Add GR00T notebook and fix LIBERO checkpoint comparisons

CHANGELOG.md

@@ -8,6 +8,10 @@ Format: each entry includes the date, a short title, and a description of what c
 
 ## Unreleased
 
+### Learn with the GR00T, MuJoCo and MCAP notebook
+
+The supplied GR00T N1.7 lab is available alongside the ABC/XDOF notebook in a shared notebook index, with dedicated kernel setup and local artifact guidance. LIBERO task selection follows the chosen suite, and before/after comparisons require distinct checkpoints saved during training. ABC video conversion also supports current FFmpeg; GPU execution remains unvalidated. *(PR #36)*
+
 ### Compare ABC-VLA and adapted pi0.5 on bimanual episodes
 
 Run compatible policies through repeated ABC simulator episodes using existing Trials, Campaigns, evidence, traces and reports. A guided comparison and matching CLI preserve scene resets, checkpoint identities, task outcomes and policy-call measurements; a separate ABC/XDOF conversion and pi0.5 training recipe prepares adapted checkpoints. Real data conversion and CPU simulation are validated; GPU training and learned-policy comparison remain pending. *(PR #35)*

examples/abc-pi05/dataset.py

@@ -248,8 +248,8 @@ def frames(ep: Episode):
                 "error",
                 "-i",
                 str(ep.path / "combined_camera-images-rgb.mp4"),
-                "-vsync",
-                "0",
+                "-fps_mode",
+                "passthrough",
                 "-f",
                 "rawvideo",
                 "-pix_fmt",

notebooks/README.md

@@ -0,0 +1,13 @@
+# ROVE notebooks
+
+Clone ROVE to get these Jupyter learning labs. Each notebook has its own setup;
+use its dedicated Python environment rather than the ROVE application environment.
+
+| Notebook | What you can explore | Setup |
+| --- | --- | --- |
+| [ABC and XDOF](abc_xdof/hello_abc.ipynb) | Demonstrations, action predictions, simulator episodes and optional ABC-VLA fine-tuning. | [ABC instructions](abc_xdof/README.md) |
+| [GR00T N1.7, MuJoCo and MCAP](gr00t/gr00t_n17_finetuning_mujoco_mcap_lab_v2.ipynb) | SO100 data inspection, MuJoCo, optional GR00T fine-tuning and LIBERO evaluation, and synthetic MCAP cleaning. | [GR00T instructions](gr00t/README.md) |
+
+The notebooks are standalone exercises. GPU training and inference require the
+runtime described in each guide. Notebook outputs do not automatically become
+ROVE Trials or Campaigns.

notebooks/gr00t/.gitignore

@@ -0,0 +1,4 @@
+.venv/
+.work/
+gr00t_lab/
+*.executed.ipynb

notebooks/gr00t/README.md

@@ -0,0 +1,72 @@
+# GR00T N1.7, MuJoCo and MCAP lab
+
+Open [the notebook](gr00t_n17_finetuning_mujoco_mcap_lab_v2.ipynb) for a learning
+exercise covering SO100 demonstration inspection, MuJoCo fundamentals, optional
+GR00T fine-tuning and LIBERO evaluation, and synthetic ROS 2 MCAP cleaning.
+Browse the [notebook index](../README.md) for the ABC and XDOF lab.
+
+This is a standalone notebook, not a GR00T integration with ROVE's evaluation
+engine. Its source is included without saved outputs. Notebook structure and
+code-cell syntax are checked, and regression tests exercise suite selection,
+checkpoint scheduling and comparison guards. Its installations, training and
+GPU evaluation cells have not been executed as part of this addition.
+
+## Open with a dedicated kernel
+
+Install Python 3.12, Git, uv and FFmpeg. Git LFS may be needed for upstream demo
+assets. From the ROVE repository root:
+
+```bash
+uv venv --python 3.12 --seed notebooks/gr00t/.venv
+uv pip install --python notebooks/gr00t/.venv/bin/python jupyterlab ipykernel
+notebooks/gr00t/.venv/bin/python -m ipykernel install --sys-prefix \
+  --name rove-gr00t --display-name "ROVE GR00T (Python 3.12)"
+GR00T_LAB_ROOT="$PWD/notebooks/gr00t/.work" \
+  notebooks/gr00t/.venv/bin/jupyter lab \
+  notebooks/gr00t/gr00t_n17_finetuning_mujoco_mcap_lab_v2.ipynb --ip=127.0.0.1
+```
+
+Choose **ROVE GR00T (Python 3.12)** in Jupyter. In VS Code, use **Select Kernel →
+Python Environments** and select `notebooks/gr00t/.venv/bin/python`. Selecting
+Homebrew's system Python instead can produce the missing `ipykernel` message.
+The seeded environment includes pip because the notebook installs its data and
+simulation dependencies into the selected kernel.
+
+## Choose the execution scope
+
+The notebook separates macOS/CPU data, MuJoCo and MCAP exercises from the full
+Linux/NVIDIA model path. Work through the prerequisite checks first. The full
+path needs a compatible CUDA environment, appropriate model access and graphics
+libraries for rendered simulation. Docker also needs NVIDIA GPU access.
+
+Fine-tuning, larger dataset downloads and LIBERO evaluation have explicit flags
+in the first configuration cell. Keep them disabled until their prerequisites are
+ready. Some earlier setup cells still install packages, clone the upstream GR00T
+repository and retrieve assets. Dependencies and the upstream checkout are not
+pinned by this notebook; inspect the commands before execution and record the
+versions used for any comparison.
+
+The MCAP exercise generates synthetic data and exports a staging episode; it does
+not by itself create a validated production training dataset. Short training runs
+demonstrate mechanics and do not establish model quality or real-robot success.
+
+## Compare checkpoints
+
+Choose `LIBERO_SUITE` once. The notebook selects a matching example task from the
+[upstream task list](https://github.com/NVIDIA/Isaac-GR00T/blob/main/examples/LIBERO/README.md)
+and uses it in both rollout command templates. The default `goal` suite evaluates
+putting the bowl on the plate; suite `10` uses the stove/moka task.
+
+The 100-step learning exercise now saves checkpoints at steps 50 and 100. The
+comparison requires two existing, distinct checkpoint directories, including
+when paths are symlink aliases. Missing or identical checkpoints block comparison
+command generation and the before/after plot. With valid checkpoints, enable
+`RUN_MUJOCO_BEFORE_AFTER` to print each server/client command pair for manual
+execution with the same task, seed and episode count, and separate video folders.
+
+## Keep generated files local
+
+The launch command puts downloads, model checkpoints and generated data under
+the ignored `.work/` folder. The notebook's default `gr00t_lab/` folder is also
+ignored when it is created beside the notebook. Keep credentials outside notebook
+cells, and clear outputs before committing or sharing an executed copy.