The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints

Andrew Hundt, Varun Jain, Chia Hung Lin, Chris Paxton, Gregory D. Hager

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A robot can now grasp an object more effectively than ever before, but once it has the object what happens next? We show that a mild relaxation of the task and workspace constraints implicit in existing object grasping datasets can cause neural network based grasping algorithms to fail on even a simple block stacking task when executed under more realistic circumstances. To address this, we introduce the JHU CoSTAR Block Stacking Dataset (BSD), where a robot interacts with 5.1 cm colored blocks to complete an order-fulfillment style block stacking task. It contains dynamic scenes and real time-series data in a less constrained environment than comparable datasets. There are nearly 12,000 stacking attempts and over 2 million frames of real data. We discuss the ways in which this dataset provides a valuable resource for a broad range of other topics of investigation. We find that hand-designed neural networks that work on prior datasets do not generalize to this task. Thus, to establish a baseline for this dataset, we demonstrate an automated search of neural network based models using a novel multiple-input HyperTree MetaModel, and find a final model which makes reasonable 3D pose predictions for grasping and stacking on our dataset. The CoSTAR BSD, code, and instructions are available at sites.google.com/site/costardataset.

Original languageEnglish (US)
Title of host publication2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1797-1804
Number of pages8
ISBN (Electronic)9781728140049
DOIs
StatePublished - Nov 2019
Event2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019 - Macau, China
Duration: Nov 3 2019Nov 8 2019

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
CountryChina
CityMacau
Period11/3/1911/8/19

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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    Hundt, A., Jain, V., Lin, C. H., Paxton, C., & Hager, G. D. (2019). The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019 (pp. 1797-1804). [8967784] (IEEE International Conference on Intelligent Robots and Systems). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IROS40897.2019.8967784