While these Multi-Agent POMDPs model human actions and the robot's observation of those actions, it does now allow the robot to explicitly query the human for help. Additionally, modeling the human and robot jointly requires exponentially larger state spaces which are less tractable to solve.
In this work, we introduce a Human Observation Provider POMDP framework (HOP-POMDP), and contribute new algorithms for planning and executing with HOP-POMDPs ...
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In this paper, we will present how partially observable. Markov decision process (POMDP) can be used as a unified framework for the three main components in a ...
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We need a general POMDP solver that can solve domains large in both state space and observation space and can generate actions real time for robotics ...
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In this paper, a modelling approach is described that represents human-robot social interactions as partially observable Markov decision processes (POMDPs). In ...
Aug 2, 2021 · Partially observable Markov decision processes (POMDPs) are a convenient mathematical model to solve sequential decision-making problems ...
This paper describes an approach for creating the POMDP model and demonstrates its working by simulating it on two mobile robots destined on a collision course.
Modeling humans as observation providers using POMDPs · Robot Planning with Mathematical Models of Human State and Action · A Survey of Nonverbal Signaling ...
We survey recent approaches for using decision-theoretic models in information-gathering scenarios, highlighting common practices and existing generic models.