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Pick the cylinder and place it at the goal position
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Pick the cylinder and place it at the goal position
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[1.1429636478424072,0.3462490439414978,-0.7891761064529419,0.25613775849342346,0.014999999664723873,(...TRUNCATED)
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Pick the cylinder and place it at the goal position
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[1.1429636478424072,0.3462490439414978,-0.7891761064529419,0.25613775849342346,0.014999999664723873,(...TRUNCATED)
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Pick the cylinder and place it at the goal position
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Pick the cylinder and place it at the goal position
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Pick the cylinder and place it at the goal position
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Pick the cylinder and place it at the goal position
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Pick the cylinder and place it at the goal position
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Pick the cylinder and place it at the goal position
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Pick the cylinder and place it at the goal position
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[0.21719197928905487,0.7166120409965515,-0.6378158330917358,0.11934346705675125,0.006535731256008148(...TRUNCATED)
[0.21719197928905487,0.7166120409965515,-0.6378158330917358,0.11934346705675125,0.006535731256008148(...TRUNCATED)
End of preview. Expand in Data Studio
YAML Metadata Warning: empty or missing yaml metadata in repo card (https://huggingface.co/docs/hub/datasets-cards)

MuJoCo Pick-and-Place Dataset

Dataset Information

  • Task: Pick and place manipulation with Open Manipulator-X
  • Episodes: 100
  • Total Frames: 2558
  • FPS: 30
  • Robot: open_manipulator_x

Features

  • observation.image: RGB camera feed (480x640x3)
  • observation.state: Robot state (10D vector)
    • Joint positions (4D): joint_1, joint_2, joint_3, joint_4
    • Gripper positions (2D): gripper_left, gripper_right
    • Gripper tip position (3D): tip_x, tip_y, tip_z
    • Grasp state (1D): grasp_active
  • action: Next robot action (5D vector)
    • Target joint positions (4D): joint_1_target, joint_2_target, joint_3_target, joint_4_target
    • Target gripper position (1D): gripper_target
  • language_instruction: Task description in natural language

Usage

import pandas as pd
from pathlib import Path

# Load episode data
episode_df = pd.read_parquet("data/episode_000000.parquet")

# Load images
from PIL import Image
img = Image.open(episode_df.iloc[0]["image_path"])

Dataset Structure

smolvla_head1/
├── data/           # Episode data (parquet files)
├── meta/           # Metadata (info.json, episodes.jsonl)
├── images/         # RGB observation images  
└── README.md       # This file

Generated with MuJoCo Pick-and-Place Video Creator

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