ACRONYM
ACRONYM is a robot grasp planning dataset jointly created by NVIDIA and the University of Washington. It comprises 17.744 million parallel‑jaw grasp samples over 8,872 objects from ShapeNetSem, spanning 262 categories. Grasp outcomes are labeled using the physical simulation engine FleX, providing high‑density and physically realistic grasps. Samples were generated via simulation with an anti‑symmetry sampling scheme and recorded in a zero‑gravity environment, noting success or failure. ACRONYM is intended to improve robotic grasping in complex settings, especially for learning‑driven grasp algorithm training and real‑world precision grasping.
Dataset description and usage context
ACRONYM Dataset Overview
Dataset Summary
ACRONYM is a dataset containing 17.7 M simulated parallel‑jaw grasps across 8,872 objects, generated using NVIDIA FleX.
Dataset Contents
- Contains samples of grasp data
- Provides tools for visualizing grasps, generating random scenes, and rendering observations
License
- Source code is released under the MIT License
- Dataset is released under the CC BY‑NC 4.0 License
Environment Requirements
- Python 3
- Install dependencies listed in
requirements.txtvia pip
Usage Examples
Visualize Grasps
acronym_visualize_grasps.py [-h] [--num_grasps NUM_GRASPS] input [input ...]
Generate Random Scenes and Visualize Grasps
generate_scene.py [-h] [--objects OBJECTS [OBJECTS ...]] --support SUPPORT [--support_scale SUPPORT_SCALE] [--show_grasps] [--num_grasps_per_object NUM_GRASPS_PER_OBJECT]
Render and Visualize Observations
render_observations.py [-h] [--objects OBJECTS [OBJECTS ...]] --support SUPPORT [--support_scale SUPPORT_SCALE] [--show_scene]
Obtaining the Full Dataset
- Download the full dataset (1.6 GB): acronym.tar.gz
- Download the ShapeNetSem meshes
- Create watertight versions of the meshes
Dataset Citation
@inproceedings{acronym2020, title = {{ACRONYM}: A Large-Scale Grasp Dataset Based on Simulation}, author = {Eppner, Clemens and Mousavian, Arsalan and Fox, Dieter}, year = {2020}, booktitle = {Under Review at ICRA 2021} }
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