[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-nvidia-i4h-workflow-dataset-annotate":3,"mdc-xkqm4k-key":37,"related-org-nvidia-i4h-workflow-dataset-annotate":3985,"related-repo-nvidia-i4h-workflow-dataset-annotate":4146},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":26,"repoUrl":27,"updatedAt":28,"license":29,"forks":30,"topics":31,"repo":32,"sourceUrl":35,"mdContent":36},"i4h-workflow-dataset-annotate","annotate and filter robot environment episodes","Use a VLM to verify whether each episode satisfies the env's task description. Use when the user asks to annotate, label episodes, filter demos, or gate finetuning on a success classifier.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},"nvidia","NVIDIA","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fnvidia.png",[12,16,19,20,23],{"name":13,"slug":14,"type":15},"Robotics","robotics","tag",{"name":17,"slug":18,"type":15},"Data Analysis","data-analysis",{"name":9,"slug":8,"type":15},{"name":21,"slug":22,"type":15},"Simulation","simulation",{"name":24,"slug":25,"type":15},"Data Cleaning","data-cleaning",2473,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fskills","2026-07-23T06:06:01.384401","Apache-2.0",281,[],{"repoUrl":27,"stars":26,"forks":30,"topics":33,"description":34},[],"AI agent skills published by NVIDIA","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fskills\u002Ftree\u002FHEAD\u002Fskills\u002Fi4h-workflow-dataset-annotate","---\nname: i4h-workflow-dataset-annotate\nversion: \"0.6.0\"\ndescription: Use a VLM to verify whether each episode satisfies the env's task description. Use when the user asks to annotate, label episodes, filter demos, or gate finetuning on a success classifier.\nlicense: Apache-2.0\nmetadata:\n  author: \"Isaac for Healthcare Team \u003Cisaac-for-healthcare-support@nvidia.com>\"\n  tags:\n    - isaac-for-healthcare\n    - i4h\n    - dataset\n    - annotation\n    - vlm\n---\n\n# i4h Workflow — Annotate Dataset\n\n## Purpose\n\nUse a VLM to verify whether each episode satisfies the env's task description. Use when the user asks to annotate, label episodes, filter demos, or gate finetuning on a success classifier.\n\n## Base Code\n\nThese steps drive the i4h-workflows base code (the `workflows\u002Fagentic\u002F` tree). To reuse an existing checkout, set `I4H_WORKFLOWS` to its path (no clone happens). Otherwise this resolves the current repo, or clones to `~\u002Fi4h-workflows` — pick that default without prompting. Run every command below from the resolved root:\n\n```bash\n# Resolve the i4h-workflows base code (provides workflows\u002Fagentic\u002F).\nROOT=\"${I4H_WORKFLOWS:-$(git rev-parse --show-toplevel 2>\u002Fdev\u002Fnull)}\"\nif [ ! -d \"$ROOT\u002Fworkflows\u002Fagentic\" ]; then\n  ROOT=\"${I4H_WORKFLOWS:-$HOME\u002Fi4h-workflows}\"\n  [ -d \"$ROOT\u002Fworkflows\u002Fagentic\" ] || git clone https:\u002F\u002Fgithub.com\u002Fisaac-for-healthcare\u002Fi4h-workflows \"$ROOT\"\nfi\nexport I4H_WORKFLOWS=\"$ROOT\"; cd \"$ROOT\"\n```\n\n## Basics\n\n- Annotation is optional. Do not run it during validation unless the user requests labels.\n- For natural-language prompts such as \"Run Annotation on all recorded episodes\", annotate all episodes in one selected HDF5 recording, not every historical HDF5 under `workflows\u002Fagentic\u002Fruns\u002F`. If the user does not name an HDF5, choose the latest annotatable recording: first look inside `runs\u002F.latest` when it contains an HDF5, otherwise pick the newest non-annotation `.hdf5` under `workflows\u002Fagentic\u002Fruns\u002F`. Only batch across multiple HDF5 files when the user explicitly asks for all historical recordings, every HDF5 file, or a batch annotation run.\n- **Env config (source of truth):** the annotator reads the success criterion (`policy.task_description`) from `workflows\u002Fagentic\u002Fconfig\u002Fenvironments\u002F\u003Cenv>.yaml`. Pass `--task-description` to override.\n- Talks to an OpenAI-compatible endpoint via `--base-url` (default `http:\u002F\u002Flocalhost:8000\u002Fv1`) and `--model` (default `Qwen\u002FQwen3-VL-8B-Instruct`). Point both at a running vision-model server. Do not use text-only\u002Fcode models such as `qwen3-coder-next`; offline annotation sends image inputs and requires a VLM.\n- Keep every annotation artifact inside `workflows\u002Fagentic\u002Fruns\u002F\u003Crun>\u002F`. Do not create or access `\u002Ftmp\u002Fannotate_*` or other external temp directories.\n\n## Start VLM\n\n> **Skip this section if an OpenAI-compatible endpoint serving a vision model is already running** — just set `VLM_BASE_URL`\u002F`VLM_MODEL` in Run to point at it. A local-agent server running `qwen3-coder-next` does not qualify because it is text-only. `annotator\u002Fvllm.sh` defaults to port `8000`, so starting it on top of an existing server collides; don't.\n\nRun the steps below in order. Each step is a separate bash call; variables persist in the local agent's tmux session.\n\nFor readiness, use `workflows\u002Fagentic\u002Fannotator\u002Fvllm.sh ensure`. Do not replace it with raw `docker ps`, fixed sleeps, ad hoc model-listing HTTP probes, or separate manual wait steps; the helper owns the start-and-wait policy.\n\n### Step 1 — start VLM (if needed)\n\nRun this exact command. Do not add `sleep`, `status`, `curl`, or shell control operators around it:\n\n```bash\nREPO_ROOT=\"${I4H_WORKFLOWS:-$(git rev-parse --show-toplevel 2>\u002Fdev\u002Fnull)}\"; [ -d \"$REPO_ROOT\u002Fworkflows\u002Fagentic\" ] || REPO_ROOT=\"$HOME\u002Fi4h-workflows\"\n\"${REPO_ROOT}\u002Fworkflows\u002Fagentic\u002Fannotator\u002Fvllm.sh\" ensure\n```\n\n## Run (Offline HDF5)\n\nRun the steps below in order. Each step is a separate bash call; variables persist in the local agent's tmux session.\n\n### Step 1 — setup and resolve HDF5\n\n```bash\nREPO_ROOT=\"${I4H_WORKFLOWS:-$(git rev-parse --show-toplevel 2>\u002Fdev\u002Fnull)}\"; [ -d \"$REPO_ROOT\u002Fworkflows\u002Fagentic\" ] || REPO_ROOT=\"$HOME\u002Fi4h-workflows\"\nENV_ID=scissor_pick_and_place\nRUNS_ROOT=\"${REPO_ROOT}\u002Fworkflows\u002Fagentic\u002Fruns\"\n\n# LLM endpoint + model (OpenAI-compatible vLLM). Defaults match annotator\u002Fvllm.sh; override to use an\n# external vision server — e.g. VLM_BASE_URL=http:\u002F\u002Flocalhost:8000\u002Fv1 VLM_MODEL=qwen3-vl-32b\nVLM_BASE_URL=\"${VLM_BASE_URL:-http:\u002F\u002Flocalhost:8000\u002Fv1}\"\nVLM_MODEL=\"${VLM_MODEL:-Qwen\u002FQwen3-VL-8B-Instruct}\"\n\n# Point HDF5_PATH at a real recording (absolute path). Recordings come from teleop, mimic, or\n# validate (which writes data\u002Fverify.hdf5 under each runs\u002Feval_* dir). If HDF5_PATH is not set,\n# choose one recording: an HDF5 inside runs\u002F.latest if present, otherwise the newest non-annotation\n# HDF5 under runs\u002F. \"All recorded episodes\" means all episodes inside this one HDF5.\nHDF5_PATH=\"${HDF5_PATH:-}\"\nif [ ! -f \"${HDF5_PATH}\" ]; then\n  LATEST_RUN=\"$(readlink -f \"${RUNS_ROOT}\u002F.latest\" 2>\u002Fdev\u002Fnull || true)\"\n  if [ -n \"${LATEST_RUN}\" ] && [ -d \"${LATEST_RUN}\" ]; then\n    HDF5_PATH=\"$(\n      find \"${LATEST_RUN}\" -name '*.hdf5' -type f -printf '%T@ %p\\n' 2>\u002Fdev\u002Fnull \\\n        | sort -nr | awk 'NR==1 { $1=\"\"; sub(\u002F^ \u002F, \"\"); print; exit }'\n    )\"\n  fi\nfi\nif [ ! -f \"${HDF5_PATH}\" ]; then\n  HDF5_PATH=\"$(\n    find \"${RUNS_ROOT}\" \\( -path '*\u002Fannotate_*' -o -path '*\u002F.latest' \\) -prune -o \\\n      -name '*.hdf5' -type f -printf '%T@ %p\\n' 2>\u002Fdev\u002Fnull \\\n      | sort -nr | awk 'NR==1 { $1=\"\"; sub(\u002F^ \u002F, \"\"); print; exit }'\n  )\"\nfi\nif [ ! -f \"${HDF5_PATH}\" ]; then\n  echo \"annotate: set HDF5_PATH to an existing .hdf5 (got '${HDF5_PATH:-\u003Cunset>}'). Candidates:\" >&2\n  find \"${RUNS_ROOT}\" \\( -path '*\u002Fannotate_*' -o -path '*\u002F.latest' \\) -prune -o \\\n    -name '*.hdf5' -type f -printf '%TY-%Tm-%Td %TH:%TM  %p\\n' 2>\u002Fdev\u002Fnull | sort -r | head\n  exit 1\nfi\n\nRUN_DIR=\"${RUNS_ROOT}\u002Fannotate_${ENV_ID}_$(date +%Y%m%d_%H%M%S)\"\nmkdir -p \"${RUN_DIR}\u002Fdata\" \"${RUN_DIR}\u002Flogs\" \"${RUN_DIR}\u002Ftmp\"\nln -sfn \"${RUN_DIR}\" \"${RUNS_ROOT}\u002F.latest\"\n```\n\n### Step 2 — annotate offline\n\n```bash\nTMPDIR=\"${RUN_DIR}\u002Ftmp\" \"${REPO_ROOT}\u002Fworkflows\u002Fagentic\u002Fannotator\u002Frun.sh\" \\\n  --env \"${ENV_ID}\" \\\n  --base-url \"${VLM_BASE_URL}\" \\\n  --model \"${VLM_MODEL}\" \\\n  --output \"${RUN_DIR}\u002Fannotations.jsonl\" \\\n  offline \\\n  --hdf5-path \"${HDF5_PATH}\" \\\n  --filter \"${RUN_DIR}\u002Fdata\u002Ffiltered.hdf5\" \\\n  > \"${RUN_DIR}\u002Flogs\u002Fannotator.log\" 2>&1\n```\n\n### Step 3 — summarize annotations\n\n```bash\nSUCCESS_COUNT=$(grep -c '\"success\": true' \"${RUN_DIR}\u002Fannotations.jsonl\" 2>\u002Fdev\u002Fnull || true)\nFAILURE_COUNT=$(grep -c '\"success\": false' \"${RUN_DIR}\u002Fannotations.jsonl\" 2>\u002Fdev\u002Fnull || true)\nprintf 'annotations: success=%s failure=%s\\n' \"${SUCCESS_COUNT}\" \"${FAILURE_COUNT}\"\ngrep -E \"Traceback|Error|FAILED\" \"${RUN_DIR}\u002Flogs\u002Fannotator.log\" || true\n```\n\n### Step 4 — stop VLM (only if you started it in Start VLM)\n\nSkip when using an external server (e.g. the local-agent one) — it would kill that server.\n\n```bash\n\"${REPO_ROOT}\u002Fworkflows\u002Fagentic\u002Fannotator\u002Fvllm.sh\" stop\n```\n\n## Live Mode\n\nAnnotate the latest camera frames from a **running** policy\u002FArena session over Zenoh (cameras default to the env config). Use only when such a session is already up and the user asks for live judging.\n\nRun the steps below in order. Each step is a separate bash call; variables persist in the local agent's tmux session.\n\n### Step 1 — setup\n\n```bash\nREPO_ROOT=\"${I4H_WORKFLOWS:-$(git rev-parse --show-toplevel 2>\u002Fdev\u002Fnull)}\"; [ -d \"$REPO_ROOT\u002Fworkflows\u002Fagentic\" ] || REPO_ROOT=\"$HOME\u002Fi4h-workflows\"\nENV_ID=scissor_pick_and_place\nRUNS_ROOT=\"${REPO_ROOT}\u002Fworkflows\u002Fagentic\u002Fruns\"\nVLM_BASE_URL=\"${VLM_BASE_URL:-http:\u002F\u002Flocalhost:8000\u002Fv1}\"\nVLM_MODEL=\"${VLM_MODEL:-Qwen\u002FQwen3-VL-8B-Instruct}\"\nRUN_DIR=\"${RUNS_ROOT}\u002Fannotate_live_${ENV_ID}_$(date +%Y%m%d_%H%M%S)\"\nmkdir -p \"${RUN_DIR}\u002Ftmp\"\n```\n\n### Step 2 — annotate live\n\n```bash\nTMPDIR=\"${RUN_DIR}\u002Ftmp\" \"${REPO_ROOT}\u002Fworkflows\u002Fagentic\u002Fannotator\u002Frun.sh\" \\\n  --env \"${ENV_ID}\" \\\n  --base-url \"${VLM_BASE_URL}\" \\\n  --model \"${VLM_MODEL}\" \\\n  --output \"${RUN_DIR}\u002Flive.jsonl\" \\\n  live \\\n  --count 5 \\\n  --interval 2.0 \\\n  --timeout 30.0\n```\n\n- `--count 0` runs forever; `--interval` is seconds between snapshots; `--timeout` is how long to wait for first frames from every camera.\n- `--min-success-frames N` (needs a finite `--count`) exits non-zero unless at least N sampled snapshots pass — use it as a gate.\n- `--dump-frames-dir DIR` saves sampled frames; add `--dump-frames-only` to dump without calling the VLM.\n- `--cameras a,b` overrides the env's Zenoh camera names.\n\n## Verify\n\n- `annotations.jsonl` exists.\n- Filtered HDF5 exists when `--filter` was passed.\n- Tally success\u002Ffailure counts from the JSONL before reporting.\n\n## Prerequisites\n\n- Workflow set up via [[i4h-workflow-setup]] (the `.venv` must exist).\n- An existing HDF5 recording to annotate (set `HDF5_PATH` to an absolute path; the Run block lists candidates if it's unset or wrong).\n- A reachable OpenAI-compatible endpoint serving a vision model — either start the annotator's own (`annotator\u002Fvllm.sh start`) or point `VLM_BASE_URL`\u002F`VLM_MODEL` at an existing vision server. The current `qwen3-coder-next` local-agent endpoint is not sufficient because it is text-only.\n- Annotation is optional — only run it when the user requests labels.\n\n## Limitations\n\n- Annotation is optional and is not run during validation unless requested.\n- Requires a reachable OpenAI-compatible vLLM server; defaults to `localhost:8000\u002Fv1`.\n- Live mode applies only when a policy\u002FArena session is already running and the user requests live judging.\n- The annotator reads task text from the env YAML; override per-run with `--task-description`.\n\n## Troubleshooting\n\n- **Error:** `.venv` not found \u002F module import fails - Cause: workflow not set up. Fix: run [[i4h-workflow-setup]] first.\n- **Error:** connection refused at `localhost:8000\u002Fv1` - Cause: no vLLM at `VLM_BASE_URL`. Fix: start one (`annotator\u002Fvllm.sh start`) or set `VLM_BASE_URL`\u002F`VLM_MODEL` to a running server.\n- **Error:** model not found \u002F 404 from the endpoint - Cause: `VLM_MODEL` is not the id the server actually serves. Fix: set `VLM_MODEL` to the served name (e.g. `qwen3-vl-32b`; check `curl ${VLM_BASE_URL}\u002Fmodels`).\n- **Error:** image input unsupported \u002F bad request from a text model - Cause: the endpoint is serving a text-only\u002Fcode model such as `qwen3-coder-next`. Fix: use a vision model endpoint such as Qwen3-VL for annotation.\n- **Error:** input HDF5 not found - Cause: `HDF5_PATH` unset or not a real file. Fix: pick an absolute path from the candidates the Run block prints.\n- **Error:** filtered HDF5 missing - Cause: `--filter` was not passed. 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