Planning under uncertainty is critical to robotics. The partially observable Markov decision
process (POMDP) is a mathematical framework for such planning problems. POMDPs are …

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

Intelligent transport systems, efficient electric grids, and sensor networks for data collection
and analysis are some examples of the multiagent systems (MAS) that cooperate to achieve …

The partially observable Markov decision process (POMDP) provides a principled general
framework for planning under uncertainty, but solving POMDPs optimally is computationally …

We present a new approach to motion planning under sensing and motion uncertainty by
computing a locally optimal solution to a continuous partially observable Markov decision …

In this paper we present feedback-based information roadmap (FIRM), a multi-query
approach for planning under uncertainty which is a belief-space variant of probabilistic …

As robots venture into new application domains as autonomous vehicles on the road or as
domestic helpers at home, they must recognize human intentions and behaviors in order to …

POMDPs. jl is an open-source framework for solving Markov decision processes (MDPs)
and partially observable MDPs (POMDPs). POMDPs. jl allows users to specify sequential …

Motion planning under uncertainty is important for reliable robot operations in uncertain and
dynamic environments. Partially Observable Markov Decision Process (POMDP) is a …

This paper introduces the QMDP-net, a neural network architecture for planning under
partial observability. The QMDP-net combines the strengths of model-free learning and …