Partially observable markov decision processes and robotics
H Kurniawati - Annual Review of Control, Robotics, and …, 2022 - annualreviews.org
Planning under uncertainty is critical to robotics. The partially observable Markov decision
process (POMDP) is a mathematical framework for such planning problems. POMDPs are …
process (POMDP) is a mathematical framework for such planning problems. POMDPs are …
Learning-Based Risk-Bounded Path Planning Under Environmental Uncertainty
Building a general and efficient path planning framework in uncertain nonconvex
environments is challenging due to the safety constraints and complex configuration …
environments is challenging due to the safety constraints and complex configuration …
Partially observable markov decision processes (pomdps) and robotics
H Kurniawati - arxiv preprint arxiv:2107.07599, 2021 - arxiv.org
Planning under uncertainty is critical to robotics. The Partially Observable Markov Decision
Process (POMDP) is a mathematical framework for such planning problems. It is powerful …
Process (POMDP) is a mathematical framework for such planning problems. It is powerful …
Value of Information and Reward Specification in Active Inference and POMDPs
R Wei - arxiv preprint arxiv:2408.06542, 2024 - arxiv.org
Expected free energy (EFE) is a central quantity in active inference which has recently
gained popularity due to its intuitive decomposition of the expected value of control into a …
gained popularity due to its intuitive decomposition of the expected value of control into a …
Online partial conditional plan synthesis for POMDPs with safe-reachability objectives: Methods and experiments
The framework of partially observable Markov decision processes (POMDPs) offers a
standard approach to model uncertainty in many robot tasks. Traditionally, POMDPs are …
standard approach to model uncertainty in many robot tasks. Traditionally, POMDPs are …
Recurrent Macro Actions Generator for POMDP Planning
Y Liang, H Kurniawati - 2023 IEEE/RSJ International …, 2023 - ieeexplore.ieee.org
Many planning problems in robotics require long planning horizon and uncertain in nature.
The Par-tially Observable Markov Descision Process (POMDP) is a mathematically …
The Par-tially Observable Markov Descision Process (POMDP) is a mathematically …
Scaling Long-Horizon Online POMDP Planning via Rapid State Space Sampling
Partially Observable Markov Decision Processes (POMDPs) are a general and principled
framework for motion planning under uncertainty. Despite tremendous improvement in the …
framework for motion planning under uncertainty. Despite tremendous improvement in the …
Simplified POMDP Planning with an Alternative Observation Space and Formal Performance Guarantees
Online planning under uncertainty in partially observable domains is an essential capability
in robotics and AI. The partially observable Markov decision process (POMDP) is a …
in robotics and AI. The partially observable Markov decision process (POMDP) is a …
Models as a Key Factor of Environments Design in Multi-Agent Reinforcement Learning
KA Morozov - 2024 6th International Youth Conference on …, 2024 - ieeexplore.ieee.org
This paper presents a generalized graphical outline of the current state of the art in the
development of Markov decision processes (MDPs). This systematization opens ways to …
development of Markov decision processes (MDPs). This systematization opens ways to …
Robotic planning under uncertainty in spatiotemporal environments in expeditionary science
In the expeditionary sciences, spatiotemporally varying environments--hydrothermal plumes,
algal blooms, lava flows, or animal migrations--are ubiquitous. Mobile robots are uniquely …
algal blooms, lava flows, or animal migrations--are ubiquitous. Mobile robots are uniquely …