File size: 3,222 Bytes
5e360d7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 | # RoboLab Motion-Planning GR00T Dataset
This dataset was generated from RoboLab/Isaac Sim scripted Cartesian motion planning.
It is stored in the GR00T-flavored LeRobot v2 layout expected by NVIDIA Isaac-GR00T.
## Contents
- Episodes: 80
- Frames: 61839
- FPS: 15
- Robot type metadata: `droid_abs_ik`
- State/action dimension: 8
## Tasks
- `0`: Stack the blocks in the order from bottom to top: red, blue, green, yellow
## Directory Layout
```text
meta/info.json
meta/modality.json
meta/tasks.jsonl
meta/episodes.jsonl
meta/stats.json
meta/relative_stats.json
data/chunk-000/episode_*.parquet
videos/chunk-000/<video_key>/episode_*.mp4
robolab_motionplanning_config.py
README.md
```
## Data Format
Each parquet row contains:
- `observation.state`: float32 list `[x, y, z, qw, qx, qy, qz, gripper]`
- `action`: float32 list with the next end-effector target in the same format
- `timestamp`: seconds at 15 FPS
- `annotation.human.action.task_description`: integer index into `meta/tasks.jsonl`
- `task_index`, `episode_index`, `index`
- `next.reward`, `next.done`
`meta/modality.json` splits state and action into:
- `eef_position`: indices `[0:3]`
- `eef_quaternion_wxyz`: indices `[3:7]`
- `gripper`: indices `[7:8]`
Video modalities:
- `observation.images.front`
- `observation.images.wrist`
## Fine-Tuning GR00T
Clone and install Isaac-GR00T following the official repository instructions:
```bash
git clone https://github.com/NVIDIA/Isaac-GR00T.git
cd Isaac-GR00T
```
Then fine-tune with this dataset path and the included modality config:
```bash
export NUM_GPUS=1
CUDA_VISIBLE_DEVICES=0 uv run python gr00t/experiment/launch_finetune.py --base-model-path nvidia/GR00T-N1.7-3B --dataset-path /path/to/this/dataset --embodiment-tag NEW_EMBODIMENT --modality-config-path /path/to/this/dataset/robolab_motionplanning_config.py --num-gpus $NUM_GPUS --output-dir /tmp/robolab_motionplanning_gr00t --save-total-limit 5 --save-steps 1000 --max-steps 2000 --global-batch-size 32 --dataloader-num-workers 4
```
For open-loop evaluation after training:
```bash
uv run python gr00t/eval/open_loop_eval.py --dataset-path /path/to/this/dataset --embodiment-tag NEW_EMBODIMENT --model-path /tmp/robolab_motionplanning_gr00t/checkpoint-2000 --traj-ids 0 --action-horizon 16 --steps 260 --modality-keys eef_position eef_quaternion_wxyz gripper
```
Notes:
- This dataset stores Cartesian end-effector pose with quaternion orientation.
- The included GR00T config treats the three action slices as `NON_EEF` absolute vectors.
- For a production EEF-specific setup, convert quaternion orientation to a GR00T-supported EEF rotation format such as 6D rotation, then use the corresponding GR00T `ActionType.EEF` / action format.
- If you change the action horizon or modality config, regenerate GR00T statistics as described in the Isaac-GR00T data config guide.
References:
- https://github.com/NVIDIA/Isaac-GR00T
- https://github.com/NVIDIA/Isaac-GR00T/blob/main/getting_started/data_preparation.md
- https://github.com/NVIDIA/Isaac-GR00T/blob/main/getting_started/finetune_new_embodiment.md
- https://github.com/NVIDIA/Isaac-GR00T/blob/main/getting_started/data_config.md
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