Robotic manipulation of multiple objects as a POMDP

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openAccess
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2017
Major/Subject
Mcode
Degree programme
Language
en
Pages
16
213-228
Series
ARTIFICIAL INTELLIGENCE, Volume 247
Abstract
This paper investigates manipulation of multiple unknown objects in a crowded environment. Because of incomplete knowledge due to unknown objects and occlusions in visual observations, object observations are imperfect and action success is uncertain, making planning challenging. We model the problem as a partially observable Markov decision process (POMDP), which allows a general reward based optimization objective and takes uncertainty in temporal evolution and partial observations into account. In addition to occlusion dependent observation and action success probabilities, our POMDP model also automatically adapts object specific action success probabilities. To cope with the changing system dynamics and performance constraints, we present a new online POMDP method based on particle filtering that produces compact policies. The approach is validated both in simulation and in physical experiments in a scenario of moving dirty dishes into a dishwasher. The results indicate that: 1) a greedy heuristic manipulation approach is not sufficient, multi-object manipulation requires multi-step POMDP planning, and 2) on-line planning is beneficial since it allows the adaptation of the system dynamics model based on actual experience.
Description
Keywords
Cluttered environment, Manipulation, Multiple objects, Planning under uncertainty, POMDP, Task planning, Unknown objects
Other note
Citation
Pajarinen, J & Kyrki, V 2017, ' Robotic manipulation of multiple objects as a POMDP ', Artificial Intelligence, vol. 247, pp. 213-228 . https://doi.org/10.1016/j.artint.2015.04.001