Research ArticleARTIFICIAL INTELLIGENCE

A formal methods approach to interpretable reinforcement learning for robotic planning

See allHide authors and affiliations

Science Robotics  18 Dec 2019:
Vol. 4, Issue 37, eaay6276
DOI: 10.1126/scirobotics.aay6276

Article Information

vol. 4 no. 37

DOI: 
https://doi.org/10.1126/scirobotics.aay6276
Published By: 
Science Robotics
History: 
  • Received July 5, 2019
  • Accepted November 4, 2019
  • .
Copyright & Usage: 
Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works http://www.sciencemag.org/about/science-licenses-journal-article-reuseThis is an article distributed under the terms of the Science Journals Default License.

Author Information

  1. Xiao Li1,*,
  2. Zachary Serlin1,
  3. Guang Yang2 and
  4. Calin Belta1,2
  1. 1Department of Mechanical Engineering, Boston University, Boston, MA, USA.
  2. 2Division of Systems Engineering, Boston University, Boston, MA, USA.
  1. ↵*Corresponding author. Email: xli87{at}bu.edu

Altmetric

Article usage

Article usage: December 2019 to March 2021

AbstractFullPdf
Dec 201934579581487
Jan 20205987328202
Feb 2020619134127
Mar 202041910465
Apr 20202096641
May 20202516839
Jun 20202247627
Jul 20202085935
Aug 20201605140
Sep 20201657744
Oct 202027911150
Nov 202019411449
Dec 202017417180
Jan 202148215127
Feb 202134232102
Mar 202163616

View Full Text

Article Tools

My saved folders

Stay Connected to Science Robotics

Related Content

Similar Articles in:

Citing Articles in:

Navigate This Article