| --- |
| license: cc-by-4.0 |
| task_categories: |
| - robotics |
| tags: |
| - robotics |
| --- |
| ## Dataset Description: |
|
|
| The Arena-G1-Static-PickNPlace-Task dataset is a multimodal collection of trajectories generated in Isaac Lab. It supports humanoid (G1) loco-manipulation task in IsaacLab-Arena environment. Each entry provides the full context (state, vision, language, action) needed to train and evaluate generalist robot policies for an apple pick-and-place task. |
|
|
| | Dataset Name | # Trajectories | |
| |---------------------------|----------------| |
| | G1 Static PickNPlace Task | 200 | |
|
|
| This dataset is ideal for behavior cloning, policy learning, and generalist robotic manipulation research. It has been for post-training GR00T N1.7 model. |
|
|
| This dataset is ready for commercial use. |
|
|
| ## Dataset Owner |
| NVIDIA Corporation |
|
|
| ## Dataset Creation Date: |
| 05/22/2025 |
|
|
| ## 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 |
|
|
| - Human |
| |
| All 200 demonstrations were manually collected through human teleoperation using a XR headset in Isaac Lab. Each demo was recorded at 50 Hz. |
|
|
| ### Labeling Method |
|
|
| Human |
|
|
| ## Dataset Format: |
| We provide a few dataset files, including |
| - A manually collected 200-demonstration HDF5 dataset file (`arena_g1_static_apple_dataset_recorded_200_demos.hdf5`) |
| - A LeRobot format dataset, converted from the collected HDF5 dataset. Converted dataset is in the `lerobot` directory. |
|
|
| Each demo in GR00T-Lerobot datasets consists of a time-indexed sequence of the following modalities: |
|
|
| ### Actions |
| - action (FP32): joint desired positions for all body joints (43 DoF) |
|
|
| ### Observations |
| - observation.state (FP32): joint positions for all body joints (43 DoF) |
|
|
| ### Task-specific |
| - timestamp (FP64): simulation time in seconds of each recorded data entry. |
| - annotation.human.task_description (INT64): index referring to the language instruction recorded in the metadata |
| - episode_index (INT64): index indicating the order of each demo |
| - task_index (INT64): index used in multi-task data loader. Not applicable to Gr00t-N1 post training, always set to 0. |
| |
| |
| ### Videos |
| - 640 x 480 RGB videos in mp4 format from an egocentric (ego-view) camera |
| |
| In additional, a set of metadata describing the followings is provided, |
| - `episodes.jsonl` contains a list of all the episodes in the entire dataset. Each episode contains a list of tasks and the length of the episode. |
| - `tasks.jsonl` contains a list of all the tasks in the entire dataset. |
| - `modality.json` contains the modality configuration. |
| - `info.json` contains the dataset information. |
| |
| |
| ## Dataset Quantification: |
| |
| ### Record Count |
| |
| #### G1 Static PickNPlace Task |
| - Number of demonstrations/trajectories: 200 |
| - Number of RGB videos: 200 |
| |
| ### Total Storage |
| |
| 10.2 GB |
| |
| ## 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. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. |
| |
| Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/). |
| |
