Abstract
Assistive robots can improve the well-being of disabled or frail human users by reducing the burden that activities of daily living impose on them. To enable personalised assistance, such robots benefit from building a user-specific model, so that the assistance is customised to the particular set of user abilities. In this paper, we present an end-to-end approach for home-environment assistive humanoid robots to provide personalised assistance through a dressing application for users who have upper-body movement limitations. We use randomised decision forests to estimate the upper-body pose of users captured by a top-view depth camera, and model the movement space of upper-body joints using Gaussian mixture models. The movement space of each upper-body joint consists of regions with different reaching capabilities. We propose a method which is based on real-time upper-body pose and user models to plan robot motions for assistive dressing. We validate each part of our approach and test the whole system, allowing a Baxter humanoid robot to assist human to wear a sleeveless jacket.
Original language | English |
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Title of host publication | Proceedings of 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
Publisher | IEEE Computer Society |
Pages | 1840-1845 |
ISBN (Electronic) | 978-1-4799-9994-1 |
DOIs | |
Publication status | Published - 28 Sept 2015 |
Event | 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) - Hamburg, Germany Duration: 28 Sept 2015 → 2 Oct 2015 |
Conference
Conference | 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
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Country/Territory | Germany |
City | Hamburg |
Period | 28/09/15 → 2/10/15 |