nvidia/GR00T-E2E-Reference-Workflow-SIM-H2-N1.7-AppleHandoverPlate-Model
Robotics • 3B • Updated • 48
Search is not available for this dataset
observation.state list | action list | timestamp float32 0 18.4 | frame_index int64 0 1.84k | episode_index int64 0 999 | index int64 0 1.18M | task_index int64 0 0 | teleop.navigate_command list |
|---|---|---|---|---|---|---|---|
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End of preview. Expand in Data Studio
H2 Plus Dataset Card
Dataset Description:
The GR00T-E2E-Reference-Workflow-SIM-H2-N1.7-AppleHandoverPlate-Dataset is a multimodal collection of trajectories generated in simulation for a Unitree H2 Plus humanoid robot with Sharpa hands. It supports a stationary bimanual manipulation task in which the robot picks up an apple with its left hand, transfers it to its right hand, and releases it onto a plate. Each entry provides the full context (state, vision, language, action) needed to train and evaluate generalist robot policies for an apple handover task.
| Dataset Name | # Trajectories |
|---|---|
| H2 SIM AppleHandoverPlate Task (seed) | 10 |
| H2 SIM AppleHandoverPlate Task (generated) | 1,000 |
The dataset contains only simulation demonstrations, collected by NVIDIA employees using NVIDIA hardware and software. It contains no real-robot recordings.
This dataset is ideal for behavior cloning, policy learning, and generalist robotic manipulation research. It has been used for post-training the GR00T N1.7 model.
This dataset is ready for commercial or non-commercial uses.
Dataset Owner(s):
NVIDIA Corporation
Dataset Creation Date:
09/13/2026 (Hugging Face repository creation date)
Release Date:
10/01/2026
Versioning:
v1.0
Previous Version(s): No previous version
Relationship to Previous Version(s): N/A
License/Terms of Use:
This dataset is governed by the Creative Commons Attribution 4.0 International License (CC-BY-4.0).
Intended Usage:
This dataset is intended for:
- Training robot manipulation policies using behavior cloning.
- Research in generalist robotics and task-conditioned agents.
- Sim-to-real / Sim-to-Sim transfer studies.
Dataset Characterization:
Data Collection Method
[Hybrid: Manually-Collected, Synthetic]
The 10 seed demonstrations were manually collected through human teleoperation in simulation using an XR headset. The 1,000 generated demonstrations were produced from those seeds via Isaac AutoData. Only successful generation attempts are included. The LeRobot dataset is recorded at 100 Hz.
Labeling Method
[Hybrid: Manually-Labeled, Automated]
Seed demonstration labels (task description) were human-assigned; generated demonstrations inherit the same task label automatically from their seed via Isaac AutoData.
Dataset Format:
We provide the dataset in two forms:
seed/raw/source-10.hdf5— the original 10 human seed demonstrations (raw HDF5).generated/raw/generated-017.hdf5andgenerated/raw/generated-983.hdf5— the 1,000 AutoData-generated demonstrations, split across two files (episodes 0–16 and 17–999 respectively);generated/raw/combined-1000.hdf5is a relative-link HDF5 view over both (keep the two source files beside it; the combined view alone does not contain the trajectory payloads).generated/lerobot/— the standalone LeRobot v2.1 training dataset, converted from the generated HDF5 dataset, with parquet, videos, and metadata. Self-contained; does not require the raw HDF5 files for training.
Each demo in the LeRobot dataset consists of a time-indexed sequence of the following modalities:
Actions
- 75-dimensional joint position target, plus separate navigation command values, stored per frame.
meta/modality.jsonselects the 14 arm and 44 hand targets plus 3 navigation values for the 61-dimensional GR00T policy action used in training. The stored 75-joint target and the 61-dimensional policy action are not identical representations.
Observations
- 75-dimensional joint state.
- Head-camera RGB video.
Task-specific
timestamp(FP32) records time in seconds;frame_index,episode_index,index, andtask_indexare INT64 indices.- The recorded training task text is
move the apple to the plate. collection.jsonrecords counts, source hashes, layout, and lineage.files.sha256.jsonrecords file sizes and SHA-256 checksums.
Videos
- 640 x 480 head-camera RGB video in mp4 format, at 100 fps, H.264 with yuv420p encoding; no audio or depth channel.
In addition, a set of metadata describing the following is provided in generated/lerobot/meta/,
episodes.jsonlcontains a list of all the episodes in the entire dataset.tasks.jsonlcontains a list of all the tasks in the entire dataset.modality.jsoncontains the modality configuration.info.jsoncontains the dataset information.
Representation note
The raw HDF5 stores replay actions, joint position targets, navigation commands, observations, head-camera images, initial state, and simulator states. Original acquisition and simulator-asset provenance metadata are retained; simulator and robot assets themselves are external and are not distributed here. Do not treat raw replay actions, the stored 75-joint target, and the policy's 61-dimensional action as identical representations.
The model was trained on the 1,000 generated episodes only; the 10 human seeds provide generation lineage and were not included as 10 extra training episodes. All training episodes are in the training split. This collection does not supply an independent held-out human demonstration test set.
Associated model
The associated checkpoint-10000 model scored 369/500 successes (73.8%) in a separate closed-loop simulation evaluation. That is a policy evaluation result, not a dataset success-rate estimate or a claim about performance on physical robots.
Dataset Quantification:
Record Count
H2 SIM AppleHandoverPlate Task
| Measurement | Count |
|---|---|
| Seed demonstrations/trajectories | 10 |
| Generated demonstrations/trajectories | 1,000 |
| RGB videos | 1,000 |
| Frames (LeRobot) | 1,178,888 |
The Hugging Face size category counts LeRobot frame records, not trajectories.
Feature Count
9 top-level LeRobot features: observation.state, action, observation.images.ego_view, timestamp, frame_index, episode_index, index, task_index, and teleop.navigate_command.
Total Storage
115.5 GB
Reference(s):
H2 Plus workflow (https://github.com/nvidia-isaac/GR00T-E2E-Reference-Workflow/blob/release/v0.2/docs/workflows/h2-pick-handover-place.md)
Ethical Considerations:
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal developer teams to ensure this dataset meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here.
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