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isaac-cable-insertion-policy-training

Set up the standalone IsaacLabTraining package from NVIDIA-ISAAC-ROS/isaac_ros_manipulation, then launch, monitor, resume, evaluate, and tune DisplayPort insertion policies without a separate Isaac Lab source installation. Use for setup and campaign planning, dependency repair, curriculum or PhysX troubleshooting, video evidence, and long GPU runs.

Published by NVIDIA ↗NVIDIA/skills

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SKILL.md

name: isaac-cable-insertion-policy-trainingdescription: Set up the standalone IsaacLabTraining package from NVIDIA-ISAAC-ROS/isaac_ros_manipulation, then launch, monitor, resume, evaluate, and tune DisplayPort insertion policies without a separate Isaac Lab source installation. Use for setup and campaign planning, dependency repair, curriculum or PhysX troubleshooting, video evidence, and long GPU runs.license: CC-BY-4.0 AND Apache-2.0metadata:   author: "NVIDIA Isaac Team <info@nvidia.com>"  tags:     - isaac-lab    - displayport-insertion    - reinforcement-learning

Isaac Cable Insertion Policy Training

Purpose

Launch, monitor, troubleshoot, and tune DisplayPort insertion policies from the manipulation repository’s standalone isaaclab_training package. Isaac Lab is a pinned dependency installed by uv; never require a separate Isaac Lab checkout. Keep the training project's source, pyproject.toml, and uv.lock unchanged. The skill manages supplemental packages and ONNX Runtime selection in .venv. The default is task-space ROS-inference training with video and reproducible run logs.

For a planning-only request, read the relevant bundled references and describe the proposed commands and checks without running setup, preflight, or training. Use supplied hypothetical facts as inputs; do not claim they were verified.

Inputs

The training repository path is optional. Honor a supplied location or explicit fresh-install request; otherwise check this skill's setup/session records for a previously installed standalone repository and try to reuse its verified .venv as described in Setup. If no suitable installation exists, choose an unused ~/isaac_ros_manipulation destination. The working directory and plan’s training_root are the repository’s isaaclab_training/ subdirectory, which owns .venv, pyproject.toml, and uv.lock. Accept an optional Git ref, run count, time budget, task variant, seed, and hyperparameter overrides. Default to release-r1, task-space training, and three completed runs when the budget permits.

Instructions

For independent validation of an existing policy, follow Independent validation with fresh seeds. Reuse the verified checkpoint and originating session; choose unused evaluation seeds and report the independent result separately. This request does not require retraining or a new installation.

Read inputs, EULA, and preflight before launching. Use setup for installation or repair, logging/supervisor for campaign execution, and tuning between runs:

  1. Inputs and boundaries: task defaults, repository access, and scope.
  2. Omniverse EULA: read the consent rules before setup; combine missing consent with the setup/location choice and record an explicit Yes before installing or launching Isaac Sim.
  3. Setup and repair: clone or reuse the public manipulation repository, sync the sim group with the documented CPU ONNX exclusion, install skill-managed supplements and requested video support, and verify before training.
  4. Preflight: verify the locked Isaac Lab wheel, editable training package, installed dependency consistency, GPU, and task configuration.
  5. Logging: create a new invocation log and record every attempted run.
  6. Run and monitor: launch durably, detect missing exit markers, check video, and handle errors.
  7. Tuning and final report: compare runs, tune in the guide's order, and report back after completion.
  8. Reusable supervisor: create a JSON campaign plan, launch with optional tmux, resume safely, and queue the next hypothesis.

Run from the selected isaaclab_training package root through the shared launch environment. Discovery, consent checks and repository creation and initial uv installation are the setup exceptions. Use the committed supervisor and evaluator; keep one report and the supervisor's records in the skill's ignored logs/ directory. Logging defines the artifacts and provenance to retain. Respect the user's time budget and report the final outcome.

Training command

Use the canonical task-space command from the prepared isaaclab_training directory. That reference covers task selection, video cadence, and monitoring. Choose and record shorter rollout, curriculum, and iteration settings when a time limit makes the full run impractical.

Troubleshooting

Use Run and monitor for PhysX collision stack overflow, CUDA out-of-memory, video-source errors, bounded retries, and escalation of unclear failures.