We show how this framework can be used to produce more robust plans as compared to flat models such as partially observable Markov decision processes (POMDPs).

Abdract- We propose and investigate a planning frame- work based on the Hierarchical Partially Observable Markov. Decision Process model (HPOMDP), ...

We propose and investigate a planning framework based on the hierarchical partially observable Markov decision process model (HPOMDP), and apply it to robot ...

Approximate planning with hierarchical partially observable Markov decision process models for robot navigation. Theocharous G., Mahadevan S.

We apply the algorithm to a large scale robot navigation task and demonstrate that with temporal abstraction we can consider an even smaller part of the belief.

Our main goal is to use hierarchical modeling as a basis for exploring more efficient learning and planning algorithms. As a case study we focus on indoor robot ...

We first review partially observable Markov decision processes (POMDPs) (Sect. 2.1), which is the framework used throughout the paper for planning in partially ...

Extending the MDP framework, partially observable Markov decision processes (POMDPs) allow for principled decision making under conditions of uncertain sensing.

Jul 31, 2007 · This paper proposes a new hierarchical formulation of POMDPs for autonomous robot navigation that can be solved in real-time, and is memory ...

Georgios Theocharous and Sridhar Mahadevan, "Approximate Planning with Hierarchical Partially Observable Markov Decision Processes for Robot Navigation ...