cuRobo V2 motion planning

The image candidate remains 0.8.0-cuda13-b300-unbuilt and publication-quarantined until built-image checks and real GPU validation pass. Build from committed inputs; build.sh checks scoped source cleanliness and archives the exact commit for Docker. The tag family does not establish B300 validation.

For image changes, inspect actual dependency wheels and base layers. The cuDNN development base carries headers; deleting inherited files in a later layer does not remove those bytes. This image uses a non-cuDNN CUDA base and filters the locked cuDNN wheel inside its installation RUN, retaining only shared runtime libraries and notices. The bundled older and current cuDNN supplements differ on headers; preserve both evidence and use the common runtime boundary. Keep the filter's version/inventory checks, then inspect every built layer before publication. Passing its source tests does not establish clean image bytes.

Run the GPU workflow on Nebius through the ordinary workflow submit path:

npa workbench workflow validate-spec workflows/testing/curobo-benchmark.yaml
npa workbench workflow submit workflows/testing/curobo-benchmark.yaml --var bucket=<your-bucket>

This is the complete benchmark: both pinned datasets, in kinematic and 3 kg payload dynamics modes. curobo_mode=kinematic|dynamics selects one complete configuration. Do not silently replace either dataset with the upstream demo or add problem/time/job limits. The golden evaluation separately qualifies one real pose; it does not prove full-benchmark completion.

Exact implementation and limits

• Source is cuRobo V2 at 8e734f3ced1df898990bcd92de40abce475907db, using MotionPlanner and MotionPlannerCfg. V1 MotionGen examples are incompatible.
• Raw benchmark datasets are robometrics 81e3d1d605de84100d8ab880b43096aba221a48b. V2 source and Franka assets are Apache-2.0; dataset/source MIT and MotionBenchMaker BSD notices are retained. No weights, gated access or model-acceptance switch is required.
• The benchmark calls upstream's configuration loader, including its relaxed joint limits (0.2 radians), obstacle-to-OBB conversion and optimizer settings. Negative collision_buffer_ik inputs remain recorded as invalid.
• Every input contributes to the full denominator. Eligible success is reported separately. Failed solves remain failed; inverse-dynamics errors fail the job instead of becoming zero energy. FK path lengths are computed from actual tool positions, not upstream's placeholder end-effector metrics.
• A matching total count is insufficient evidence. Validate exact problem identities and invalid indices against benchmark_inventory.py, independently derived from the pinned YAML with file hashes. The runner checks those bytes, and report validation requires the known metrics for every status plus sample timeline consistency. Do not accept a self-consistent but invented journal.
• Energy is a Pinocchio inverse-dynamics proxy on the optimized joint trajectory. Planner success is upstream feasibility, not independent collision certification or authorization to move physical hardware.

Artifacts and diagnostics

The workflow writes its recipe, results/problems.jsonl, results/result.json, validation.json, reports/planning.rrd and reports/rrd-manifest.json under the same run prefix. S3 publication reads back and hashes every object. The mandatory RRD contains actual joint traces, tool paths from FK, timing/pose/dynamics metrics and every problem status. It contains no invented robot meshes.

On a GPU or upload failure the runtime retains a mode-0700 working directory and the already flushed problem journal. Inspect that evidence before any retry. Do not repeat a successful GPU run to repair telemetry. CUDA/Warp caches are node-local ephemeral state unless explicitly mounted by the workflow. After both result artifacts pass S3 readback verification, the runtime removes only that call's working directory. A cleanup failure emits a fixed warning and preserves the successful result; it must not trigger another GPU run.

Decode artifacts with rerun rrd verify and rerun rrd print -vv; compare problem and trajectory counts against the journal. A file extension or viewer opening does not establish correctness. Follow emit-reviewable-rrd for live artifact discovery and readback handoff.

Operator inputs and access surfaces

npa workbench curobo plan --input-path <s3-manifest> --output-path <s3-prefix> --run-id <run-id> accepts a strict npa.curobo.plan.v1 manifest: robot is franka.yml, and problems carries unique simple ids, seven-joint start, goal_pose.position_xyz, normalized goal_pose.quaternion_wxyz, and optional named cuboids (dims plus xyz/wxyz pose). Arbitrary robot YAML paths or executable configs are rejected. The SDK module is npa.sdk.workbench.curobo.

The optional service exposes the same operations. Configure CUROBO_TOKEN and CUROBO_ALLOWED_S3_ROOTS; it refuses missing auth, cross-root S3 writes, and concurrent GPU requests. It owns no deployment resources or worker launcher.

GPU and publication gates

CUDA 13 and driver 580 or later are required by the pinned upstream runtime. B200 and RTX PRO 6000 need separate real validation because SM100 and SM120 are different CUDA majors. The headless solver/FK recording needs no RT cores. The image remains publication-quarantined until exact-byte scans and physical GPU validation pass; a checked-in Dockerfile is not a published capability. Read health-preflight, gpu-selection, secure-image-build and solution-licensing before building/provisioning/submitting.

Verify source changes

npa/.venv/bin/python -m pytest npa/tests/workbench/test_curobo.py npa/tests/cli/test_curobo_cli.py npa/tests/workflows/test_curobo_workflow.py -q

Then run applicable pre-pr-validation, container, catalog, skill and live-submit gates. Never report mocked unit tests as GPU or benchmark evidence.