Abstract
In this paper we propose the creation of context-aware middleware to solve the challenge of integrating disparate incompatible systems involved in the teaching of human action skills to robots. Context-aware middleware provides the solution to retrofitting capabilities onto existing robots (agents) and bridges the technology differences between systems. The experimental results demonstrate a framework for handling situational and contextual data for robot Learning from Demonstration.
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Acknowledgments
The authors would like to acknowledge support from DREAM project of EU FP7-ICT (grant no. 611391), Research Project of State Key Laboratory of Mechanical System and Vibration China (grant no. MSV201508), and National Natural Science Foundation of China (grant no. 51575412).
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Phiri, C.C., Ju, Z., Liu, H. (2016). Accelerating Humanoid Robot Learning from Human Action Skills Using Context-Aware Middleware. In: Kubota, N., Kiguchi, K., Liu, H., Obo, T. (eds) Intelligent Robotics and Applications. ICIRA 2016. Lecture Notes in Computer Science(), vol 9834. Springer, Cham. https://doi.org/10.1007/978-3-319-43506-0_49
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DOI: https://doi.org/10.1007/978-3-319-43506-0_49
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