ACRONYM: A Large-Scale Grasp Dataset Based on Simulation

11/18/2020
by   Clemens Eppner, et al.
6

We introduce ACRONYM, a dataset for robot grasp planning based on physics simulation. The dataset contains 17.7M parallel-jaw grasps, spanning 8872 objects from 262 different categories, each labeled with the grasp result obtained from a physics simulator. We show the value of this large and diverse dataset by using it to train two state-of-the-art learning-based grasp planning algorithms. Grasp performance improves significantly when compared to the original smaller dataset. Data and tools can be accessed at https://sites.google.com/nvidia.com/graspdataset.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

page 5

09/20/2021

Human Initiated Grasp Space Exploration Algorithm for an Underactuated Robot Gripper Using Variational Autoencoder

Grasp planning and most specifically the grasp space exploration is stil...
12/11/2019

A Billion Ways to Grasp: An Evaluation of Grasp Sampling Schemes on a Dense, Physics-based Grasp Data Set

Robot grasping is often formulated as a learning problem. With the incre...
08/13/2020

A Tendon-driven Robot Gripper with Passively Switchable Underactuated Surface and its Physics Simulation Based Parameter Optimization

In this paper, we propose a single-actuator gripper that can lift thin o...
09/17/2021

Learning to Model the Grasp Space of an Underactuated Robot Gripper Using Variational Autoencoder

Grasp planning and most specifically the grasp space exploration is stil...
08/24/2021

Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning

Isaac Gym offers a high performance learning platform to train policies ...
10/07/2020

Toward Stance-based Personas for Opinionated Dialogues

In the context of chit-chat dialogues it has been shown that endowing sy...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.