IEEE

Partially Observable Markov Decision Processes in Robotics: A Survey

Abstract: Noisy sensing, imperfect control, and environment changes are defining characteristics of many real-world robot tasks. The partially observable Markov decision process (POMDP) provides a ...
IEEE

POMDP-lite for robust robot planning under uncertainty

Abstract: The partially observable Markov decision process (POMDP) provides a principled general model for planning under uncertainty. However, solving a general POMDP is computationally intractable ...
Robohub

Learning robust controllers that work across many partially observable environments

In intelligent systems, applications range from autonomous robotics to predictive maintenance problems. To control these systems, the essential aspects are captured with a model. When we design ...
GitHub

Deep Active Inference for Partially Observable MDPs

This repository contains the code and results for the algorithms published in: van der Himst O., Lanillos P. (2020) Deep Active Inference for Partially Observable MDPs. In: Verbelen T., Lanillos P., ...
GitHub

TRU-POMDP: Task Planning Under Uncertainty via Tree of Hypotheses and Open-Ended POMDPs

Customer stories Events & webinars Ebooks & reports Business insights GitHub Skills ...
eLife

Individual differences in tail risk sensitive exploration using Bayes-adaptive Markov decision processes

Shen et al. present a computational account of individual differences in mouse exploration when faced with a novel object in an open field from a previously published study (Akiti et al.) that relates ...
Microsoft

Project Frigatebird: AI for Autonomous Soaring

Techniques for automatic decision making under uncertainty have been making great strides in their ability to learn complex policies from streams of observations. However, this progress is happening ...