International audienceAmbient assisted living systems are based on sensors and actuators, with a diversity of network protocols and vendors. This commonly leads to the introduction of gateways or middlewares into the technical infrastructure in order to address interoperability issues. The xAAL framework presented in this paper aims to provide interoperability and to redesign such "gateways" into well-defined functional entities communicating with each other via a lightweight message bus over IP. Each entity may have multiple instances, may be shared between several boxes, and may be physically located in any box. Thanks to the distributed architecture of the system, each home automation vendor may peacefully provide its own xAAL box without revealing details of its technology. Also, several applications may be plugged together on the xAAL bus without getting bored with underlying heterogeneity. Moreover, the management of the dynamicity allows sensors or applications to enter and leave the system at any time

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Autonomous control of reconfigurable robots is crucial for their deployment in diverse environments. The development of such skills is however hampered by the diversity in hardware and task constraints. We advocate the use of artificial intelligence-based approaches to improve scalability to different conditions and portability to platforms of comparable traversability skills. In particular, we succeed in tackling the problem of staircase traversal via a reinforcement learning-based control framework applicable to different articulated tracked robots, powerful enough to generalize to varying conditions learnt in simulation and to transfer to reality in a zero-shot setting. Our extensive experiments demonstrate the robustness of the framework in learning tasks with increased risk and difficulty induced by platform diversification and increased control dimensionality.

In open-ended continuous environments, robots need to learn multiple parameterised control tasks in hierarchical reinforcement learning. We hypothesise that the most complex tasks can be learned more easily by transferring knowledge from simpler tasks, and faster by adapting the complexity of the actions to the task. We propose a task-oriented representation of complex actions, called procedures, to learn online task relationships and unbounded sequences of action primitives to control the different observables of the environment. Combining both goal-babbling with imitation learning, and active learning with transfer of knowledge based on intrinsic motivation, our algorithm self-organises its learning process. It chooses at any given time a task to focus on; and what, how, when and from whom to transfer knowledge. We show with a simulation and a real industrial robot arm, in cross-task and cross-learner transfer settings, that task composition is key to tackle highly complex tasks. Task decomposition is also efficiently transferred across different embodied learners and by active imitation, where the robot requests just a small amount of demonstrations and the adequate type of information. The robot learns and exploits task dependencies so as to learn tasks of every complexity.