| # Evaluate an agent method |
|
|
| The unit of evaluation is a complete method: its general-purpose models, |
| prompts, memory, tools, planning and action-generation behavior. Single-model, |
| fixed-prompt policies remain useful baselines. Publish comparisons within a |
| benchmark and protocol, with method differences explicit; this repository does |
| not yet define a cross-benchmark aggregate leaderboard. |
|
|
| ## Two control ends |
|
|
| - `benchmarks/simBenchmarks/<benchmark>/`: robot capabilities, native execution |
| budget, fixed episode selection and native scoring. |
| - `AgentPolicies/<method>/`: actor implementation, trusted driver, models, |
| prompts/configuration and `results/<benchmark>/`. |
| - `benchkit/`: shared transport, central case scheduling, data formats, optional recorders and reports; |
| not a mandatory third process or a central model proxy. |
|
|
| Scheduling organizes selected cases, not the agent's thoughts or actions. Current |
| campaign scheduling uses frozen local adapters; protocol-first remote drivers |
| are a separate supported path. See [workflow choices](evaluation.md#choose-a-workflow). |
|
|
| Direct control constrains environment access and robot execution, not internal |
| reasoning. A method may use several declared general-purpose models and tools |
| over permitted information. This does not allow oracle state, extra resets, |
| hypothetical simulator rollouts, a VLA/hybrid controller, a fallback task policy |
| or a server-side task solver. Fixed robot-only/native low-level execution remains |
| benchmark-owned. Tool/network availability and cross-episode memory restrictions |
| must still follow the selected protocol and declared deployment configuration. |
|
|
| Check the selected track before comparing methods. Some existing benchmark |
| documents, notably SimplerEnv's single-request development track, still specify |
| narrower interaction or evidence conditions. The open-method framework does not |
| retroactively relax those conditions or relabel their results. Reconciling that |
| track requires an explicit version decision before an agentic-method comparison; |
| it is not achieved by changing a prompt or this general guide. |
|
|
| Use benchmark-native execution conditions. Report inference requests, tokens, |
| latency and cost when available; do not invent unknown values. There is no new |
| universal reasoning/token/decision budget. Existing method-specific timeouts, |
| feedback-loop caps and protocol-specific limits remain explicit configuration, |
| not silently disabled. In particular the current Direct example still requires |
| a finite decision-call cap to bound invalid-action feedback. |
|
|
| ## Connect using the protocol |
|
|
| 1. Install the benchmark's documented native environment and assets. Choose the |
| protocol and fixed schedule, then launch `rsi-bench serve --benchmark <id> ...`. |
| 2. Implement the [authenticated JSON protocol](../benchmarks/simBenchmarks/SERVER_PROTOCOL.md) |
| in your trusted driver. `describe` supplies the actual observation/action |
| contract. Claim each selected episode once; obey sequencing, native budgets, |
| termination and uncertain-ACK/no-replay rules. |
| 3. Expose only declared observations and robot actions to the actor. Do not give |
| it the administrative client, credentials, private metrics or native handles. |
| 4. Close/finalize every attempt and emit essential result records. Detailed |
| interaction history is optional, default enabled where supported. |
|
|
| Any language or framework can implement this protocol. The Python remote client, |
| `Policy` interface and `AgentPolicies/policy_support/runner.py` are optional SDK |
| conveniences. They are not prerequisites for participating. A new method does not |
| require a copy of the simulator or a new benchmark server. |
|
|
| For a small worked example, see |
| [`AgentPolicies/protocol_example`](../AgentPolicies/protocol_example/README.md). |
| It owns a loop without the profile registry or Python `Policy`, and produces |
| basic-result-compatible records. It deliberately accepts only a complete |
| single-case service schedule with horizon mode and prefix one; it is not a |
| general evaluator or a RoboDojo panel driver. Tests use a fake service, not a |
| native robot or model. |
|
|
| ### Administrative access is not an actor tool |
|
|
| | Capability | Trusted driver / operator | Model actor | |
| | --- | --- | --- | |
| | Select and claim a fixed case; reset once | Yes, according to the predeclared schedule | No reset, resampling or branch-rollout tool | |
| | Receive declared RGB, robot state, instruction and execution feedback | Project only the selected protocol's allowed fields | Yes; state provenance and units remain benchmark-specific | |
| | Submit robot actions | Validate/forward and account for ACKs | Choose actions through the declared action interface | |
| | Read native metrics, private artifacts, credentials or deployment configuration | Keep private; collect outcomes for reporting | Never passed as observations or tools | |
|
|
| The current service token authorizes administrative operations; it is not an |
| actor-scoped token. A same-process callback or separate directory alone is not a |
| sandbox. Review the driver and deploy untrusted code with separate filesystem, |
| process and credential permissions. The framework does not attest that isolation. |
|
|
| ## Declare composition, not just a model label |
|
|
| The reference driver accepts a JSON `--method-file`; the reusable Python runner |
| accepts `RunRequest(method=...)`. A declaration looks like: |
|
|
| ```json |
| { |
| "schema_version": "starvla-rsi.method/v1", |
| "id": "visual_planner_controller", |
| "version": "v1", |
| "models": [ |
| {"id": "planner", "role": "planning", "provider": null, "model": null, "version": null}, |
| {"id": "controller", "role": "action_generation", "provider": null, "model": null, "version": null} |
| ], |
| "configuration": { |
| "memory": "episode_local", |
| "prompt_revision": "v1", |
| "tools": ["local_image_analysis"], |
| "decision_call_cap": 240 |
| } |
| } |
| ``` |
|
|
| Replace unknown identifiers with what is actually requested/known. This is a |
| participant declaration, not provider attestation or an automatic multi-model |
| implementation: your method must implement the composition. Do not put secrets, |
| private paths or evaluator information in public configuration. Report prompt, |
| memory, tool and inference settings sufficient to explain the comparison. |
|
|
| Without a method file the reference runner derives a limited identity from the |
| legacy policy descriptor. This does **not** establish a complete method |
| configuration; use an explicit declaration for reproducible comparisons. Legacy |
| `policy` fields remain in raw records, and historical anonymous results are not |
| relabeled as verified named-model outcomes. |
|
|
| ## Usage and decisions are different counters |
|
|
| `policy_calls` counts invocations of the reference decision boundary, including |
| stop/rejected/failed calls. `action_decisions` counts decisions with at least one |
| acknowledged control. `environment_steps` counts acknowledged controls. Native |
| physics ticks and provider inference requests are separate quantities. |
|
|
| The method chooses how many actions, including repeated targets, to send before |
| another decision. Its trusted driver submits successive single-action `step` |
| requests and accounts for each ACK; the server does not run a model-specific |
| repeat loop. Native timing, physics substeps, end conditions and total execution |
| budget remain benchmark-owned. A method's five-control example is not a universal |
| benchmark rule. Retained decision-coupled tracks and declared replan requirements |
| must still match until explicitly migrated. |
|
|
| New records declare `usage_accounting_version: starvla-rsi.method-usage/v1`: |
| one decision may contain several model requests. Historical records without |
| that marker retain their original one-request audit semantics. |
|
|
| The Python SDK accepts optional `component_usage` in decision or typed-error |
| metadata, keyed by the declared model component ID, with the normal `Usage` |
| fields (`calls`, tokens, latency and estimated cost). Record measured increments, |
| not repeatedly accumulated totals. Missing component accounting remains unknown; |
| aggregate usage is never divided among models or filled in as zero. Optional |
| malformed component accounting produces a warning, not a different robot action. |
| Aggregate `Usage` remains the validated reference runner contract. |
|
|
| ## Results are independent of detailed logging |
|
|
| Read [recording and basic results](recording.md) for minimal/standard modes and |
| the closed-result contract. An external driver may emit that JSON layout without |
| importing the Python runner. Source hashes and deep audits remain optional |
| internal support, not public eligibility requirements. Failures, errors and the |
| preselected denominator must still be retained. Do not attribute all method |
| differences to the underlying model. |
|
|
