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name: inference-serving description: "Serve and consume Dexbotic VLA/VLM policies through the legacy or v1 HTTP APIs and the BasePolicy contract." metadata: disco-role: operating disable-model-invocation: true license: MIT

Dexbotic inference serving

Use this route for policy wrappers, HTTP server/client contracts, action chunks, reset semantics, capability discovery, and optional DM0 realtime serving. Read HTTP and policy contract before wiring a client and troubleshooting when requests fail. Route benchmark setup and hardware bridges to evaluation-deployment.

Operating sequence

1. Start from a checkpoint whose model family, processor/tokenizer, action dimension, camera order, normalization stats, and control mode are known.
2. Query GET /health, then GET /v1/capabilities; do not guess whether state is required or whether actions are absolute/relative.
3. Build one observation with 1-based image slots, a prompt, and state only when required/used. For the Python client, convert RGB arrays correctly and choose use_delta from the declared action mode.
4. Send a bounded request with sampling.num_steps, cfg_scale, and optional seed; verify the action chunk shape and units before passing actions to a robot or simulator.
5. Call /v1/reset at episode boundaries for stateful policies such as MemVLA. Treat a reset failure as a session boundary problem, not a model-quality result.
6. Use the legacy /process_frame route only when the consumer requires multipart compatibility.

No server, checkpoint download, physical control loop, or Triton weight conversion is run by the bundled smoke helper unless explicitly requested with a reachable local URL.