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Auflistung nach Autor:in "Nguyen, Sao Mai"

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  • Zeitschriftenartikel
    Plug and Play your Robot into your Smart Home: Illustration of a New Framework
    (KI - Künstliche Intelligenz: Vol. 31, No. 3, 2017) Nguyen, Sao Mai; Lohr, Christophe; Tanguy, Philippe; Chen, Yiqiao
    We present our team IHSEV, and our preliminary studies to tackle the question of interoperability of devices and robots for smart homes. We propose a framework enabling the seamless communication between smart home devices and robots. Our framework relies primarily on the xAAL protocol, which allows any device from any type to be plugged into a smart house network. We have recently extended xAAL to allow any ROS-compatible robot to be integrated into the smart house network. We illustrate the relevance of this framework in an implemented use case: assistance of an elderly person in the case of a fall (Fig. 1).
  • Zeitschriftenartikel
    Robots Learn Increasingly Complex Tasks with Intrinsic Motivation and Automatic Curriculum Learning
    (KI - Künstliche Intelligenz: Vol. 35, No. 1, 2021) Nguyen, Sao Mai; Duminy, Nicolas; Manoury, Alexandre; Duhaut, Dominique; Buche, Cedric
    Multi-task learning by robots poses the challenge of the domain knowledge: complexity of tasks, complexity of the actions required, relationship between tasks for transfer learning. We demonstrate that this domain knowledge can be learned to address the challenges in life-long learning. Specifically, the hierarchy between tasks of various complexities is key to infer a curriculum from simple to composite tasks. We propose a framework for robots to learn sequences of actions of unbounded complexity in order to achieve multiple control tasks of various complexity. Our hierarchical reinforcement learning framework, named SGIM-SAHT, offers a new direction of research, and tries to unify partial implementations on robot arms and mobile robots. We outline our contributions to enable robots to map multiple control tasks to sequences of actions: representations of task dependencies, an intrinsically motivated exploration to learn task hierarchies, and active imitation learning. While learning the hierarchy of tasks, it infers its curriculum by deciding which tasks to explore first, how to transfer knowledge, and when, how and whom to imitate.