Paper Info
Title
A Software Framework For Planning Under Partial Observability
Abstract
Planning under partial observability is both challenging and critical for reliable robot operation. The past decade has seen substantial advances in this domain: The mathematically principled approach for addressing such problems, namely the Partially Observable Markov Decision Process (POMDP), has started to become practical for various robotics tasks. Good approximate solutions for problems framed as POMDPs can now be computed on-line, with a few classes of problems being solved in near real-time. However, applications of these more recent advances are often hindered by the lack of easy-to-use software tools. Implementation of state of the art algorithms exist, but most (if not all) require the POMDP model to be hard-coded inside the program, increasing the difficulty of applying them. To alleviate this problem, we propose a software toolkit, called On-line POMDP Planning Toolkit (OPPT) (downloadable from http://robotics.itee.uq.edu.au/similar to oppt). By providing a well-defined and general abstract solver API, OPPT enables the user to quickly implement new POMDP solvers. Furthermore, OPPT provides an easy-to-use plug-in architecture with interfaces to the high-fidelity simulator Gazebo that, in conjunction with user-friendly configuration files, allows users to specify POMDP models of a standard class of robot motion planning under partial observability problems with no additional coding effort.
Year
DOI
Venue
2018
10.1109/IROS.2018.8593714
2018 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
Field
DocType
ISSN
Observability,Computer science,Partially observable Markov decision process,Control engineering,Coding (social sciences),Software,Artificial intelligence,Solver,Robot,Software framework,Robotics,Distributed computing
Conference
2153-0858
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Marcus Hörger121.79
Hanna Kurniawati252726.03
Alberto Elfes31470416.36