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Add MustardInLeftBinTask: 100 GR00T demonstrations (front+wrist 256x256, 15fps)

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  1. MustardInLeftBinTask/README.md +89 -0
  2. MustardInLeftBinTask/data/chunk-000/episode_000000.parquet +3 -0
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MustardInLeftBinTask/README.md ADDED
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+ # RoboLab Motion-Planning GR00T Dataset
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+
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+ This dataset was generated from RoboLab/Isaac Sim scripted Cartesian motion planning.
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+ It is stored in the GR00T-flavored LeRobot v2 layout expected by NVIDIA Isaac-GR00T.
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+
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+ ## Contents
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+
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+ - Episodes: 100
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+ - Frames: 19500
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+ - FPS: 15
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+ - Robot type metadata: `droid_abs_ik`
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+ - State/action dimension: 8
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+
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+ ## Tasks
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+
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+ - `0`: Put the mustard in the left bin
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+
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+ ## Directory Layout
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+
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+ ```text
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+ meta/info.json
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+ meta/modality.json
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+ meta/tasks.jsonl
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+ meta/episodes.jsonl
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+ meta/stats.json
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+ meta/relative_stats.json
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+ data/chunk-000/episode_*.parquet
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+ videos/chunk-000/<video_key>/episode_*.mp4
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+ robolab_motionplanning_config.py
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+ README.md
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+ ```
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+
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+ ## Data Format
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+
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+ Each parquet row contains:
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+
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+ - `observation.state`: float32 list `[x, y, z, qw, qx, qy, qz, gripper]`
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+ - `action`: float32 list with the next end-effector target in the same format
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+ - `timestamp`: seconds at 15 FPS
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+ - `annotation.human.action.task_description`: integer index into `meta/tasks.jsonl`
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+ - `task_index`, `episode_index`, `index`
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+ - `next.reward`, `next.done`
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+
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+ `meta/modality.json` splits state and action into:
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+
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+ - `eef_position`: indices `[0:3]`
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+ - `eef_quaternion_wxyz`: indices `[3:7]`
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+ - `gripper`: indices `[7:8]`
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+
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+ Video modalities:
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+
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+ - `observation.images.front`
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+ - `observation.images.wrist`
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+
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+ ## Fine-Tuning GR00T
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+
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+ Clone and install Isaac-GR00T following the official repository instructions:
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+
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+ ```bash
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+ git clone https://github.com/NVIDIA/Isaac-GR00T.git
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+ cd Isaac-GR00T
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+ ```
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+
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+ Then fine-tune with this dataset path and the included modality config:
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+
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+ ```bash
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+ export NUM_GPUS=1
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+ 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
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+ ```
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+
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+ For open-loop evaluation after training:
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+
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+ ```bash
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+ 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
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+ ```
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+
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+ Notes:
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+
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+ - This dataset stores Cartesian end-effector pose with quaternion orientation.
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+ - The included GR00T config treats the three action slices as `NON_EEF` absolute vectors.
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+ - 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.
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+ - If you change the action horizon or modality config, regenerate GR00T statistics as described in the Isaac-GR00T data config guide.
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+
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+ References:
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+
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+ - https://github.com/NVIDIA/Isaac-GR00T
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+ - https://github.com/NVIDIA/Isaac-GR00T/blob/main/getting_started/data_preparation.md
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+ - https://github.com/NVIDIA/Isaac-GR00T/blob/main/getting_started/finetune_new_embodiment.md
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+ - https://github.com/NVIDIA/Isaac-GR00T/blob/main/getting_started/data_config.md
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