I4h Workflow Train Rl

NVIDIAOfficialCreated by Isaac for Healthcare TeamDistributed by NVIDIA

Skill
Workflow
Data and AI
Apache-2.0
Medium risk

From the author

Use when training, evaluating, or exporting Workflow policies with online RSL-RL or RLinf, including RL checkpoint and Workflow handoff.

— the description by Isaac for Healthcare Team

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured procedure for managing reinforcement learning (RL) lifecycles within the Isaac for Healthcare (i4h) ecosystem. It supports training, evaluating, and exporting policies using either RSL-RL or RLinf backends. The workflow ensures that all RL-backed policies adhere to strict Scene, observation, action, and reward contracts before and after training. It handles the transition from raw training checkpoints to validated, deployable policy artifacts, including integration with the standard i4h Workflow validation runner.

What it does

Resolves and validates RL profiles and environment contracts for specific i4h workflows.
Executes training runs using RSL-RL or RLinf backends with support for dry-run verification.
Evaluates trained checkpoints against randomized episodes to ensure policy performance.
Exports trained models into TorchScript or native RLinf run bundles for deployment.
Validates exported policies through the standard i4h Workflow simulator runner.
Provides a framework for authoring new RL-backed workflows with custom adapters.

When to use it

When you need to train a new policy for a supported i4h workflow from scratch or a base checkpoint.
When you need to evaluate an existing RL checkpoint to verify performance metrics.
When you need to export a trained model into a format compatible with the i4h Workflow Task system.
When you are developing a new RL-backed workflow and need to integrate it into the i4h training pipeline.

Advantages

Ensures consistency by enforcing strict contract validation for observations, actions, and rewards.
Provides a unified interface for managing different RL backends like RSL-RL and RLinf.
Automates the transition from training to validated deployment artifacts.
Includes built-in dry-run capabilities to catch configuration errors before resource-intensive training.

Usage examples

“Train the ultrasound probe reach policy with PPO and evaluate 20 episodes.”: The agent performs a dry-run of the RSL-RL profile, executes the training, evaluates the resulting policy over 20 episodes, exports the TorchScript artifact, and validates it via the Workflow Task runner.
“RL post-train the Trocar policy from my local GR00T checkpoint.”: The agent verifies the G1/camera/action mapping, performs a dry-run, executes the RLinf training using the provided checkpoint, exports the model, and validates the remote policy Task.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/i4h-workflow-train-rl/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip i4h-workflow-train-rl.zip -d .agents/skills

Documentation by Isaac for Healthcare Team

Train a Workflow Policy with RL

Purpose

Resolve a maintained online-RL profile, verify its Scene/objective/model contracts, run the selected vectorized trainer, evaluate and export its artifact, and hand that artifact to normal Workflow policy validation.

Requirements

Run the Workflow setup skill first so its uv environments and pinned third-party checkouts are available.
Use a CUDA-capable Isaac Lab/Arena runtime for the RSL-RL workflow.
For Trocar, provide a local GR00T N1.5 3B base or SFT checkpoint and two visible local GPUs for the isolated controller and simulator runtimes. A compatible checkpoint is a complete local Hugging Face directory that the pinned GR00T N1.5/RLinf loader accepts without conversion; it must retain the N1.5 3B architecture and support the maintained three-camera plus 28-joint observation mapping and 28-D policy action head. Reject another model family, an exported inference-only Task artifact, or a checkpoint whose config changes those interfaces.

Instructions

Resolve the checkout, confirm Workflow setup is complete, and select training, evaluation, or export from the request. Evaluation/export with an existing checkpoint skips training; never start a new training run to satisfy those requests.
For a maintained profile, proceed directly to profile and contract checks. Only a request to author a new RL-backed Workflow takes the authoring branch below; return to these checks after adding its profile.
Confirm the Workflow, Scene, observations, actions, rewards, resets, termination, trainer, and runtime Task contracts, then dry-run training or evaluation with its exact checkpoint and options. Export uses profile/contract checks and has no dry-run mode.
For training, preflight the selected trainer runtime and train in the foreground. For evaluation/export, go directly to the matching commands below using the supplied checkpoint or run bundle.
Evaluate before export and validate the export through the normal Workflow runner when those stages are in scope. Report any required validation that could not be completed.

Resolve the checkout

Before resolving the checkout, use the maintained repository below or an alternative already selected by the user or trusted project configuration. Check an existing checkout's origin and working-tree changes before executing its scripts; an inherited environment variable alone does not establish trust in an alternative source. Honor any requested revision and preserve local changes. If the source is unexpected, stop and resolve it before cloning or launching.

export I4H_WORKFLOWS_REPO_URL="${I4H_WORKFLOWS_REPO_URL:-https://github.com/isaac-for-healthcare/i4h-workflows}"
I4H_REPO_DIR_NAME="${I4H_WORKFLOWS_REPO_URL%/}"
I4H_REPO_DIR_NAME="${I4H_REPO_DIR_NAME##*/}"
I4H_REPO_DIR_NAME="${I4H_REPO_DIR_NAME##*:}"
I4H_REPO_DIR_NAME="${I4H_REPO_DIR_NAME%.git}"
[ -n "$I4H_REPO_DIR_NAME" ] || { echo "Cannot derive a checkout name from I4H_WORKFLOWS_REPO_URL" >&2; exit 2; }
ROOT="${I4H_WORKFLOWS:-$(git rev-parse --show-toplevel 2>/dev/null)}"
if [ ! -d "$ROOT/workflows/i4h_workflows" ]; then
  ROOT="${I4H_WORKFLOWS:-$HOME/$I4H_REPO_DIR_NAME}"
  [ -d "$ROOT/workflows/i4h_workflows" ] || git clone "$I4H_WORKFLOWS_REPO_URL" "$ROOT" || exit 2
fi
[ -d "$ROOT/workflows/i4h_workflows" ] && [ -x "$ROOT/run.sh" ] || { echo "Incomplete workflow checkout: $ROOT" >&2; exit 2; }
export I4H_WORKFLOWS="$ROOT"
cd "$ROOT" || exit 2

Treat this resolver as part of the skill contract. I4H_WORKFLOWS_REPO_URL selects the clone source; I4H_WORKFLOWS selects or reuses a checkout. Never replace an existing checkout.

Resolve the profile and contracts

./train.sh rl list
./train.sh rl show <workflow>
./run.sh show <workflow> --mode policy

Read the profile under ./rl/profiles/, its referenced declarative trainer config under ./rl/config/, Workflow, Scene manifest and implementation, Arena objective config, embodiment, and runtime policy Task manifest before a long run.

Select the maintained path from the profile:

Workflow	Trainer	Starting artifact	Training observations/actions	Export and runtime
`ultrasound_probe_reach`	RSL-RL PPO	None; train from scratch	34-D joint/probe/target state → 6-D relative EE pose	TorchScript `policy.pt` → `rsl_rl/ultrasound_probe_reach` in-process Task
`assemble_trocar`	RLinf PPO actor/critic	Local GR00T N1.5 SFT/base checkpoint	Three cameras + 28 arm/hand joints → 28 policy actions padded to the 43-D Scene action	Native RLinf run bundle → GR00T inference export → existing remote `gr00t_n15/assemble_trocar` Task

For ultrasound_probe_reach, require the target to be sampled from verified upper-torso surface points, the table and phantom to remain fixed, success to require both position and orientation tolerance for consecutive steps, and the exported Task observation order to match training exactly.

For assemble_trocar, require Unitree G1 with Dex3 hands, front and both wrist cameras, the current 87-value body state (29 positions + 29 velocities + 29 torques), 14 Dex3 joint positions, the 28-D GR00T arm/hand mapping, the 15-value body-action prefix, and the maintained g1_trocar reward/termination contract. Do not copy another Trocar environment into the training tree.

Author a new RL-backed Workflow

Create and visibly validate the Workflow and Scene before adding training. Keep assets, embodiment, observations, actions, rewards, resets, termination, and success in their normal Arena and Workflow owners.
Add ./rl/profiles/<workflow>.yaml using the schema below. The filename and workflow value must match.
Add one declarative YAML trainer config under ./rl/config/; use <workflow>_<algorithm>_<backend>.yaml and point trainer_config to it as ../config/<file>.yaml.
Reuse a generic backend under ./rl/i4h_rl/backends/. Add ./rl/i4h_rl/adapters/<workflow>.py only when the maintained Scene needs workflow-specific observation, action, registration, or evaluation conversion. Do not add workflow branches to cli.py, sim_server.py, or a package __init__.py.
Train and evaluate the native checkpoint, export it into a reusable in-process or remote policy Task, add that Task to the Workflow's policy TaskGraph, and validate simulator success through the normal Workflow runner. Compare the runtime observation/action values with training, not only their dimensions and ordering: preserve coordinate frames, quaternion convention, normalization, action scaling, previous-action state, and reset semantics.

schema_version: 1
workflow: <workflow>
scene: <scene>
trainer: <rsl_rl-or-rlinf>
algorithm: ppo
adapter_module: i4h_rl.adapters.<workflow>
trainer_config: ../config/<workflow>_ppo_<backend>.yaml
train_task_id: <trainer-environment-id>
eval_task_id: <trainer-evaluation-environment-id>
task_description: <short instruction>
action_dof: <scene-action-width>
policy_action_dof: <policy-action-width>
state_dof: <state-observation-width>
cameras: []
default_num_envs: <positive-integer>
default_epochs: <positive-integer>
simulation:
  env_spacing: <positive-metres>
  presets: physx
  enable_cameras: false

Run ./train.sh rl show <workflow> and a training/evaluation --dry-run immediately. The CLI calls RLProfile.load in rl/i4h_rl/profile.py, validate_workflow_contract in contract.py, and the selected backend's validate_profile before launch. These checks cover profile fields and sources, backend support, dimensions, Scene cameras, and backend-specific task/config contracts. Require the applicable checks to exit successfully; if the checkout lacks these checks or a contract fails, stop before starting the simulator and report the failing check.

Dry-run

RSL-RL from scratch:

./train.sh rl ultrasound_probe_reach \
  --num-envs 128 \
  --epochs 400 \
  --dry-run

RLinf foundation-policy post-training:

./train.sh rl assemble_trocar \
  --model-path /absolute/path/to/gr00t-sft-checkpoint \
  --num-envs 64 \
  --epochs 1000 \
  --dry-run

These examples are training dry-runs. For evaluation use --eval --checkpoint <path> --dry-run; export rejects --dry-run, so check the profile with show and inspect the checkpoint and output destination before using the export command below. Inspect the resolved Scene, trainer, task IDs, observation/action dimensions, environment count, iteration/epoch count, config path, starting model when required, and explicit overrides. Keep user-requested resource values exact.

Preflight and train

The lightweight rl uv project owns profile resolution and command orchestration. Its trainer configs are YAML and do not import Isaac Lab. RSL-RL backend integration runs in the Arena environment because its trainer and simulator dependencies are compatible. Set its runtime explicitly only when the prepared Arena environment is not appropriate:

export I4H_RL_PYTHON=/absolute/path/to/arena-rsl-runtime-python

Train the compact RSL-RL example:

./train.sh rl ultrasound_probe_reach \
  --num-envs 128 \
  --epochs 400

Resolve TRAIN_RUN from the timestamped run dir: path printed by the training command. Do not guess or select a run by recency.

Trocar uses two isolated processes on one host because GR00T N1.5/RLinf requires Python 3.11 while the current Isaac Sim/Arena runtime requires Python 3.12. The model controller defaults to tasks/gr00t_n15/.venv/bin/python on physical GPU 0, the simulator defaults to arena/.venv/bin/python on physical GPU 1, and observations/actions cross a local Unix-socket data bridge. Override those runtimes when needed:

export I4H_RL_PYTHON=/absolute/path/to/gr00t-rlinf-python
export I4H_RL_SIM_PYTHON=/absolute/path/to/isaac-sim-arena-python

Train the GR00T/RLinf profile only with a compatible local starting checkpoint, the pinned RLinf checkout, and two visible GPUs:

./train.sh rl assemble_trocar \
  --model-path /absolute/path/to/gr00t-sft-checkpoint \
  --num-envs 64 \
  --epochs 1000

Keep training in the foreground. Preserve the run directory and exact command on failure. Do not silently lower environment counts or epochs. The current RSL-RL launcher does not expose resume or distributed multi-GPU training; a second GPU is not used automatically. Trocar's two GPUs isolate the model and simulator processes rather than distributing one trainer across both GPUs.

Evaluate, export, and hand off

For an evaluation/export-only request, set TRAIN_RUN or the checkpoint argument from the user-supplied artifact; the timestamped paths below are examples, not instructions to pick the newest run. Evaluate the RSL-RL checkpoint over independent randomized episodes:

TRAIN_RUN="$PWD/runs/ultrasound_probe_reach/YYYYMMDD_HHMMSS"

./train.sh rl ultrasound_probe_reach \
  --eval \
  --checkpoint "$TRAIN_RUN/model_final.pt" \
  --episodes 20

Export a simulator-compatible TorchScript actor, then validate the concrete Workflow Task:

./train.sh rl export ultrasound_probe_reach \
  --checkpoint "$TRAIN_RUN/model_final.pt" \
  --output-dir "$TRAIN_RUN/exported"

./run.sh ultrasound_probe_reach --policy \
  --checkpoint "$TRAIN_RUN/exported/policy.pt" \
  --episodes 20

Successful RLinf training writes checkpoint.json in the run directory. It points to the native FSDP checkpoint and records the starting GR00T model, so evaluation accepts the run bundle directly without repeating --model-path. A successful evaluation must write evaluation.json with non-empty TensorBoard metrics and at least one trajectory. Export also discovers the resolved RLinf training config stored in that run bundle:

TRAIN_RUN="$PWD/runs/assemble_trocar/YYYYMMDD_HHMMSS"

./train.sh rl assemble_trocar \
  --eval \
  --checkpoint "$TRAIN_RUN" \
  --video

./train.sh rl export assemble_trocar \
  --checkpoint "$TRAIN_RUN" \
  --output-dir "$TRAIN_RUN/exported"

./run.sh assemble_trocar --policy \
  --checkpoint "$TRAIN_RUN/exported" \
  --episodes 1

Treat the native trainer checkpoint as the training result and evaluate it before export. The GR00T inference export is runtime packaging for the remote Task, not a substitute for checkpoint evaluation. Require exit status 0, requested evaluation episodes when the trainer exposes them, non-empty metrics, expected checkpoint/export artifacts, and normal Workflow simulator success. Never claim success from loss curves or training exit alone.

Troubleshooting

Report the first missing runtime dependency, checkpoint path, registration failure, observation/action mismatch, non-finite loss, CUDA memory error, distributed/Ray/FSDP error, environment construction failure, or missing success artifact. Preserve the failed run directory and exact command. Retry once with the same configuration only after a diagnosed transient or missing-dependency failure has been resolved within the task. If it persists, or the failure is a contract mismatch, non-finite loss, or resource exhaustion, stop and report the blocker and preserved artifacts. Do not change the Scene objective or resource settings without user direction.

Limitations

The maintained profiles are ultrasound_probe_reach with RSL-RL and assemble_trocar with RLinf. RSL-RL resume, RSL-RL evaluation video, and distributed multi-GPU launch are not yet exposed. Trocar currently requires two visible local GPUs and its Unix-socket simulator bridge is single-host; a distributed simulator/controller deployment is not exposed. This skill does not perform supervised LeRobot fine-tuning or make unsupported workflows trainable.

Examples

Train the ultrasound probe reach policy with PPO and evaluate 20 episodes. → dry-run the RSL-RL profile, train from scratch, evaluate randomized episodes, export TorchScript, and validate the Workflow Task.
RL post-train the Trocar policy from my local GR00T checkpoint. → dry-run the RLinf profile, verify the G1/camera/action mapping, train, export a loadable GR00T checkpoint, and validate the remote policy Task.

Completion gate

Report the Workflow and Scene, trainer/profile/config, starting checkpoint when applicable, observation/action/reward/reset/termination contract, requested and completed resources, run directory, evaluation metrics, checkpoint and export artifacts, exact Workflow validation command and success rate, exit status, and any remaining runtime or hardware limitation.

Files

ATTRIBUTION.mdAdded by AutoGineer940 B
BENCHMARK.md8.0 KiB
LICENSELicence file added by AutoGineer11.2 KiB
SKILL.md14.7 KiB
agents/openai.yaml260 B
evals/evals.json9.8 KiB
skill-card.md4.2 KiB
skill.oms.sig4.7 KiB

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

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Attribution and licence

Created by

Isaac for Healthcare Team

Publisher

NVIDIA (Official)

Distributed by

NVIDIA

Source

NVIDIA/skills · skills/i4h-workflow-train-rl · commit fce3cca

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LICENSE (added by AutoGineer from LICENSE-APACHE)
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2026-11-09

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