| <p align="center"> |
| <img src="assets/logo.svg" alt="StarVLA-RSI" width="10%"> |
| </p> |
|
|
| # StarVLA-RSI |
|
|
| Evaluate **agent methods** across embodied benchmarks in simulation and, |
| eventually, real robots. Bring your own decision loop, prompts, memory, tools |
| and one or more general-purpose models. Each benchmark service owns its robot |
| interface, native execution budget and success criteria. Compare declared |
| methods within the same benchmark/protocol, not an unexplained model-name score. |
| RoboDojo is one integration, not a separate framework. |
|
|
| This is an evaluation framework, not a model-training repository. |
| Direct control limits environment access and the robot interface, not how the |
| agent reasons internally. No VLA-assisted hybrid controller or fallback task |
| policy is inserted between a method and its benchmark. Fixed-prompt, single-model |
| policies remain useful controlled baselines, not the only permitted methods. |
|
|
| > Status: experimental. Development SIM services and protocol drafts coexist; |
| > inspect the per-benchmark support matrix before use. |
| > Native-aligned full evaluation presets, verified provider integrations and |
| > unified real-robot adapters are not yet complete. Adapter availability does |
| > not imply the simulator or licensed assets are installed. |
|
|
| ## Quick start |
|
|
| Use Python 3.10 or newer, from a repository checkout: |
|
|
| ```sh |
| pip install -e . |
| rsi-bench list |
| rsi-bench serve --benchmark robodojo --help |
| ``` |
|
|
| The selector delegates to the benchmark's existing server; it does not install |
| its simulator or change its sensors, actions or task schedule. Follow that |
| benchmark's setup guide, then launch with a fixed configuration, seed and fresh |
| output directory. See [service startup and interaction history](docs/evaluation.md). |
|
|
| For dependency-free infrastructure checks (no native robot or model request): |
|
|
| ```sh |
| python -B -m benchkit catalog --check |
| python -B -m benchkit list |
| python -B -m benchkit smoke --episodes 3 --seed 7 |
| ``` |
|
|
| The smoke check is a dependency-free synthetic fixture, not a robot capability |
| score. It writes records under `results/rsi-smoke/`. Install the environment |
| described by the selected benchmark before attempting a native task. |
|
|
| - [Declare and connect an agent method](docs/agent-methods.md) |
| - [Optional interaction recording and basic results](docs/recording.md) |
| - [Run an evaluation](docs/evaluation.md) |
| - [Architecture and ownership](docs/architecture.md) |
| - [Bench Server visual introduction](assets/bench_server/index.html) |
| - [Add a benchmark server](docs/benchmark-server-guide.md) |
| - [Policy interaction protocol](benchmarks/simBenchmarks/POLICY_PROTOCOL.md) |
| - [Simulator adapter interface](benchmarks/simBenchmarks/INTERFACE.md) |
| - [Run SIMs as independent servers](benchmarks/simBenchmarks/SERVER_PROTOCOL.md) |
| - [Implement a policy](AgentPolicies/README.md) |
| - [Real-robot integration status](benchmarks/realRobots/README.md) |
|
|
| ## Repository structure |
|
|
| Upgrading an existing checkout? See the [package migration notes](docs/package-migration.md) |
| for the `examples` → `benchmarks` and `harness` → `benchkit` changes. |
|
|
| ```text |
| benchkit/ shared communication and result/output tools |
| sim_server/ service selector, transport and session lifecycle |
| reports/interaction_history.py closed-run interaction HTML / JSON export |
| reports/basic_result.py outcome view without required interaction logs |
| methods.py / recording.py method declarations and optional record helpers |
| campaigns/ central fixed-plan/case scheduling and progress |
| catalog/ benchmark metadata, not native adapters |
| diagnostics/ non-capability infrastructure diagnostics |
| benchmarks/ |
| simBenchmarks/<benchmark>/ environment, tasks, adapter and native protocol |
| server.py benchmark-owned independent SIM entrypoint |
| bench_server/protocol/ task definitions and sample plans, where migrated |
| realRobots/ hardware references; unified adapters pending |
| AgentPolicies/ |
| <method-profile>/ policy, model composition, prompts and dependencies |
| run.py model-owned trusted evaluation entrypoint, where implemented |
| results/<benchmark>/ reports, indices and ignored raw runs/media |
| policy_support/ reusable agent-side loop and policy behavior |
| docs/ public design and usage guides |
| ``` |
|
|
| The benchmark answers “what happens when this action executes?” The policy |
| answers “what action should I take?” Its trusted driver communicates with the |
| service through `benchkit/sim_server` and uses the other `benchkit` record/report tools. Planning and |
| voluntary-stop preferences belong to the policy, not the native benchmark. |
| The actor never receives the driver's administrative client or credentials. |
| The model-owned client driver currently starts with |
| [`AgentPolicies/gpt6_astra/run.py`](AgentPolicies/gpt6_astra/run.py). Its implementations, |
| transports, presets and guides are grouped by role. Fixed-plan tools use |
| `python -m benchkit.campaigns`; no old `benchkit.runner` or `benchkit.campaign` |
| forwarding modules are retained. Import the optional runner from |
| `AgentPolicies.policy_support.runner`. |
|
|
| Central task scheduling remains in this version. Current campaign commands |
| coordinate frozen local cases; they are not yet integrated with remote service |
| campaigns. Use the [workflow guide](docs/evaluation.md#choose-a-workflow) to choose |
| the service/driver path or retained campaign path, rather than combining them. |
| Automatic plugins, expanded scheduling, a comprehensive CLI and cross-benchmark |
| rankings are planned extensions, not claims of current support. |
|
|
| ## Available integrations |
|
|
| | Family | Current status | Entry point | |
| | --- | --- | --- | |
| | SimplerEnv | Development adapter | [Setup and protocol](benchmarks/simBenchmarks/SimplerEnv/README.md) | |
| | LIBERO | Development adapter | [Setup and protocol](benchmarks/simBenchmarks/LIBERO/README.md) | |
| | SIMPLE | Development adapter | [Setup and protocol](benchmarks/simBenchmarks/SIMPLE/README.md) | |
| | RoboCasa365 | Development adapter | [Setup and protocol](benchmarks/simBenchmarks/Robocasa_365/README.md) | |
| | RoboCasa Tabletop | Development adapter | [Setup and protocol](benchmarks/simBenchmarks/Robocasa_tabletop/README.md) | |
| | RoboDojo | Qualified development adapter; portable setup incomplete | [Scope and limitations](benchmarks/simBenchmarks/RoboDojo/README.md) | |
| | MetaWorld MT50 | Standalone development SIM server | [Setup and protocol](benchmarks/simBenchmarks/MetaWorld/README.md) | |
| | LIBERO-Plus | Separate variation-aware development SIM server | [Setup and protocol](benchmarks/simBenchmarks/LIBERO-plus/README.md) | |
| | RoboTwin | Two-task clean direct-joint development SIM server | [Setup and protocol](benchmarks/simBenchmarks/Robotwin/README.md) | |
| | DOMINO | One-task fixed-manifest direct-joint development SIM server; native validation pending | [Setup and protocol](benchmarks/simBenchmarks/DOMINO/README.md) | |
| | BEHAVIOR 2026 | One-task RGB/proprioception debug SIM server; one-case native smoke passed, full challenge parity pending | [Setup and protocol](benchmarks/simBenchmarks/Behavior/README.md) | |
| | VLA-Arena | Level-0 direct-control development SIM server | [Setup and protocol](benchmarks/simBenchmarks/VLA-Arena/README.md) | |
| | CALVIN | Debug-assets sequence development SIM server | [Setup and protocol](benchmarks/simBenchmarks/calvin/README.md) | |
| | VLN-CE | Deferred pending authorized navigation assets | [Catalog](benchmarks/simBenchmarks/README.md) | |
|
|
| The catalog contains the original 13 simulation families plus SIMPLE. The |
| [service support matrix](benchmarks/simBenchmarks/SERVICE_SUPPORT.md) distinguishes |
| entrypoint availability, setup requirements and native validation. No standalone |
| development server implies native full parity. Environment assets and their |
| licenses are separate from this repository; VLN-CE is deferred until authorized |
| navigation datasets are available. |
|
|
| ## Evaluation guarantees and limits |
|
|
| - Freeze tasks, initial states, seeds, action/observation contracts, budgets and |
| model configuration before execution. |
| - `fast` plans select tasks with one attempt each. A future `full` preset must |
| follow the verified native task/variant/episode schedule; it is not a retry loop. |
| - Keep failures and errors. Report attempted and completed denominators |
| separately; never turn infrastructure errors into hidden missing samples. |
| - Record policy calls, executed action decisions and control steps separately. |
| One action decision can execute several controls. |
| - Use native success and scoring. Policies receive only declared observations, |
| not simulator object state, reward predicates or evaluator internals. |
| - Interaction recording is optional and enabled by default. Disabling it does |
| not prevent participation or result publication; essential outcomes, method |
| configuration, denominators, counters and errors remain available. |
| - Report inference usage when available, per model component when measured; |
| unknown usage stays unknown. No universal inference budget is imposed. |
| - Keep useful recorded protocol, upstream revision, environment and asset |
| provenance. Content hashes and source snapshots remain internal integrity |
| support; viewing an interaction history does not require a full source audit. |
| - Preserve original events and results; never rewrite an outcome or silently |
| rerun a task to improve its presentation. |
|
|
| The current `anonymous_code_agent` profile uses an experimental file mailbox. |
| It does not authenticate the actor's provider/model identity, meter hidden |
| provider requests or enforce OS isolation. Unknown usage is recorded as |
| `null`. [`gpt6_astra`](AgentPolicies/gpt6_astra/README.md) offers several versioned |
| methods; its server driver uses generic persistent Direct v3 with an explicit benchmark. The |
| Native baseline (the factory default) and Planned method use declared numeric |
| action interfaces. The three methods have distinct compatibility and evidence; |
| retired API/EEF methods remain only in Git and frozen records. |
| A passing offline check is not a new capability score. |
| Declared model identity is distinct from provider receipts and |
| from historical owner-reported attribution. `opus` remains unconfigured. |
|
|
| ## Development |
|
|
| The core does not import native simulator SDKs. Lightweight checks are organized |
| beside the code they protect: |
|
|
| ```sh |
| make check |
| # Use an environment with NumPy, Pillow and PyAV for media fixtures: |
| python -B -m benchkit.checks.run_regressions --jobs 3 --output /absolute/new/check-report |
| ``` |
|
|
| To add a benchmark, implement [BenchmarkAdapter and EpisodeSession](benchkit/interfaces.py) |
| under its `benchmarks/` directory, pin the native evaluator and provide explicit |
| contracts and selection plans. To add a method, implement the service protocol |
| in your trusted driver and declare its configuration/model composition. The |
| Python `Policy` interface and reference loop are optional conveniences, not a |
| required framework or language. |
| See [AGENTS.md](AGENTS.md) for repository safety and contribution boundaries. |
|
|
| Internal working notes under `project_readmes/` are local and Git-ignored. |
| Public usage must not depend on them. Raw runs, generated media, credentials and |
| local environment artifacts are also excluded from Git; lightweight model |
| reports and provenance indices may be tracked. |
|
|
| The grouped [.gitignore](.gitignore) keeps generated artifacts out of Git, not |
| source, tests or published results. `git status --short --ignored` marks ignored |
| paths with `!!`; `git check-ignore -v -- PATH` identifies the matching rule. |
| Ignored files can remain on disk. Editors may show them dimmed rather than hide |
| them; that display is independent of whether Git will track them. |
|
|
| ## Attribution |
|
|
| Derived from [StarVLA](https://github.com/starVLA/starVLA), retaining its MIT |
| license and citation metadata. Original model-training implementations remain |
| in the upstream project and Git history, not the active evaluation surface here. |
|
|
