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Probabilities Are Not Enough: Formal Controller Synthesis for Stochastic
  Dynamical Models with Epistemic Uncertainty
v1v2 (latest)

Probabilities Are Not Enough: Formal Controller Synthesis for Stochastic Dynamical Models with Epistemic Uncertainty

12 October 2022
Thom S. Badings
Licio Romao
Alessandro Abate
N. Jansen
ArXiv (abs)PDFHTML

Papers citing "Probabilities Are Not Enough: Formal Controller Synthesis for Stochastic Dynamical Models with Epistemic Uncertainty"

14 / 14 papers shown
Title
Learning Control Policies for Stochastic Systems with Reach-avoid
  Guarantees
Learning Control Policies for Stochastic Systems with Reach-avoid Guarantees
Dorde Zikelic
Mathias Lechner
T. Henzinger
K. Chatterjee
67
26
0
11 Oct 2022
Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement
  Learning
Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning
Bertrand Charpentier
Ransalu Senanayake
Mykel Kochenderfer
Stephan Günnemann
PERUD
80
26
0
03 Jun 2022
Data-driven Abstractions with Probabilistic Guarantees for Linear PETC
  Systems
Data-driven Abstractions with Probabilistic Guarantees for Linear PETC Systems
Andrea Peruffo
M. Mazo
55
13
0
10 Mar 2022
Sampling-Based Robust Control of Autonomous Systems with Non-Gaussian
  Noise
Sampling-Based Robust Control of Autonomous Systems with Non-Gaussian Noise
Heinke Hihn
Alessandro Abate
Nils Jansen
David Parker
Hasan A. Poonawala
Marielle Stoelinga
54
27
0
25 Oct 2021
Safe Learning in Robotics: From Learning-Based Control to Safe
  Reinforcement Learning
Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning
Lukas Brunke
Melissa Greeff
Adam W. Hall
Zhaocong Yuan
Siqi Zhou
Jacopo Panerati
Angela P. Schoellig
OffRL
63
632
0
13 Aug 2021
Model Error Propagation via Learned Contraction Metrics for Safe
  Feedback Motion Planning of Unknown Systems
Model Error Propagation via Learned Contraction Metrics for Safe Feedback Motion Planning of Unknown Systems
Glen Chou
N. Ozay
Dmitry Berenson
93
25
0
18 Apr 2021
Planning with Learned Dynamics: Probabilistic Guarantees on Safety and
  Reachability via Lipschitz Constants
Planning with Learned Dynamics: Probabilistic Guarantees on Safety and Reachability via Lipschitz Constants
Craig Knuth
Glen Chou
N. Ozay
Dmitry Berenson
70
34
0
18 Oct 2020
Safe Optimal Control Using Stochastic Barrier Functions and Deep
  Forward-Backward SDEs
Safe Optimal Control Using Stochastic Barrier Functions and Deep Forward-Backward SDEs
M. Pereira
Ziyi Wang
Ioannis Exarchos
Evangelos A. Theodorou
53
38
0
02 Sep 2020
Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction
  to Concepts and Methods
Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods
Eyke Hüllermeier
Willem Waegeman
PERUD
249
1,427
0
21 Oct 2019
A General Framework for Uncertainty Estimation in Deep Learning
A General Framework for Uncertainty Estimation in Deep Learning
Antonio Loquercio
Mattia Segu
Davide Scaramuzza
UQCVBDLOOD
68
293
0
16 Jul 2019
Finite Sample Analysis of Stochastic System Identification
Finite Sample Analysis of Stochastic System Identification
Anastasios Tsiamis
George J. Pappas
51
150
0
21 Mar 2019
Safe Model-based Reinforcement Learning with Stability Guarantees
Safe Model-based Reinforcement Learning with Stability Guarantees
Felix Berkenkamp
M. Turchetta
Angela P. Schoellig
Andreas Krause
191
853
0
23 May 2017
Funnel Libraries for Real-Time Robust Feedback Motion Planning
Funnel Libraries for Real-Time Robust Feedback Motion Planning
Anirudha Majumdar
Russ Tedrake
77
368
0
15 Jan 2016
Risk-Sensitive Reinforcement Learning Applied to Control under
  Constraints
Risk-Sensitive Reinforcement Learning Applied to Control under Constraints
Peter Geibel
F. Wysotzki
84
319
0
09 Sep 2011
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