Learning user preferences from corrections on state lattices

Nils Wilde, Dana Kulic, Stephen L. Smith

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


Enabling a broader range of users to efficiently deploy autonomous mobile robots requires intuitive frameworks for specifying a robot's task and behaviour. We present a novel approach using learning from corrections (LfC), where a user is iteratively presented with a solution to a motion planning problem. Users might have preferences about parts of a robot's environment that are suitable for robot traffic or that should be avoided as well as preferences on the control actions a robot can take. The robot is initially unaware of these preferences; thus, we ask the user to provide a correction to the presented path. We assume that the user evaluates paths based on environment and motion features. From a sequence of corrections we learn weights for these features, which are then considered by the motion planner, resulting in future paths that better fit the user's preferences. We prove completeness of our algorithm and demonstrate its performance in simulations. Thereby, we show that the learned preferences yield good results not only for a set of training tasks but also for test tasks, as well as for different types of user behaviour.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Robotics and Automation, ICRA 2020
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages7
ISBN (Electronic)9781728173955
Publication statusPublished - 2020
EventIEEE International Conference on Robotics and Automation 2020 - Paris, France
Duration: 31 May 202031 Aug 2020

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729


ConferenceIEEE International Conference on Robotics and Automation 2020
Abbreviated titleICRA 2020
Internet address

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