Efficiently exploring for human robot interaction: partially observable Poisson processes

Journal article

Abstract:: Consider a mobile robot exploring an office building with the aim of observing as much human activity as possible over several days. It must learn where and when people are to be found, count the observed activities, and revisit popular places at the right time. In this paper we present a series of Bayesian estimators for the levels of human activity that improve on simple counting. We then show how these estimators can be used to drive efficient exploration for human activities. The estimators arise from modelling the human activity counts as a partially observable Poisson process (POPP). This paper presents novel extensions to POPP for the following cases: (i) the robot’s sensors are correlated, (ii) the robot’s sensor model, itself built from data, is also unreliable, (iii) both are combined. It also combines the resulting Bayesian estimators with a simple, but effective solution to the exploration-exploitation trade-off faced by the robot in a real deployment. A series of 15 day robot deployments show how our approach boosts the number of human activities observed by 70% relative to a baseline and produces more accurate estimates of the level of human activity in each place and time.

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Language:: English
Keywords:: FFR

human robot interaction

exploration-exploitation

Bayesian estimators

Poisson processes
Pubs id:: 1305056
Local pid:: pubs:1305056
Deposit date:: 2022-12-19

Copyright holder:: Jovan et al.
Rights statement:: Copyright © 2022, The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Notes:: A correction to this article is available online from Springer at: https://doi.org/10.1007/s10514-023-10152-2

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