| # Physical AI: Unreal Engine - Isaac Sim Navigation Dataset | |
| Demonstration datasets for the Unitree G1 humanoid robot performing vision-based object navigation in procedurally generated indoor environments. | |
| Physics calculation is performed using Isaac Sim while the rendering is performed by Unreal Engine. | |
| We assume the dataset is placed inside `demo_data` in [GR00T repository](https://github.com/NVIDIA/Isaac-GR00T). | |
| ## Task | |
| The robot receives a front-facing camera image and a language instruction specifying a target object in the scene. It must navigate to the target object using a combination of high-level velocity commands (HLC) and low-level joint position actions (LLC). | |
| Example instruction: | |
| ``` | |
| Scene contains: Plant in a Pot, Towel, Aerosol Spray Can, Light Blue Bucket, Old Clock. Navigate to the Old Clock. | |
| ``` | |
| ## Dataset Variants | |
| | Dataset | Episodes | Description | | |
| |---|---|---| | |
| | `g1_procedural_room_navigation_20260206_062009` | 100 | 5 objects per scene | | |
| | `g1_procedural_room_navigation_20260206_080307` | 100 | 1 object per scene | | |
| | `g1_procedural_room_navigation_20260206_095145` | 100 | 3 objects per scene | | |
| ## Dataset Format | |
| Each dataset follows the [LeRobot v2](https://github.com/huggingface/lerobot) format: | |
| ``` | |
| g1_procedural_room_navigation_*/ | |
| ├── meta/ | |
| │ ├── info.json # Schema, features, robot config, processing params | |
| │ ├── episodes.jsonl # Per-episode metadata (index, length, task instruction) | |
| │ ├── tasks.jsonl # Task index definitions | |
| │ ├── modality.json # Modality-to-column mapping with slice indices | |
| │ └── stats.json # Per-feature statistics (see Generating Statistics section below) | |
| ├── data/ | |
| │ └── chunk-{NNN}/ | |
| │ └── {episode_index:06d}.parquet | |
| └── videos/ | |
| └── chunk-{NNN}/ | |
| └── observation.images.front/ | |
| └── episode_{episode_index:06d}.mp4 | |
| ``` | |
| ## Features | |
| | Feature | Type | Shape | Description | | |
| |---|---|---|---| | |
| | `observation.images.front` | video | (480, 640, 3) | Front camera RGB at 50 fps | | |
| | `observation.state.joint_pos` | float32 | (29,) | Joint positions (rad) | | |
| | `observation.state.joint_vel` | float32 | (29,) | Joint velocities (rad/s) | | |
| | `observation.state.root_pos_w` | float32 | (3,) | Root position in world frame | | |
| | `observation.state.root_quat_w` | float32 | (4,) | Root orientation quaternion (w, x, y, z) | | |
| | `observation.state.root_lin_vel_b` | float32 | (3,) | Root linear velocity in body frame | | |
| | `observation.state.root_ang_vel_b` | float32 | (3,) | Root angular velocity in body frame | | |
| | `action.hlc_raw` | float32 | (3,) | Raw high-level command (vx, vy, omega_z) | | |
| | `action.hlc_processed` | float32 | (3,) | Processed HLC (scaled, shifted, clipped) | | |
| | `action.llc_raw` | float32 | (29,) | Raw low-level joint position targets | | |
| | `action.llc_processed` | float32 | (29,) | Processed LLC (scaled around default pose) | | |
| | `timestamp` | float64 | (1,) | Time in seconds from episode start | | |
| | `episode_id` | int64 | (1,) | Episode index | | |
| | `frame_id` | int64 | (1,) | Frame index within episode | | |
| ## Robot | |
| - **Model**: Unitree G1 | |
| - **Joints**: 29 DoF (legs, waist, arms, wrists) | |
| - **Joint order**: IsaacLab convention | |
| - **FPS**: 50 | |
| ## Combining Datasets | |
| To merge multiple collection sessions into a single dataset, edit `SOURCE_DATASETS` and `OUTPUT_DATASET` in the script, then run: | |
| ```bash | |
| python demo_data/scripts/combine_datasets.py | |
| ``` | |
| This will: | |
| - Re-index episodes continuously (0, 1, ..., N-1) across all sources | |
| - Copy parquet files with updated `episode_id` columns | |
| - Symlink video files to the originals (no duplication) | |
| - Merge `episodes.jsonl` with new indices | |
| - Create `meta/origin.yaml` tracking which source datasets were combined | |
| - Correctly bucket episodes into `chunk-NNN/` directories when total episodes exceed `chunks_size` | |
| ## Generating Statistics | |
| After combining (or for any new dataset), generate `stats.json` using: | |
| ```bash | |
| python gr00t/data/stats.py <dataset_path> --embodiment-tag <embodiment-tag> | |
| ``` | |
| For example: | |
| ```bash | |
| python gr00t/data/stats.py demo_data/g1_procedural_room_navigation_combined --embodiment-tag unitree_g1_navigation_vel | |
| ``` | |
| This computes per-feature statistics (mean, std, min, max, q01, q99) across all parquet files and writes them to `meta/stats.json`. It also generates `meta/relative_stats.json` for relative action representations if configured in the embodiment config. | |
| **Note**: The embodiment's modality configuration must be defined in `gr00t/configs/data/embodiment_configs.py` and the tag must be added in `gr00t/data/embodiment_tags.py` before running this script. | |