One acceptance sequence for every integration

Gate	Required evidence	Does not establish
Source/contract review	Native evaluator pins, task inventory, sensor/action/scorer mapping	A working environment
Offline regression	Import-safe entrypoint; no-reset startup; captured source; shared lifecycle/error tests plus native contract fixtures	Rendering or task success
Native readiness	Separate authorized no-model check of reset, RGB, one valid control and cleanup through the service	Model capability
Model sample	Preselected task/case once through the same server, with score, counts, errors and declared optional recording	Whole-suite SR or native-full parity
Expansion	Independent review, fixes, regression; then predeclare broader coverage	Permission to silently retry failures

The cross-benchmark entrypoint regression is benchkit/sim_server/checks/test_benchmark_conventions.py; benchmark-specific tests and the shared service tests remain required. Model samples must be separately authorized; structural refactoring and offline tests do not start one. A valid completed failure is useful acceptance evidence, not a reason to rewrite the scorer.

See the wire protocol and support matrix for current implementation limits. A passing conformance test is not a claim that all native, sensor, provider or full-schedule gates have passed.