Abstract
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 language | English |
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Title of host publication | 2020 IEEE International Conference on Robotics and Automation, ICRA 2020 |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 4913-4919 |
Number of pages | 7 |
ISBN (Electronic) | 9781728173955 |
DOIs | |
Publication status | Published - 2020 |
Event | IEEE International Conference on Robotics and Automation 2020 - Paris, France Duration: 31 May 2020 → 31 Aug 2020 https://www.icra2020.org/ |
Publication series
Name | Proceedings - IEEE International Conference on Robotics and Automation |
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ISSN (Print) | 1050-4729 |
Conference
Conference | IEEE International Conference on Robotics and Automation 2020 |
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Abbreviated title | ICRA 2020 |
Country/Territory | France |
City | Paris |
Period | 31/05/20 → 31/08/20 |
Internet address |