Ravula AIRavula AI

Physical-AI platform research canvases

Nine canvases on the platform stack behind physical AI for heavy industry: construction, mining and earth-moving autonomy. Each follows the same research-area framework and ends with open questions and sources.

Drafted from public sources in November 2026. Some claims rest on trade press and are labeled as such; check the cited source before relying on a figure.

  1. Canvas 1 of 9

    Physical-AI data flywheel

    Capture, governance, curation, labeling and dataset versioning, with feedback from deployed models.

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  2. Canvas 2 of 9

    Simulation and synthetic data

    Scenario generation, digital twins, synthetic data and the sim-to-real gap.

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  3. Canvas 3 of 9

    Training and post-training (VLA)

    Fine-tuning vision-language-action and foundation models, and what fits on one node.

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  4. Canvas 4 of 9

    Evaluation and safety assurance

    Evaluation harnesses, replay, scenario coverage and release gates for safety-relevant models.

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  5. Canvas 5 of 9

    Model lifecycle (MLOps) and governance

    Registry, lineage, approvals, over-the-air rollout and rollback, and model monitoring.

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  6. Canvas 6 of 9

    GPU orchestration and AI infrastructure

    Queues, quotas, scheduling and infrastructure as code for mixed training, simulation and inference workloads.

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  7. Canvas 7 of 9

    Inference optimization and serving

    Quantization, compilation, serving and benchmarking from data center to edge.

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  8. Canvas 8 of 9

    Observability, SLOs and GPU cost

    GPU telemetry, service-level objectives and cost per job.

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  9. Canvas 9 of 9

    Reference architecture

    How canvases 1 to 8 fit together: stages, contracts between layers and an implementation sequence.

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Services built on this work are on the physical-AI data architecture page.