datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
PhysicalAI-Robotics-GR00T-X-Embodiment-Sim
PhysicalAI-Robotics-GR00T-X-Embodiment-Sim
Github Repo: Isaac GR00T N1
We provide a set of datasets used for post-training of GR00T N1. Each dataset is a collection of trajectories from different robot embodiments and tasks.
Cross-embodied bimanual manipulation: 9k trajectories
Dataset Name
#trajectories
bimanual_panda_gripper.Threading
1000
bimanual_panda_hand.LiftTray
1000
bimanual_panda_gripper.ThreePieceAssembly
1000… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-GR00T-X-Embodiment-Sim.PhysicalAI-Robotics-Open-H-Embodiment
Dataset Description:
Open-H-Embodiment is a community‑driven dataset initiative building the open, shared foundation needed to train and evaluate AI autonomy models for surgical robotics and ultrasound.
This dataset is a multi-embodiment collection of LeRobot datasets of paired kinematics and video, across tasks such as tabletop exercises, clinical procedures, as well as simulations of healthcare robotics applications.
Maintainer / Hosting Organization:
NVIDIA… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-Open-H-Embodiment.Retargeted_AMASS_for_robotics
Retargeted AMASS for Robotics
Project Overview
This project aims to retarget motion data from the AMASS dataset to various robot models and open-source the retargeted data to facilitate research and applications in robotics and human-robot interaction. AMASS (Archive of Motion Capture as Surface Shapes) is a high-quality human motion capture dataset, and the SMPL-X model is a powerful tool for generating realistic human motion data.
By adapting the motion data from AMASS… See the full description on the dataset page: https://huggingface.co/datasets/fleaven/Retargeted_AMASS_for_robotics.PhysicalAI-Robotics-Locomanipulation-GRAIL
📢 News
[2026-07-15] Released task-general tracking policy checkpoints trained on the released data. Follow the tracking doc to use them to track our released motion data.
[2026-07-14] Updated data/pickup_table and data/pickup_ground. If you downloaded them before this date, please re-download.
Dataset Overview
Tabletop Pickup
Ground Pickup
Tabletop Manipulation
Ground Manipulation
Sitting
Curb
Slope… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-Locomanipulation-GRAIL.TruckDrive
TruckDrive: Long-Range Autonomous Highway Driving Dataset
Torc Robotics · Princeton University · CVPR 2026
Filippo Ghilotti, Edoardo Palladin, Samuel Brucker, Adam Sigal, Mario Bijelic, Felix Heide
TruckDrive is a long-range autonomous highway driving dataset designed for heavy-truck safety, perception, prediction, and planning research. It targets high-speed highway operation, where reliable scene understanding hundreds of meters ahead is required for anticipatory… See the full description on the dataset page: https://huggingface.co/datasets/Torc-Robotics/TruckDrive.PhysicalAI-Robotics-Manipulation-Kitchen-Demos
PhysicalAI-Robotics-Manipulation-Kitchen-Demos
We provide a 600 hours of human-teleoperated demonstrations across 316 different tasks, totalling 55k trajectories.
The datasets are collected using Franka Panda robot with an Omron mobile base.
The datasets follow the LeRobot format. Here is an overview of important elements of each dataset:
Click to expand dataset structure
lerobot/
├── meta/ # Metadata files describing the dataset
│ ├── info.json… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-Manipulation-Kitchen-Demos.WIYH
WorldCode Exported JSON Field Specification
The following specification is based on the actual outputs produced by the export script, as well as the example file worldcode_HS-2-1420125020208_2025-10-21-14-50-09_3_s0_vlta_reorg_sample_1-2.json.
Top-level Fields
Field
Description
worldcode_name
Name of the current sample, typically also the filename of the exported JSON.
dataset_path
Absolute path to the original worldcode directory.
task_description… See the full description on the dataset page: https://huggingface.co/datasets/tars-robotics/WIYH.PhysicalAI-Robotics-Manipulation-SingleArm
Dataset Description:
PhysicalAI-Robotics-Manipulation-SingeArm is a collection of datasets of automatic generated motions of a Franka Panda robot performing operations such as block stacking, opening cabinets and drawers. The dataset was generated in IsaacSim leveraging task and motion planning algorithms to find solutions to the tasks automatically [1, 3]. The environments are table-top scenes where the object layouts and asset textures are procedurally generated [2].This dataset… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-Manipulation-SingleArm.PhysicalAI-Robotics-GR00T-Teleop-Sim
Simulation GR1 Tabletop Task 1K Dataset
Dataset Description:
The PhysicalAI-Robotics-GR00T-Teleop-GR1 dataset consists of 1000 teleoperation trajectories in simulation using the GR1 robot with upper body control. The simulation setup mimics tabletop manipulation tasks and uses RGB observations with a virtual camera. The robot is equipped with simulated Fourier hands.
This dataset is ready for non-commercial use.
Dataset Owner(s):
NVIDIA GEAR… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-GR00T-Teleop-Sim.PhysicalAI-Robotics-GR00T-Teleop-GR1
Introduction
TL;DR: DreamDojo is a generalist robot world model pretrained on 44k hours of human egocentric data, showing unprecedented generalization to diverse objects and environments.
Project page: https://dreamdojo-world.github.io/
Paper: https://arxiv.org/abs/2602.06949
Code: https://github.com/NVIDIA/DreamDojo
How to Use
Check out https://github.com/NVIDIA/DreamDojo
Citation
@article{gao2026dreamdojo,
title={DreamDojo: A Generalist Robot… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-GR00T-Teleop-GR1.OmniVitacpico-robotics-basic
Pico Egocentric Dataset · Basic Edition
First-person stereo video, depth and head pose of real shelf-work and facility tasks, captured on a Pico VR headset.
By OpenElephant Intelligence (公象智能), Beijing.
This is the free, open entry point to a much larger commercial collection (10,000+ hours).
It is meant for evaluating the data quality and format before requesting the
Advanced Edition or a commercial license.
At a glance
Episodes
1,203 across 21… See the full description on the dataset page: https://huggingface.co/datasets/skycn110/pico-robotics-basic.reachy-mini-emotions-library
Reachy Mini Emotions Library
Curated emotion recordings for the Reachy Mini robot, maintained by
Pollen Robotics. Each move is a JSON trajectory (head pose, antennas,
body yaw, sampled over time) paired with an Opus audio track.
Motion is sampled at 50 Hz; audio is mono Ogg/Opus (decoded natively by
the robot). Requires reachy_mini ≥ v1.8.4 (its move loader resolves
non-.wav audio sidecars).
File layout
Files live at the root of the dataset, named <emotion>.json +… See the full description on the dataset page: https://huggingface.co/datasets/pollen-robotics/reachy-mini-emotions-library.pico-robotics-annotated
This edition has been merged into the Advanced Edition
The Annotated Edition is discontinued. Its time-stamped action captions (segments.json) are now
included in every episode of the Advanced Edition.
Free entry point: Basic Edition
Point clouds, MCAP and captions: Advanced Edition
Commercial licensing and the full 10,000+ hour collection: jiuchen@openelephant.ai
— OpenElephant Intelligence (公象智能)
reachy-mini-wall-data
Reachy Mini — wall data (public)
posts.json for the Reachy Mini community wall: the AI-filtered posts shown publicly,
aggregated from Bluesky, YouTube, LinkedIn, TikTok, X and Reddit by the social-wall pipeline.
Fetch it directly (CORS-enabled) from any static site:
const url = "https://huggingface.co/datasets/pollen-robotics/reachy-mini-wall-data/resolve/main/posts.json";
const posts = await (await fetch(url)).json();
Each item: id, platform, author, handle, avatar, text… See the full description on the dataset page: https://huggingface.co/datasets/pollen-robotics/reachy-mini-wall-data.malware-samples
This dataset is part of the ULE-CIBERLAB Project: Transfer of knowledge in cybersecurity for the country's business fabric, funded by the European Union NextGeneration-EU, Recovery, Transformation and Resilience Plan, through INCIBE.
MALWARE-SAMPLES DATASET
Disclaimer: This repository contains real samples of malware that can be executed (.exe) and artifacts related with their execution in CAPEv2 sandbox (JSON/HTML reports, screenshots, dropped files). DO NOT execute any of… See the full description on the dataset page: https://huggingface.co/datasets/unileon-robotics/malware-samples.pico-robotics-advanced
Pico Egocentric Dataset · Advanced Edition
First-person stereo video, depth, head pose, pose-aligned point clouds and time-stamped action captions of real shelf-work and facility tasks, captured on a Pico VR headset.
By OpenElephant Intelligence (公象智能), Beijing.
The Advanced Edition adds 3D geometry and language to the
Basic Edition: every episode comes with
world-aligned point clouds, a ready-to-view MCAP recording, and a sequence of time-stamped
captions describing what the… See the full description on the dataset page: https://huggingface.co/datasets/skycn110/pico-robotics-advanced.rdt-ft-data
Dataset Card
This is the fine-tuning dataset used in the paper RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation.
Source
Project Page: https://rdt-robotics.github.io/rdt-robotics/
Paper: https://arxiv.org/pdf/2410.07864
Code: https://github.com/thu-ml/RoboticsDiffusionTransformer
Model: https://huggingface.co/robotics-diffusion-transformer/rdt-1b
Uses
Download all archive files and use the following command to extract:
cat rdt_data.tar.gz.* |… See the full description on the dataset page: https://huggingface.co/datasets/robotics-diffusion-transformer/rdt-ft-data.community-benign-samples
This dataset is part of the ULE-CIBERLAB Project: Transfer of knowledge in cybersecurity for the country's business fabric, funded by the European Union NextGeneration-EU, Recovery, Transformation and Resilience Plan, through INCIBE.
MALWARE-SAMPLES DATASET
Disclaimer: This repository contains benign samples with their execution in CAPEv2 sandbox (JSON/HTML reports, screenshots, dropped files). This README file explains how dataset is structured, its metadata, safe use as well… See the full description on the dataset page: https://huggingface.co/datasets/unileon-robotics/community-benign-samples.community-suspicious-samples
This dataset is part of the ULE-CIBERLAB Project: Transfer of knowledge in cybersecurity for the country's business fabric, funded by the European Union NextGeneration-EU, Recovery, Transformation and Resilience Plan, through INCIBE.
MALWARE-SAMPLES DATASET
Disclaimer: This repository may contain real samples of malware that can be executed (.exe) and artifacts related with their execution in CAPEv2 sandbox (JSON/HTML reports, screenshots, dropped files). DO NOT execute any of… See the full description on the dataset page: https://huggingface.co/datasets/unileon-robotics/community-suspicious-samples.PhysicalAI-Robotics-Manipulation-Objects-Kitchen-MJCF
Manipulation Objects Kitchen MJCF
We provide a collection of digital 3D mjcf assets intended for use in a simulated kitchen environment.
The assets are broadly divided into 2 categories: fixtures and objects. The fixture assets are comprised of interactable kitchen appliances such as stoves, microwaves, and ovens. The object assets consist of common kitchen objects such as saucepans and glass cups.
Preview
Objects
Fixtures
Objects… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-Manipulation-Objects-Kitchen-MJCF.SharpaDex-v1.0SharpaDex v1.0
A multimodal dataset of real-world teleoperated demonstrations for bimanual dexterous manipulation.
Overview ·
Get Started ·
Tasks ·
Examples ·
Features ·
Language ·
Citation
An animated overview of 56 tasks in a uniform 8 × 7 grid, with head-camera and wrist-camera observations distributed throughout the montage. It spans assembly, tool use, deformable-object manipulation, cleaning, material transfer, and long-horizon activities. Open MP4.… See the full description on the dataset page: https://huggingface.co/datasets/Sharpa-Robotics/SharpaDex-v1.0.PhysicalAI-Robotics-NuRec
Dataset Description
The Physical AI NuRec dataset seeks to empower robotic researchers to build the next generation of physical AI based end-to-end robotic models.
This dataset includes various 3DGUT in USD files that can be loaded in Isaac Sim. Some datasets also include a mesh and occupancy map. The Mesh components are used for collision detection while the 3DGUT components provide realistic rendering. The asset can also be used with Isaac Sim Extensions like MobilityGen for… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-NuRec.DatasetDemo
Motus Training Dataset Demo
Introduction
This repository serves as a demonstration dataset illustrating the required data format for training the Motus model. It provides a reference for structuring your data to ensure compatibility with the training pipeline. The demo data come from Robotwin-clean benchmark.
Directory Structure
Data is generally organized following a hierarchy of Dataset Name, Task Name (optional), and Data Type.
Standard Format:… See the full description on the dataset page: https://huggingface.co/datasets/motus-robotics/DatasetDemo.reachy-mini-dances-library
Reachy Mini Dances Library
Curated dance moves for the Reachy Mini robot, maintained by Pollen
Robotics. Each move is a JSON trajectory (head pose, antennas, body
yaw, sampled over time). Motion-only — no audio tracks in this set.
File layout
Files live at the root of the dataset, named <dance>.json.
How to use
Python — via the reachy_mini package:
from reachy_mini import ReachyMini
from reachy_mini.motion.recorded_move import RecordedMoves
library… See the full description on the dataset page: https://huggingface.co/datasets/pollen-robotics/reachy-mini-dances-library.Uranus-Demo-Data
Download
Download and unpack the samples from either Hugging Face or ModelScope into examples/data/.
Hugging Face
pip install -U huggingface_hub
hf download D-Robotics/Uranus-Demo-Data \
--repo-type dataset \
--local-dir ./examples/data
ModelScope
pip install -U modelscope
modelscope download \
--dataset D-Robotics/Uranus-Demo-Data \
--local_dir ./examples/data
Each episode lands in ./examples/data/<episode_id>/ and can be passed… See the full description on the dataset page: https://huggingface.co/datasets/D-Robotics/Uranus-Demo-Data.PhysicalAI-Robotics-Manipulation-Augmented
Dataset Description:
This is a fully annotated, synthetically generated dataset consisting of 1,000 demonstrations of a single Franka Panda robot arm performing a fixed-order three-cube stacking task in Isaac Lab. The robot consistently stacks cubes in the order: blue (bottom) → red (middle) → green (top).
The dataset was produced using the following pipeline:
Collected 10 human teleoperation demonstrations of the stacking task.
Used Isaac Lab’s Mimic tool [1] to simulate 1,000… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-Manipulation-Augmented.pollen_robotic_fleet_usagePhysicalAI-Robotics-GR00T-Teleop-G1
Unitree G1 Fruits Pick and Place 1K Dataset
Dataset Description:
The PhysicalAI-Robotics-GR00T-Teleop-G1 dataset consists of1000 teleoperation trajectories of real robot data using Unitree G1, with upper body control. The robot chooses the correct fruit to pick and place on the plate according to the language prompt. A total of 4 fruits are used: Apple, Pear, Starfruit, Grape. The robot is equipped with the default realsense camera, and a pair of Unitree G1 Tri-fingers… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-GR00T-Teleop-G1.kine2go
Kine2Go: Kinematic dataset for the Unitree Go2 robot with diverse gaits and motions
A kinematic motion dataset for the Unitree Go2 quadruped robot. Forty reference clips (dog, horse, and synthetic robot motions) are retargeted to the Go2 morphology and paired with a per-clip imitation-learning policy (PPO) and 20 perturbed rollouts with rendered video. Designed to support training and regularization of behavioral foundation models for legged locomotion (Meta Motivo style), with… See the full description on the dataset page: https://huggingface.co/datasets/MIMUW-Robotics/kine2go.