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LeRobot Visualizer

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Easily visualize and inspect LeRobot datasets in your browser.

by nvidia2.8k stars on nvidia/skills
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Updated Aug 7, 2026
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Free · Opens the source repo

What LeRobot Visualizer does

LeRobot Visualizer serves as a dedicated HTML visualizer for converted LeRobot datasets, allowing users to inspect and visualize data efficiently. This skill is particularly useful for developers and data scientists who work with LeRobot datasets and need to verify the integrity and quality of their data after conversion. By providing a straightforward way to launch a visual inspection process, it streamlines the workflow of data validation and analysis.

To use the LeRobot Visualizer, you must have a converted dataset directory that contains a meta/info.json file. This skill is not intended for dataset conversion but rather for visual checks post-conversion or during video augmentation. Users can easily set up the environment by running a series of bash commands that prepare the dataset and launch the visualizer in a local browser window. The visualizer will generate a local URL, allowing for quick access to the visual representation of the dataset.

The setup process involves ensuring the necessary workflows are in place and that the dataset directory is specified correctly. Users should note that the dataset path must be absolute, as relative paths will not be recognized. This requirement ensures that the visualizer can accurately locate and serve the dataset files. If any issues arise during setup, the skill provides clear troubleshooting guidance to help users resolve common errors related to environment setup or dataset accessibility.

Overall, the LeRobot Visualizer is an essential tool for anyone working with LeRobot datasets, making it easier to conduct visual inspections and ensure data quality before further analysis or processing. Its integration into the i4h workflows enhances productivity by simplifying the visualization process, allowing users to focus on their data analysis tasks without unnecessary complications.

When to use it

Use this skill when you need to visualize or inspect a LeRobot dataset that has already been converted.

When not to use it

Do not use this skill for converting datasets; it specifically requires a dataset already prepared with `meta/info.json`.

What you can build with it

Visualizing Converted Datasets

After converting a dataset with the appropriate workflow, use the LeRobot Visualizer to check the data integrity in a browser.

Inspecting Data Quality

Quickly inspect the quality and structure of your LeRobot datasets post-conversion to ensure they meet your analysis needs.

Debugging Dataset Issues

When encountering issues with your dataset, use the visualizer to identify potential problems visually before further processing.

How to install LeRobot Visualizer

View source

1. Install with the skills CLI

npx skills add nvidia/skills/i4h-lerobot-viz --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by nvidia

i4h Workflow — LeRobot Viz

Purpose

Serve the LeRobot HTML visualizer for a converted dataset in a browser. Use when the user asks to visualize, inspect, or open a LeRobot dataset.

Base Code

These steps drive the i4h-workflows base code (the workflows/agentic/ tree). To reuse an existing checkout, set I4H_WORKFLOWS to its path (no clone happens). Otherwise this resolves the current repo, or clones to ~/i4h-workflows — pick that default without prompting. Run every command below from the resolved root:

# Resolve the i4h-workflows base code (provides workflows/agentic/).
ROOT="${I4H_WORKFLOWS:-$(git rev-parse --show-toplevel 2>/dev/null)}"
if [ ! -d "$ROOT/workflows/agentic" ]; then
  ROOT="${I4H_WORKFLOWS:-$HOME/i4h-workflows}"
  [ -d "$ROOT/workflows/agentic" ] || git clone https://github.com/isaac-for-healthcare/i4h-workflows "$ROOT"
fi
export I4H_WORKFLOWS="$ROOT"; cd "$ROOT"

Basics

  • Input is a converted LeRobot dataset directory containing meta/info.json.
  • Use for visual checks after conversion or video augmentation.

Run

Run the steps below in order. Each step is a separate bash call; variables persist in the local agent's tmux session.

Step 1 — setup and resolve dataset

REPO_ROOT="${I4H_WORKFLOWS:-$(git rev-parse --show-toplevel 2>/dev/null)}"; [ -d "$REPO_ROOT/workflows/agentic" ] || REPO_ROOT="$HOME/i4h-workflows"
RUNS_ROOT="${REPO_ROOT}/workflows/agentic/runs"

# Point DATASET_DIR at a converted LeRobot dataset dir (absolute; must contain meta/info.json),
# produced by [[i4h-workflow-dataset-convert]]. List candidates:
#   find "${RUNS_ROOT}" "${HF_LEROBOT_HOME:-$HOME/.cache/huggingface/lerobot}" -name info.json -path '*/meta/*' -printf '%h\n' | sed 's#/meta$##' | sort -u
DATASET_DIR="${DATASET_DIR:-}"
if [ ! -f "${DATASET_DIR%/}/meta/info.json" ]; then
  echo "viz: set DATASET_DIR to a LeRobot dataset dir with meta/info.json (got '${DATASET_DIR:-<unset>}'). Candidates:" >&2
  find "${RUNS_ROOT}" "${HF_LEROBOT_HOME:-$HOME/.cache/huggingface/lerobot}" -name info.json -path '*/meta/*' -printf '%h\n' 2>/dev/null | sed 's#/meta$##' | sort -u | head
  exit 1
fi

RUN_DIR="${RUNS_ROOT}/viz_$(date +%Y%m%d_%H%M%S)"
mkdir -p "${RUN_DIR}/logs" "${RUN_DIR}/viz_state"
ln -sfn "${RUN_DIR}" "${RUNS_ROOT}/.latest"

Step 2 — serve visualizer

"${REPO_ROOT}/workflows/agentic/dataset/viz.sh" "${DATASET_DIR}" \
  --state-dir "${RUN_DIR}/viz_state" \
  2>&1 | tee "${RUN_DIR}/logs/viz.log"

Notes

  • The dataset path must be absolute. viz.sh treats relative paths as Hugging Face repo ids and looks them up under ~/.cache/huggingface/lerobot/<path>.
  • Override --state-dir only when the caller provides one.

Verify

  • The visualizer prints a local URL (e.g. http://127.0.0.1:9090/).
  • Videos and joint timelines load in the browser.

Prerequisites

  • Workflow set up via [[i4h-workflow-setup]] (the .venv must exist).
  • A converted LeRobot dataset directory containing meta/info.json (see [[i4h-workflow-dataset-convert]]).
  • An absolute path to that dataset directory.

Limitations

  • Input must be a converted LeRobot dataset directory with meta/info.json; intended for visual checks after conversion or video augmentation.
  • The dataset path must be absolute; viz.sh treats relative paths as Hugging Face repo ids and looks them up under ~/.cache/huggingface/lerobot/<path>.
  • Override --state-dir only when the caller provides one.

Troubleshooting

  • Error: .venv not found / module import fails - Cause: workflow not set up. Fix: run [[i4h-workflow-setup]] first.
  • Error: dataset resolved as a Hugging Face repo id / not found - Cause: a relative dataset path was passed. Fix: pass the absolute path to the dataset directory.
  • Error: no meta/info.json - Cause: directory is not a converted LeRobot dataset. Fix: convert first with [[i4h-workflow-dataset-convert]].
  • Error: address/port already in use - Cause: a visualizer is already serving that local URL. Fix: stop the existing process before starting a new one.

Final Response

Report dataset path, visualizer URL, stop command, startup failures.

Frequently asked questions about LeRobot Visualizer

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