KRAFTON/PAI_UE-IsaacSim-Navigation

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.

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 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:

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:

python gr00t/data/stats.py <dataset_path> --embodiment-tag <embodiment-tag>

For example:

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.