Work

Learning Geometrically-Constrained Hidden Markov Models for Robot Navigation: Bridging the Topological-Geometrical Gap

Year: 2002

Type: article

Abstract: Hidden Markov models (HMMs) and partially observable Markov decision processes (POMDPs) provide useful tools for modeling dynamical systems. They are particularly useful for representing the topology of environments such as road networks and office buildings, which are typical for robot navigation and planning. The work presented here describes a formal framework for incorporating readily available odometric information and geometrical constraints into both the models and the algorithm that learns them. By taking advantage of such information, learning HMMs/POMDPs can be made to generate better solutions and require fewer iterations, while being robust in the face of data reduction. Experimental results, obtained from both simulated and real robot data, demonstrate the effectiveness of the approach.

Source: Journal of Artificial Intelligence Research

DOI: 10.1613/jair.874

Authors: H. Shatkay, L. P. Kaelbling

Institution: Massachusetts Institute of Technology

Language: English

Topic: Robotics and Sensor-Based Localization

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: diamond

Funders: National Science Foundation, Brown University, Advanced Research Projects Agency

Awards: Presidential Faculty Fellows: Learning for Autonomous Agents, Robot Planning, Learning and Selective Perception in Stochastic Domains