Abstract
The deployment of robots at home must involve robots with pre-defined skills and the capability of personalizing their behavior by non-expert users. A framework to tackle this personalization is presented and applied to an automatic feeding task. The personalization involves the caregiver providing several examples of feeding using Learning-by-Demostration, and a ProMP formalism to compute an overall trajectory and the variance along the path. Experiments show the validity of the approach in generating different feeding motions to adapt to user’s preferences, automatically extracting the relevant task parameters. The importance of the nature of the demonstrations is also assessed, and two training strategies are compared.
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Notes
- 1.
A video showing the process of the personalized feeding task can be found at www.iri.upc.edu/groups/perception/frameworkFUTE.
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Acknowledgments
This work has been supported by the MINECO project RobInstruct TIN2014-58178-R and the ERA-Net CHIST-ERA project I-DRESS PCIN-2015-147. Gerard Canal is also supported by the Ministry of Economy and Knowledge of the Government of Catalonia via a FI-DGR 2016 fellowship.
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Canal, G., Alenyà, G., Torras, C. (2016). Personalization Framework for Adaptive Robotic Feeding Assistance. In: Agah, A., Cabibihan, JJ., Howard, A., Salichs, M., He, H. (eds) Social Robotics. ICSR 2016. Lecture Notes in Computer Science(), vol 9979. Springer, Cham. https://doi.org/10.1007/978-3-319-47437-3_3
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