Physis-AI/GR1_robot

GR1 Robot Teleoperation Dataset

A large-scale humanoid robot teleoperation dataset collected using the Fourier GR-1 (GR1T1) robot, part of the GR00T robotics initiative.

This is a true subset of nvidia/PhysicalAI-Robotics-GR00T-Teleop-GR1.

Dataset Summary

• Episodes: 22,209
• Total frames: 6,362,293
• Total tasks: 46,669
• Videos: 22,209
• Frequency: 20 FPS
• Robot: GR1T1 (Fourier GR-1 humanoid)

The dataset contains egocentric video observations (1280x800) paired with full-body robot states and actions across 44 degrees of freedom, including dual arms, dual dexterous hands, waist, and neck.

Repository Structure

GR1_robot/
├── meta/                   # Metadata (8 files)
│   ├── info.json           # Dataset summary & feature schema
│   ├── stats.json          # Per-feature statistics (mean, std, min, max, quantiles)
│   ├── metadata.json       # Full metadata with model configuration
│   ├── modality.json       # Modality-to-key mapping
│   ├── embodiment.json     # Robot embodiment info
│   ├── episodes.jsonl      # Per-episode metadata (22,209 lines)
│   ├── tasks.jsonl         # Per-task metadata (46,669 lines)
│   └── initial_actions.npz # Initial action array
├── data/                   # Trajectory data as Parquet files (2.9 GB)
│   └── chunk-{000..022}/episode_{000000..022208}.parquet
└── videos/                 # Egocentric videos as MP4 files (37.2 GB)
    └── chunk-{000..022}/observation.images.ego_view_freq20/episode_{000000..022208}.mp4

Features

Each episode Parquet file contains per-timestep records:

Feature	Dtype	Shape	Description
observation.state	float64	(44,)	Full-body joint positions
action	float64	(44,)	Full-body joint actions
timestamp	float64	(1,)	Timestep timestamp (s)
next.reward	float64	(1,)	Reward
next.done	bool	(1,)	Episode termination flag
task_index	int64	(1,)	Task identifier
episode_index	int64	(1,)	Episode index
index	int64	(1,)	Global frame index
annotation.human.*	int64	(1,)	Human annotations (verb, object, location, hand, rating, etc.)

State / Action Space

Both observation.state and action are 44-dimensional vectors:

Group	Start	End	Dims	Description
left_arm	0	7	7	Left arm joints
left_hand	7	13	6	Left dexterous hand
left_leg	13	19	6	Left leg (passive)
neck	19	22	3	Neck joints
right_arm	22	29	7	Right arm joints
right_hand	29	35	6	Right dexterous hand
right_leg	35	41	6	Right leg (passive)
waist	41	44	3	Waist joints

Video

• Resolution: 1280x800
• FPS: 20
• Codec: H.264 (YUV420P)
• View: Egocentric (robot head camera)

Splits

The dataset uses a single training split (0:100). Normalization statistics (mean, std, min, max, quantiles) for all features are provided in meta/stats.json.

Usage

With Hugging Face datasets

from datasets import load_dataset

# Load the full dataset (streaming recommended for this 40GB+ dataset)
dataset = load_dataset("Physis-AI/GR1_robot", split="train", streaming=True)

for episode in dataset:
    states = episode["observation.state"]  # (T, 44)
    actions = episode["action"]            # (T, 44)
    break

To load only metadata without downloading videos/parquets:

from datasets import load_dataset

dataset = load_dataset("Physis-AI/GR1_robot", split="train",
                       data_files="data/**/*.parquet", streaming=True)

With Pandas (local files)

import pandas as pd

ep = pd.read_parquet("data/chunk-000/episode_000000.parquet")
states = ep["observation.state"].values   # (T, 44)
actions = ep["action"].values             # (T, 44)
left_arm_state = states[:, 0:7]

Loading Videos

import cv2

cap = cv2.VideoCapture("videos/chunk-000/observation.images.ego_view_freq20/episode_000000.mp4")
while cap.isOpened():
    ret, frame = cap.read()
    if not ret:
        break
    # frame is (800, 1280, 3) BGR

Attribution

This dataset is a true subset of nvidia/PhysicalAI-Robotics-GR00T-Teleop-GR1.

License

MIT