nvidia/Arena-G1-Loco-Manipulation-Task

Dataset Description:

The Arena-G1-Loco-Manipulation-Task dataset is multimodal collections 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 box pick and place task.

Dataset Name	# Trajectories
G1 Loco-Manipulation Task	50

This dataset is ideal for behavior cloning, policy learning, and generalist robotic (loco) manipulation research. It has been for post-training GR00T N1.5 model.

This dataset is ready for commercial use.

Dataset Owner

NVIDIA Corporation

Dataset Creation Date:

10/10/2025

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

• Automated
• Automatic/Sensors
• Synthetic

5 human teleoperated demonstrations are collected through a depth camera and keyboard in Isaac Lab. All 50 demos are generated automatically using a synthetic motion trajectory generation framework, Mimicgen [1]. Each demo is generated at 50 Hz.

Labeling Method

Not Applicable

Dataset Format:

We provide a few dataset files, including - a human-annoated 5 demonstrations in HDF5 dataset file (arena_g1_loco_manipulation_dataset_annotated.hdf5) - a Mimic-generated 1 demostration in HDF5 dataset file (arena_g1_loco_manipulation_dataset_generated_small.hdf5) - a Mimic-generated 50 demonstrations in HDF5 dataset file (arena_g1_loco_manipulation_dataset_generated.hdf5) - a GR00T-Lerobot formatted dataset converted from the Mimic-generated HDF5 dataset file (lerobot)

Each demo in GR00T-Lerobot datasets consists of a time-indexed sequence of the following modalities:

Actions

• action (FP64): joint desired positions for all body joints (26 DoF)

Observations

• observation.state (FP64): joint positions for all body joints (26 DoF)

Task-specific

• timestamp (FP64): simulation time in seconds of each recorded data entry.
• annotation.human.action.task_description (INT64): index referring to the language instruction recorded in the metadata
• annotation.human.action.valid (INT64): index indicating validity of annotaion 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

• 256 x 256 RGB videos in mp4 format from first-person-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 Loco-Manipulation Task

• Number of demonstrations/trajectories: 50
• Number of RGB videos: 50

Total Storage

24.1 GB

Ethical Considerations:

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Reference(s):

[1] @inproceedings{mandlekar2023mimicgen, title={MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations}, author={Mandlekar, Ajay and Nasiriany, Soroush and Wen, Bowen and Akinola, Iretiayo and Narang, Yashraj and Fan, Linxi and Zhu, Yuke and Fox, Dieter}, booktitle={7th Annual Conference on Robot Learning}, year={2023} }