I4h Workflow Dataset Teleop
NVIDIAOfficialCreated by Isaac for Healthcare TeamDistributed by NVIDIA
- Skill
- Workflow
- Automation and integrations
- Apache-2.0
- Medium risk
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a structured procedure for recording human-controlled teleoperation demonstrations within the Isaac for Healthcare (i4h) framework. It facilitates the capture of robot actions, state, camera feeds, and task outcomes into standardized HDF5 files. The process requires an active human operator using supported input devices such as keyboards, VR controllers, or bus inputs to perform tasks in a simulated environment.
What it does
- Resolves workflow repositories and teleoperation device configurations.
- Executes simulation environments for interactive human-led task performance.
- Records demonstration data including actions, state, and camera streams into HDF5 format.
- Provides validation steps to inspect recorded segments and action motion.
- Supports custom run directories for integration into larger data pipelines.
When to use it
- When you need to collect human-demonstration data for training or analysis.
- When working with i4h-compatible workflows that support teleoperation modes.
- When you have a human operator available to perform tasks via supported input devices.
- When you need to ensure recorded data adheres to the standard HDF5 contract.
Advantages
- Ensures consistent data structure across different teleoperation sessions.
- Integrates directly with existing simulation runners and inspection tools.
- Supports multiple input modalities including VR and keyboard.
- Provides clear verification steps to confirm data integrity and task success.
Usage examples
- “Record 5 keyboard teleop demonstrations for locomanip tray pick and place.”
- The agent initiates the specified workflow in teleop mode using the keyboard device, prompts the operator to complete 5 successful episodes, and saves the data to an HDF5 file for verification.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/nvidia/i4h-workflow-dataset-teleop/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip i4h-workflow-dataset-teleop.zip -d .agents/skills
Documentation by Isaac for Healthcare Team
Record Teleop Demonstrations
Purpose
Run the workflow's declared teleop graph through the shared SimulationRunner so actions, state, cameras, segments, attempts, and outcomes use the normal HDF5 contract.
Instructions
Resolve the base checkout and live teleop device contract.
Choose a supported human-input device.
Record in the foreground through
run.sh.Inspect visible motion and HDF5 content.
Resolve support
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" "$ROOT"
fi
export I4H_WORKFLOWS="$ROOT"
cd "$ROOT"
./run.sh list
./run.sh show <workflow> --mode teleopTreat the resolver above as part of the skill contract: a hosted copy may run outside the base repository, so never assume the current checkout contains workflows/i4h_workflows. I4H_WORKFLOWS_REPO_URL selects the clone source. When I4H_WORKFLOWS is unset, derive the fallback directory from that URL; set I4H_WORKFLOWS only to reuse or choose a specific destination. Never replace an existing checkout.
Require teleop in the live mode list. Read the workflow builder, its scene manifest teleop override, and the embodiment manifest's teleop_devices. Do not maintain a static support table in the skill.
Choose input
Use the named device when supported.
Otherwise use the workflow builder's default device.
Keep interactive keyboard/leader/VR/bus sessions visible and in the foreground. Surface the device controls and require the human operator to complete the task.
Never pretend to provide human input in an unattended shell.
Record
./run.sh <workflow> --teleop <device> \
--episodes <N> --attempts 3 \
--recordOmit <device> to use the workflow default. Bare --record writes demos.hdf5 inside the launcher's automatic run directory. Read the absolute directory from the ==> run dir ... line or run.json; do not recreate its timestamp in the shell. When a larger pipeline requires a caller-selected shared directory, pass --run-dir "$RUN_DIR" --record demos.hdf5; the launcher creates the directory and anchors the relative recording name inside it. An absolute --record path remains supported.
Verify
Require the final N/N episodes succeeded summary. Then inspect content:
RUN_DIR="<absolute run_dir from run.json or launcher output>"
uv run --project tools/dataset i4h-dataset inspect "$RUN_DIR/demos.hdf5" --segments
uv run --project tools/dataset i4h-dataset actions "$RUN_DIR/demos.hdf5"Visually confirm that the operator completes the requested task, robot motion matches the input device, and all expected cameras record the same behavior. Treat zero saved episodes, missing observations, absent action motion, or an unsuccessful task outcome as failure. Stop leftovers with ./stop.sh all.
Troubleshooting
On device or width errors, compare the workflow teleop builder, Scene mode override, and embodiment devices.
Prerequisites
Require a workflow with teleop, a supported device, a working simulator, and a present operator for interactive input.
Limitations
Teleop records human input and requires an operator for interactive devices. Record autonomous rule-based Tasks with i4h-workflow-validate instead.
Examples
Record 5 keyboard teleop demonstrations for locomanip tray pick and place.→ use G1's supported keyboard device, require a human operator, and verify the recorded action motion.
Completion gate
Report workflow, mode/device, controls, requested/saved episodes, attempts, visual result, HDF5 path, dimensions/segments, and whether a human operator completed the task.
Files
- ATTRIBUTION.mdAdded by AutoGineer958 B
- BENCHMARK.md7.0 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md4.6 KiB
- evals/evals.json2.1 KiB
- skill-card.md3.8 KiB
- skill.oms.sig4.5 KiB
evals/evals.json
[
{
"id": "i4h-workflow-dataset-teleop-g1-keyboard-five",
"question": "Record 5 keyboard teleop demonstrations for locomanip tray pick and place.",
"expected_skill": "i4h-workflow-dataset-teleop",
"ground_truth": "This G1 teleop prompt routes to visible keyboard_23d demonstration recording with a human operator for five episodes.",
"expected_behavior": [
"routes to i4h-workflow-dataset-teleop",
"maps the prompt to locomanip_tray_pick_and_place",
"selects the supported keyboard_23d device",
"keeps the simulator visible and requires a human operator",
"runs five episodes through the normal teleop action path",
"records the demonstrations to HDF5",
"inspects segments, action motion, cameras, and target progress after recording",
"reports the output HDF5 path"
]
},
{
"id": "i4h-workflow-dataset-teleop-table-keyboard-five",
"question": "Record 5 keyboard teleop demos for the scissor pick-and-place task.",
"expected_skill": "i4h-workflow-dataset-teleop",
"ground_truth": "This README skill-table prompt routes to keyboard teleop recording for scissor_pick_and_place with five demos.",
"expected_behavior": [
"routes to i4h-workflow-dataset-teleop",
"maps scissor pick-and-place to scissor_pick_and_place",
"selects keyboard teleoperation",
"records five demos to HDF5"
]
},
{
"id": "i4h-workflow-dataset-teleop-specialty-endoluminal-patient-twin",
"question": "Start endoluminal_navigation teleop with the prepared s0011 patient twin.",
"expected_skill": "i4h-workflow-dataset-teleop",
"ground_truth": "This endoluminal specialty prompt launches the declared keyboard teleop mode with the prepared s0011 patient-twin manifest and leaves control to the human operator.",
"expected_behavior": [
"routes to i4h-workflow-dataset-teleop",
"requires the generated s0011 patient_twin.yaml to exist",
"uses endoluminal_navigation teleop mode with its default keyboard device",
"keeps the simulator visible and in the foreground",
"requires a human operator and never pretends to provide keyboard input"
]
}
]
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.
Needs: Needs Python
Security
What our scanner noticed· Medium risk
- Large base64-encoded blobs
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Created by
- Isaac for Healthcare Team
- Publisher
- NVIDIA (Official)
- Distributed by
- NVIDIA
- Source
- NVIDIA/skills · skills/i4h-workflow-dataset-teleop · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Isaac for Healthcare Team or NVIDIA.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download i4h-workflow-dataset-teleop.zip