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Add BlockStackingSpecifiedOrderTask: 80 GR00T demos (long-horizon, front+wrist 256x256)
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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 

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:

git clone https://github.com/NVIDIA/Isaac-GR00T.git
cd Isaac-GR00T

Then fine-tune with this dataset path and the included modality config:

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:

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: