Object-based world modeling in semi-static environments with dependent dirichlet process mixtures
Name
wong-ijcai16.pdf
Description
Accepted version
Size
3.13 MB
Format
Adobe PDF
Checksum (MD5)
dc20b2078f73532d9d0d657c93d45be6
Author(s)
Kaelbling, Leslie P.
Lozano-Pérez, Tomás
Kurutach, Thanard
Date Issued
2016
Citation
Kaelbling, Leslie P., Lozano-Pérez, Tomás and Kurutach, Thanard. 2016. "Object-based world modeling in semi-static environments with dependent dirichlet process mixtures."
Version
Author's final manuscript
Abstract
To accomplish tasks in human-centric indoor environments, agents need to represent and understand the world in terms of objects and their attributes. We consider how to acquire such a world model via noisy perception and maintain it over time, as objects are added, changed, and removed in the world. Previous work framed this as multiple-target tracking problem, where objects are potentially in motion at all times. Although this approach is general, it is computationally expensive. We argue that such generality is not needed in typical world modeling tasks, where objects only change state occasionally. More efficient approaches are enabled by restricting ourselves to such semi-static environments. We consider a previously-proposed clusteringbased world modeling approach that assumed static environments, and extend it to semi-static domains by applying a dependent Dirichlet process (DDP) mixture model. We derive a novel MAP inference algorithm under this model, subject to data association constraints. We demonstrate our approach improves computational performance for world modeling in semi-static environments.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://dl.acm.org/citation.cfm?id=3061112
