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Explaining Multi-stage Tasks by Learning Temporal Logic Formulas from
  Suboptimal Demonstrations

Explaining Multi-stage Tasks by Learning Temporal Logic Formulas from Suboptimal Demonstrations

3 June 2020
Glen Chou
N. Ozay
Dmitry Berenson
ArXivPDFHTML

Papers citing "Explaining Multi-stage Tasks by Learning Temporal Logic Formulas from Suboptimal Demonstrations"

13 / 13 papers shown
Title
$L^*LM$: Learning Automata from Examples using Natural Language Oracles
L∗LML^*LML∗LM: Learning Automata from Examples using Natural Language Oracles
Marcell Vazquez-Chanlatte
Karim Elmaaroufi
Stefan J. Witwicki
S. Seshia
19
4
0
10 Feb 2024
A Safe Preference Learning Approach for Personalization with
  Applications to Autonomous Vehicles
A Safe Preference Learning Approach for Personalization with Applications to Autonomous Vehicles
Ruya Karagulle
Nikos Arechiga
Andrew Best
Jonathan A. DeCastro
N. Ozay
24
5
0
30 Oct 2023
Pointwise-in-Time Explanation for Linear Temporal Logic Rules
Pointwise-in-Time Explanation for Linear Temporal Logic Rules
Noel Brindise
Cédric Langbort
19
1
0
24 Jun 2023
Data-Efficient Learning of Natural Language to Linear Temporal Logic
  Translators for Robot Task Specification
Data-Efficient Learning of Natural Language to Linear Temporal Logic Translators for Robot Task Specification
Jiayi Pan
Glen Chou
Dmitry Berenson
29
35
0
09 Mar 2023
Learning Task Requirements and Agent Capabilities for Multi-agent Task
  Allocation
Learning Task Requirements and Agent Capabilities for Multi-agent Task Allocation
Bo Fu
W. Smith
Denise M. Rizzo
Matthew Castanier
Maani Ghaffari
Kira Barton
19
4
0
07 Nov 2022
Learning Interpretable Temporal Properties from Positive Examples Only
Learning Interpretable Temporal Properties from Positive Examples Only
Rajarshi Roy
Jean-Raphael Gaglione
Nasim Baharisangari
Daniel Neider
Zhe Xu
Ufuk Topcu
AI4TS
AI4CE
19
13
0
06 Sep 2022
Demonstration Informed Specification Search
Demonstration Informed Specification Search
Marcell Vazquez-Chanlatte
Ameesh Shah
Gil Lederman
S. Seshia
31
3
0
20 Dec 2021
Gaussian Process Constraint Learning for Scalable Chance-Constrained
  Motion Planning from Demonstrations
Gaussian Process Constraint Learning for Scalable Chance-Constrained Motion Planning from Demonstrations
Glen Chou
Hao Wang
Dmitry Berenson
14
10
0
08 Dec 2021
Scalable Anytime Algorithms for Learning Fragments of Linear Temporal
  Logic
Scalable Anytime Algorithms for Learning Fragments of Linear Temporal Logic
Ritam Raha
Rajarshi Roy
Nathanaël Fijalkow
Daniel Neider
33
22
0
13 Oct 2021
Temporal and Object Quantification Networks
Temporal and Object Quantification Networks
Jiayuan Mao
Zhezheng Luo
Chuang Gan
J. Tenenbaum
Jiajun Wu
L. Kaelbling
T. Ullman
NAI
30
3
0
10 Jun 2021
Counterexample-Guided Repair for Symbolic-Geometric Action Abstractions
Counterexample-Guided Repair for Symbolic-Geometric Action Abstractions
Wil Thomason
H. Kress-Gazit
21
3
0
13 May 2021
WFA-IRL: Inverse Reinforcement Learning of Autonomous Behaviors Encoded
  as Weighted Finite Automata
WFA-IRL: Inverse Reinforcement Learning of Autonomous Behaviors Encoded as Weighted Finite Automata
Tianyu Wang
Nikolay Atanasov
22
0
0
10 Mar 2021
Uncertainty-Aware Constraint Learning for Adaptive Safe Motion Planning
  from Demonstrations
Uncertainty-Aware Constraint Learning for Adaptive Safe Motion Planning from Demonstrations
Glen Chou
N. Ozay
Dmitry Berenson
32
15
0
09 Nov 2020
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