Implementation Of Feeding Task Via Learning From Demonstration

Keywords

Activities of daily living tasks (ADLs); Feeding task; Gaussian mixture model (GMM); Human robot interaction (HRI); Learning by imitation; Learning from demonstration (LFD); Meal assistant; Probabilistic motion encoding; Programming by demonstration (PBD)

Abstract

In this paper, a Learning From Demonstration (LFD) approach is used to design an autonomous meal-assistant agent. The feeding task is modeled as a mixture of Gaussian distributions. Using the data collected via kinesthetic teaching, the parameters of Gaussian Mixture Model (GMM) are learned using Gaussian Mixture Regression (GMR) and Expectation Maximization (EM) algorithm. Reproduction of feeding trajectories for different environments is obtained by solving a constrained optimization problem. In this method we show that obstacles can be avoided by robot's end-effector by adding a set of extra constraints to the optimization problem. Finally, the performance of the designed meal assistant is evaluated in two feeding scenario experiments: one considering obstacles in the path between the bowl and the mouth and the other without.

Publication Date

4-2-2018

Publication Title

Proceedings - 2nd IEEE International Conference on Robotic Computing, IRC 2018

Volume

2018-January

Number of Pages

274-277

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/IRC.2018.00058

Socpus ID

85049608147 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85049608147

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