Physical AI
16 docs

Research

Cross-cutting docs synthesizing the state of Physical AI in 2026 — AV industry, robotics foundation models, simulation, labeling, world models, open problems, and a sequenced learning roadmap — plus a four-part automotive-industry history (the A0–A3 annex) tracing the car from 1886 to the EV & autonomy era.

  1. 00

    Overview — Physical AI in 2026

    ~30 min

    Cross-cutting synthesis of the field: data is the bottleneck, the data engine is the product, the modular/E2E pendulum is dissolving.

  2. 01

    AV industry and data

    ~90 min

    Who collects what at what scale: Tesla, Waymo, Mobileye, Wayve, Waabi. Fleet logs vs customer-shadow vs sim vs world models.

  3. 02

    Robotics foundation models

    ~60 min

    The VLA recipe — RT-X, OpenVLA, π0/π0.5, Helix, Gemini Robotics. Open X-Embodiment, demo collection economics.

  4. 03

    Simulation and synthetic data

    ~90 min

    The competitor map. Applied Intuition Simian, Nvidia Cosmos + Isaac, CARLA, Waymax, the death of pure-rendering shops.

  5. 04

    Labeling and data curation

    ~120 min

    The most important doc for the role. Data-engine philosophy, FiftyOne/SAM2/OpenSCENARIO tools, foundation models as labelers.

  6. 05

    World models and generative

    ~60 min

    Cosmos, GAIA, GR00T-Dreams, Wayve PRISM-1. World models as data engine and as eval substrate.

  7. 06

    Open problems and benchmarks

    ~60 min

    The RAND 11-billion-mile result; SOTIF, ODD, V&V; benchmarks that drive the field forward.

  8. 07

    Learning roadmap with mini-projects

    ~45 min

    The 8-to-12-week plan. Each phase produces a tangible artifact; each project has a definite "done" criterion.

  9. 08

    Connections and gaps

    ~30 min

    How the projects link together — the critical chain, the round-2 audit, the bijective folder-rename mapping.

  10. 09

    Research frontier and outlook

    ~50 min

    What's next: open research questions, where the next 12–24 months of progress will land, what to watch.

  11. 10

    Glossary

    ~35 min

    Every recurring term in the repo, defined with the context that makes it useful — from ODD and SOTIF to VLA, JEPA, and the data engine. Grouped by theme; Ctrl-F friendly.

  12. 11

    Key papers and blogs — reading list

    ~25 min

    The ~50 canonical artifacts consolidated in one place: a "read only ten things" starter table, theme-by-theme depth, and the staying-current feeds.

  13. A0

    Auto industry — overview & timeline

    ~12 min

    How the car industry evolved from one patented three-wheeler (1886) into a software-defined, autonomous industry — and why that arc maps onto Physical AI. Map for the 4-part series.

  14. A1

    Auto history: Motorwagen to mass production (1886–2000s)

    ~25 min

    Invention (Benz, Daimler, Panhard, Peugeot), Ford's moving line, Sloan's brand ladder, the Toyota Production System, oil shocks, and the 2008–09 GM/Chrysler crisis.

  15. A2

    Auto players: OEMs, Tier-1 suppliers & consolidation

    ~18 min

    The structural map: the OEM-over-supplier pyramid, the major OEM groups, the Tier-1 layer (Bosch, Denso, Continental, ZF, Magna), and how autonomy is rewriting the org chart.

  16. A3

    Auto's EV & autonomy era: the data-engine turn

    ~30 min

    EVs (EV1, Prius, Tesla, BYD, batteries/LFP), the AV stack (DARPA, SAE levels, Waymo, robotaxis, end-to-end learning), software-defined vehicles, and the data flywheel. The deep end.