--- license: apache-2.0 language: - en pretty_name: GR00T G1 Grab-Bottle (right hand) - zero-wandering radius-20 task_categories: - robotics tags: - LeRobot - robotics - imitation-learning - vla - vision-language-action - humanoid - unitree-g1 - inspire-ftp - grab-bottle - sonic - teleoperation - curated - arxiv:2503.14734 - arxiv:2511.07820 size_categories: - 10K SMPL retargeting (CloudWalk Robotics Lab) | | Visibility | **Public** | | License | Apache-2.0 | ## Collection setup Same data collection as the [105-ep source set](https://huggingface.co/datasets/cloudwalk-research/gr00t-g1-grab-bottle-right-hand-105ep-v1) - see that card for the full setup. In brief: - **Robot:** Unitree G1 (29-DoF body) + Inspire FTP hands (7-DoF/hand: 6 finger joints + 1 grip pad, via InspireFTPGripMapper) - **Whole-body controller:** [SONIC](https://arxiv.org/abs/2511.07820) WBC - **Teleoperation:** PICO 4 Ultra (headset + 2 controllers, 3-point VR tracking) + 2 ankle trackers -> SMPL retargeting - **Camera:** Intel RealSense D435I on the G1 head; **Hand control:** grip buttons close the Inspire FTP right hand during the grasp - **Task prompt:** the teleop demonstrations were collected under the task prompt "grab the bottle"; the same prompt is used to invoke the policy at inference (see the model cards' "Prompt conditioning" caveat). This card's dataset is the **radius-20 curation** of that source: the kinematic zero-wandering pipeline splits each source episode into clean sub-segments by removing non-purposeful wandering (see [Curation method](#curation-method)). ## Action and state layout This is a GR00T + SONIC dataset, **not** a flat-joint-vector dataset. The policy action is emitted as a **64-D latent `motion_token`** (decoded by the SONIC whole-body controller into whole-body joint commands) plus 7-D left/right hand-joint targets - it only produces motion through the SONIC WBC + ZMQ deploy pipeline, not on a bare G1. | Field | Shape | Meaning | | --- | --- | --- | | `observation.state` | 43 | left_leg 6 + right_leg 6 + waist 3 + left_arm 7 + left_hand 7 + right_arm 7 + right_hand 7 | | `action.wbc` | 43 | whole-body joint targets (same layout as `observation.state`) | | `action.motion_token` | 64 | latent action token decoded by the SONIC WBC | | `observation.eef_state` | 14 | left/right wrist pos (3) + abs quat (4), x2 | | `observation.images.ego_view` | 480×640×3 | head camera (video) | See `meta/info.json` for the full feature schema, including `teleop.*` retargeting fields and `observation.*` base state. ## Curation method Kinematic goal-progress curation (no vision). The `(x, y, z)` of the right wrist is known each frame; the `goal_position` is the wrist's position at the moment the grip is ~95 % closed. **Wandering** happens whenever the distance between `current_position` and `goal_position` grows. The sensitivity is controlled by one parameter, the **smoothing radius** (half-width): like a low-pass filter, a larger half-width ignores briefer wandering. Tested at half-widths of 5, 10, 15, 20 frames; all other parameters are chosen in a principled manner (see below). **Never removed (two gates):** - before the point of **maximum distance** (hand getting into position) - at or after the **grasp** (always kept) **Post-processing:** - pad each removed block by the smoothing half-width (compensates the blur the smoothing introduces) - drop kept blocks shorter than 40 frames (too short to yield any training samples under the action horizon) Provenance for every output episode (source episode, source window, curation mode) is recorded in `meta/episodes.jsonl`. ### The four single-source variants Each variant differs only in the smoothing half-width; everything else is fixed. Metrics are aggregated over all 105 source episodes. | smoothing half-width | clean segments kept | frames removed | training samples removed | |:---:|:---:|:---:|:---:| | 5 | 417 | 31.3% | 51.5% | | 10 | 371 | 29.9% | 47.3% | | 15 | 342 | 28.4% | 44.0% | | 20 | 314 | 28.6% | 42.6% | A larger half-width tolerates briefer wandering, so it removes fewer, longer blocks: fewer (but longer) clean segments survive, and fewer training samples are discarded. ## What this dataset is - and is not - **Is:** a zero-wandering-curated variant of the 105-ep source. Brief non-purposeful arm motion ("wandering") is removed; each surviving clean sub-segment is its own episode (no action chunk spans a cut). - **Is:** the training data for the [v5 GR00T N1.7 fine-tune](https://huggingface.co/cloudwalk-research/gr00t-n17-g1-grab-bottle-rh-314ep-v5-finetune). - **Is not:** the source teleoperation set. For un-segmented demonstrations use the [105-ep source set](https://huggingface.co/datasets/cloudwalk-research/gr00t-g1-grab-bottle-right-hand-105ep-v1). - **Is not:** the merged set. The [radius-20-merged](https://huggingface.co/datasets/cloudwalk-research/gr00t-g1-grab-bottle-right-hand-radius-20-merged) set additionally folds in the worst-positions 115-ep set and re-curates both sources. - **Right-hand only, single camera, single task** - see caveats below. ## How to download ```python from huggingface_hub import snapshot_download snapshot_download( repo_id="cloudwalk-research/gr00t-g1-grab-bottle-right-hand-zero-wandering-smooth-radius-20", repo_type="dataset", local_dir="data/real/gr00t-g1-grab-bottle-right-hand-zero-wandering-smooth-radius-20", ) ``` ## How to load ```python from lerobot.common.datasets.lerobot_dataset import LeRobotDataset ds = LeRobotDataset( "gr00t-g1-grab-bottle-right-hand-zero-wandering-smooth-radius-20", repo_id="cloudwalk-research/gr00t-g1-grab-bottle-right-hand-zero-wandering-smooth-radius-20", ) print(ds.num_episodes, ds.num_frames, ds.fps) ``` ## How to use for fine-tuning & inference This set trained the [v5 GR00T N1.7 fine-tune](https://huggingface.co/cloudwalk-research/gr00t-n17-g1-grab-bottle-rh-314ep-v5-finetune). Fine-tuning was run on NVIDIA B200 GPUs with the [Isaac-GR00T](https://github.com/NVIDIA/Isaac-GR00T) stack; see the model card for the exact hyperparameters. To run the resulting policy in inference (policy server), from an Isaac-GR00T environment: ```bash python -m gr00t.eval.run_gr00t_server \ --model-path \ --embodiment-tag UNITREE_G1_SONIC \ --device cuda:0 --host 0.0.0.0 --port 5550 ``` The server **must** be launched with the same `UNITREE_G1_SONIC` embodiment tag used in training. Closed-loop control of the G1 is driven by the SONIC WBC in [GR00T-WholeBodyControl](https://github.com/NVlabs/GR00T-WholeBodyControl); see the NVlabs [VLA inference tutorial](https://nvlabs.github.io/GR00T-WholeBodyControl/tutorials/vla_inference.html). ## Caveats 1. **Right-hand-only, single task, single camera.** Left-hand and locomotion dims reflect the largely stationary demonstrations; do not expect bimanual or walking behavior. Only the head `ego_view` was recorded - no wrist cameras. 2. **Curated, not source.** Episodes are clean sub-segments, so "episode length" is a clean-segment length (shorter than full demonstrations); wandering has been removed by construction. Use the [source set](https://huggingface.co/datasets/cloudwalk-research/gr00t-g1-grab-bottle-right-hand-105ep-v1) for the un-segmented distribution. 3. **safe_frames = 40** for these single-source variants. The [radius-20-merged](https://huggingface.co/datasets/cloudwalk-research/gr00t-g1-grab-bottle-right-hand-radius-20-merged) set uses safe_frames = 50 on its 105-ep pass, so it keeps 270 segments from the 105-ep set vs this set's 314 - not a discrepancy, a different threshold. 4. **Deployment needs the SONIC stack.** Actions are `motion_token` + hand joints for the `UNITREE_G1_SONIC` embodiment; they only produce motion through the SONIC WBC + ZMQ deploy pipeline, not on a bare G1. ## Lineage This set is a single-source zero-wandering curation of the [105-ep source set](https://huggingface.co/datasets/cloudwalk-research/gr00t-g1-grab-bottle-right-hand-105ep-v1). Its **direct siblings** are the other `radius-(5, 10, 15, 20)` variants (same method, 105-ep alone), the [radius-20-merged](https://huggingface.co/datasets/cloudwalk-research/gr00t-g1-grab-bottle-right-hand-radius-20-merged) set (same method, additionally folding in the [115-ep worst-positions set](https://huggingface.co/datasets/cloudwalk-research/gr00t-g1-grab-bottle-right-hand-worst-positions-empty-115ep-v3) -> v6), and the [right-hand-v2](https://huggingface.co/datasets/cloudwalk-research/gr00t-g1-grab-bottle-right-hand-v2) set (same 105-ep source, the earlier distance-to-goal wandering removal -> **v2 production champion**). The full family - including the DP-resampled both-source sets - is in the table below. | Dataset | Derived from | Episodes | Frames | Notes | |---|---|:---:|:---:|---| | [105ep-v1](https://huggingface.co/datasets/cloudwalk-research/gr00t-g1-grab-bottle-right-hand-105ep-v1) | - (source) | 105 | 70,680 | Base teleoperation set -> **v1 fine-tune** | | [worst-positions-empty-115ep-v3](https://huggingface.co/datasets/cloudwalk-research/gr00t-g1-grab-bottle-right-hand-worst-positions-empty-115ep-v3) | - (source) | 115 | 78,621 | Worst-positions + 20 empty-scene episodes (eps 95-114, no bottle/no movement) | | [right-hand-v2](https://huggingface.co/datasets/cloudwalk-research/gr00t-g1-grab-bottle-right-hand-v2) | 105ep-v1 | 210 | 62,772 | Distance-to-goal wandering removal -> **v2 fine-tune (production champion)** | | [zero-wandering-smooth-radius-5](https://huggingface.co/datasets/cloudwalk-research/gr00t-g1-grab-bottle-right-hand-zero-wandering-smooth-radius-5) | 105ep-v1 | 417 | 48,577 | Zero-wandering r=5 (most aggressive) -> **v4 fine-tune** | | [zero-wandering-smooth-radius-10](https://huggingface.co/datasets/cloudwalk-research/gr00t-g1-grab-bottle-right-hand-zero-wandering-smooth-radius-10) | 105ep-v1 | 371 | 49,578 | Zero-wandering r=10 (experiment, no fine-tune) | | [zero-wandering-smooth-radius-15](https://huggingface.co/datasets/cloudwalk-research/gr00t-g1-grab-bottle-right-hand-zero-wandering-smooth-radius-15) | 105ep-v1 | 342 | 50,634 | Zero-wandering r=15 (experiment, no fine-tune) | | **[zero-wandering-smooth-radius-20](https://huggingface.co/datasets/cloudwalk-research/gr00t-g1-grab-bottle-right-hand-zero-wandering-smooth-radius-20) (this)** | 105ep-v1 | 314 | 50,496 | Zero-wandering r=20 (least aggressive) -> **v5 fine-tune** | | [radius-20-merged](https://huggingface.co/datasets/cloudwalk-research/gr00t-g1-grab-bottle-right-hand-radius-20-merged) | 105ep-v1 + 115ep-v3 | 502 | 120,017 | Both sources, zero-wandering r=20, grasp-frame preservation; 115ep eps 0-94 curated, 95-114 as-is -> **v6 fine-tune** | | [speedup-3mm-v1](https://huggingface.co/datasets/cloudwalk-research/gr00t-g1-grab-bottle-right-hand-speedup-3mm-v1) | 105ep-v1 + 115ep-v3 | 220 | 60,163 | DP wrist-Cartesian resample 3mm/frame, max_K=40, NO segment removal -> **v7 fine-tune (trained 2026-06-25, checkpoint-20000 only)** | | [speedup-3mm-cycle-removed-v1](https://huggingface.co/datasets/cloudwalk-research/gr00t-g1-grab-bottle-right-hand-speedup-3mm-cycle-removed-v1) | 105ep-v1 + 115ep-v3 | 405 | 47,944 | DP resample + cycle detection + sub-episode splitting (no fine-tune yet) | ## Attribution Collected at the **CloudWalk Robotics Lab (CW-RL)** using PICO 4 Ultra teleoperation on the Unitree G1 with the [SONIC](https://github.com/NVlabs/GR00T-WholeBodyControl) whole-body controller, then curated with the kinematic zero-wandering pipeline. Stored in the [LeRobot v2.1](https://github.com/huggingface/lerobot) format. ## Citation ```bibtex @misc{cwrl_grab_bottle_radius_20_2026, title = {GR00T G1 "grab the bottle" --- right-hand zero-wandering curation, smoothing half-width 20 (314 episodes, SONIC WBC)}, author = {{CloudWalk Robotics Lab}}, year = {2026}, howpublished = {Hugging Face dataset repository}, url = {https://huggingface.co/datasets/cloudwalk-research/gr00t-g1-grab-bottle-right-hand-zero-wandering-smooth-radius-20} } @article{gr00t_n1_2025, title = {{GR00T N1}: An Open Foundation Model for Generalist Humanoid Robots}, author = {{NVIDIA}}, year = {2025}, eprint = {2503.14734}, archivePrefix = {arXiv}, url = {https://arxiv.org/abs/2503.14734} } @article{sonic_2025, title = {{SONIC}: Whole-Body Controller for Humanoid Robot Teleoperation}, year = {2025}, eprint = {2511.07820}, archivePrefix = {arXiv}, url = {https://arxiv.org/abs/2511.07820} } @software{lerobot2024, title = {{LeRobot}: State-of-the-art Machine Learning for Real-World Robotics in {PyTorch}}, author = {{Hugging Face}}, url = {https://github.com/huggingface/lerobot}, year = {2024} } ``` ## License Released under **Apache-2.0** by CloudWalk Research. Demonstrations were collected on hardware owned by CloudWalk; the SONIC WBC and GR00T stacks retain their respective upstream licenses. Research preview - closed-loop deployment on a physical humanoid requires human oversight.