Now or later? Predicting and Maximising Success of Navigation Actions from Long-Term Experience

Jaime Pulido Fentanes, Bruno Lacerda, Tomas Krajnik, Nick Hawes, Marc Hanheide

Research output: Chapter in Book/Report/Conference proceedingConference contribution

34 Citations (Scopus)


In planning for deliberation or navigation in real-world robotic systems, one of the big challenges is to cope with change. It lies in the nature of planning that it has to make assumptions about the future state of the world, and the robot's chances of successively accomplishing actions in this future. Hence, a robot's plan can only be as good as its predictions about the world. In this paper, we present a novel approach to specifically represent changes that stem from periodic events in the environment (e.g. a door being opened or closed), which impact on the success probability of planned actions. We show that our approach to model the probability of action success as a set of superimposed periodic processes allows the robot to predict action outcomes in a long-term data obtained in two real-life offices better than a static model. We furthermore discuss and showcase how this knowledge gathered can be successfully employed in a probabilistic planning framework to devise better navigation plans. The key contributions of this paper are (i) the formation of the spectral model of action outcomes from non-uniform sampling, the (ii) analysis of its predictive power using two long-term datasets, and (iii) the application of the predicted outcomes in an MDP-based planning framework.
Original languageEnglish
Title of host publicationInternational Conference on Robotics and Automation (ICRA)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Publication statusPublished - 2015
Event2015 IEEE International Conference on Robotics and Automation (ICRA) - Seattle, United Kingdom
Duration: 26 May 201530 May 2015


Conference2015 IEEE International Conference on Robotics and Automation (ICRA)
Country/TerritoryUnited Kingdom


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