Research ArticleARTIFICIAL INTELLIGENCE
A formal methods approach to interpretable reinforcement learning for robotic planning
- View ORCID ProfileXiao Li1,*,
- View ORCID ProfileZachary Serlin1,
- Guang Yang2 and
- Calin Belta1,2
- 1Department of Mechanical Engineering, Boston University, Boston, MA, USA.
- 2Division of Systems Engineering, Boston University, Boston, MA, USA.
- ↵*Corresponding author. Email: xli87{at}bu.edu
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Science Robotics 18 Dec 2019:
Vol. 4, Issue 37, eaay6276
DOI: 10.1126/scirobotics.aay6276
Vol. 4, Issue 37, eaay6276
DOI: 10.1126/scirobotics.aay6276
Xiao Li
1Department of Mechanical Engineering, Boston University, Boston, MA, USA.
Zachary Serlin
1Department of Mechanical Engineering, Boston University, Boston, MA, USA.
Guang Yang
2Division of Systems Engineering, Boston University, Boston, MA, USA.
Calin Belta
1Department of Mechanical Engineering, Boston University, Boston, MA, USA.
2Division of Systems Engineering, Boston University, Boston, MA, USA.
