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      "title": "Translate learned representations into robot action units",
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      "title": "Explore in Atlas",
      "url": "https://semiaifoundry.com/ai-atlas/#vision-language-action-interface"
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      "title": "OpenVLA: An Open-Source Vision-Language-Action Model",
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      "title": "Predict transitions conditional on actions",
      "url": "https://semiaifoundry.com/learn/atlas/action-conditioned-world-model/"
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      "title": "Learn behavior from a model and test in the environment",
      "url": "https://semiaifoundry.com/learn/atlas/imagined-rollouts-policy-learning/"
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  "content": "model-lifecycle-world-action · Atlas v1.3.0\n\n# Translate learned representations into robot action units\n\nDecode action tokens into physical commands with explicit frame, units and validity checks.\n\n[Explore in Atlas](https://semiaifoundry.com/ai-atlas/#vision-language-action-interface)Mathematical, data and behavior space; logical dimensions are not physical distance\n\nLearning objectiveDecode action tokens into physical commands with explicit frame, units and validity checks.\n\n## The mechanism\n\nA vision-language-action interface produces commands for an environment, not merely descriptive text. Discrete action tokens require an inverse mapping into physical quantities, including coordinate frame, units, control rate and bounds. The OpenVLA paper uses 256 bins per action dimension over training-data quantile ranges; this is a recipe-specific encoding rather than a universal actuator protocol. Quantization error and out-of-range clipping require separate accounting. A syntactically valid token can still request an unsafe or meaningless command if its frame or scale is mismatched.\n\n1.  Declare the action dimension, reference frame, units, control period and allowed range.\n2.  Quantize a training action under one stated binning convention.\n3.  Decode to the chosen representative and measure physical error.\n4.  Reject invalid coordinates, stale observations or incompatible actuator contracts before execution; a model probability is not authorization.\n\n## Science and assumptions\n\n### Translate learned representations into robot action units · worked relation\n\n$$\n\\Delta=\\frac{u-l}{K},\\quad b=\\left\\lfloor\\frac{a-l}{\\Delta}\\right\\rfloor,\\quad \\hat a=l+(b+\\tfrac12)\\Delta\n$$\n\nDecode action tokens into physical commands with explicit frame, units and validity checks.\n\nSymbols and units\n\nl,u\n\nLower and upper declared action limits\n\nm\n\nK\n\nEqual-width bins\n\nbins\n\nDelta\n\nBin width\n\nm\n\nb\n\nZero-based bin index, with endpoint convention\n\nindex\n\na,hat a\n\nCommand and midpoint reconstruction\n\nm\n\nAn authored four-bin position command spans \\[−1,1\\] m with equal width 0.5 m and midpoint decoding. Centers are \\[−0.75,−0.25,0.25,0.75\\] m. Command 0.3 m maps to bin 2 (zero-based) and decodes to 0.25 m, error −0.05 m. Interior midpoint error is at most 0.25 m. A 2 m command is outside the declared range; silently clipping it loses 1.25 m relative to the top center and should not be reported as ordinary quantization error.\n\nAssumptions Authored four-bin midpoint convention, range checked first; a=u maps to final bin. It is not a verbatim implementation of OpenVLA token IDs or quantile calibration.\n\n## Engineering example\n\nAn authored four-bin position command spans \\[−1,1\\] m with equal width 0.5 m and midpoint decoding. Centers are \\[−0.75,−0.25,0.25,0.75\\] m. Command 0.3 m maps to bin 2 (zero-based) and decodes to 0.25 m, error −0.05 m. Interior midpoint error is at most 0.25 m. A 2 m command is outside the declared range; silently clipping it loses 1.25 m relative to the top center and should not be reported as ordinary quantization error.\n\n## Distinctions and tradeoffs\n\nIt is a mistake to conclude: Every token automatically authorizes any physical motion.\n\nDiscrete tokens reuse sequence-model interfaces but introduce resolution, range and control-contract constraints. Textual plausibility does not establish valid actuation.\n\nDistinguish predicting observations, choosing actions and controlling a changing environment. Decode action tokens into physical commands with explicit frame, units and validity checks.\n\n## Specifications and interfaces\n\nLearning mode\n\nworked example\n\nThe executable decoder has 423 parameters; worked extensions do not imply an additional simulator.\n\nWorked contract\n\nDecode action tokens into physical commands with explicit frame, units and validity checks.\n\nAuthored four-bin midpoint convention, range checked first; a=u maps to final bin. It is not a verbatim implementation of OpenVLA token IDs or quantile calibration.\n\n### Interfaces\n\nInputs and state Input\n\nDeclare the action dimension, reference frame, units, control period and allowed range.\n\nResult and check Output\n\nDecode action tokens into physical commands with explicit frame, units and validity checks.\n\n## Check your understanding\n\n### Why must a 2 m command be handled separately?\n\n1.  Every token automatically authorizes any physical motion.\n2.  Quantization error is always zero for model outputs.\n3.  It lies outside the declared encoding range and invalidates the interior error bound.\n\nAnswer and reasoning\n\nIt lies outside the declared encoding range and invalidates the interior error bound.\n\nIt lies outside the declared encoding range and invalidates the interior error bound. Authored four-bin midpoint convention, range checked first; a=u maps to final bin. It is not a verbatim implementation of OpenVLA token IDs or quantile calibration.\n\n## Evidence and scope\n\nMode: worked example. Authored four-bin midpoint convention, range checked first; a=u maps to final bin. It is not a verbatim implementation of OpenVLA token IDs or quantile calibration. Numerical examples are authored calculations, except explicitly identified automated core fixtures. Primary sources support the scoped mechanism; no human specialist review or learner outcome is implied.\n\nModel lifecycle studio: worked example. Authored four-bin midpoint convention, range checked first; a=u maps to final bin. It is not a verbatim implementation of OpenVLA token IDs or quantile calibration.\n\n## Sources\n\n-   [OpenVLA: An Open-Source Vision-Language-Action Model](https://arxiv.org/html/2406.09246v1)\n\n[Published lesson data](https://semiaifoundry.com/ai-atlas/lesson-data/vision-language-action-interface-76dbc75ad34ff28a.json) · [Atlas releases and licensing](https://semiaifoundry.com/ai-atlas/licenses.html)\n\n## Continue exploring\n\n### Foundations\n\n-   [Patches, projection and visual instruction tuning](https://semiaifoundry.com/learn/atlas/visual-language-bridge/)\n-   [Predict transitions conditional on actions](https://semiaifoundry.com/learn/atlas/action-conditioned-world-model/)\n\n### Related lessons\n\n-   [Learn behavior from a model and test in the environment](https://semiaifoundry.com/learn/atlas/imagined-rollouts-policy-learning/)\n-   [Predict action chunks and replan with feedback](https://semiaifoundry.com/learn/atlas/diffusion-action-horizon/)"
}