Programming robots for performing different activities requires calculating sequences of values of their joints by taking into account many factors, such as stability and efficiency, at the same time. Particularly for walking, state of the art techniques to approximate these sequences are based on reinforcement learning (RL). In this work we propose a multi-level system, where the same RL method is used first to learn the configuration of robot joints (poses) that allow it to stand with stability, and then in the second level, we find the sequence of poses that let it reach the furthest distance in the shortest time, while avoiding falling down and keeping a straight path. In order to evaluate this, we focus on measuring the time it takes for the robot to travel a certain distance. To our knowledge, this is the first work focusing both on speed and precision of the trajectory at the same time. We implement our model in a simulated environment using q-learning. We compare with the built-in walking modes of an NAO robot by improving normal-speed and enhancing robustness in fast-speed. The proposed model can be extended to other tasks and is independent of a particular robot model.

The retrofitting of less energy efficient building stock represents one of the most significant challenges in the transition to a low-carbon economy. Nowadays, the housing sector represents about 40% of the energy consumption in the European Union. In this regard, the level of insulation installed in buildings is directly related to the energy efficiency of the building, and consequently to the urban area. In addition, several studies have shown that a comprehensive perspective of energy efficiency is needed, together with calculating the importance of introducing Life Cycle Assessment (LCA) methodology. The purpose of this study is to develop a methodology to: first, measure the energy efficiency level of specific urban areas and their buildings using a geospatial model in an integral perspective; and second, the environmental impact caused by the refurbishment of these building façades using a LCA method.On the one hand, according to a bottom-up framework the quantitative and qualitative characterisation of the building stock façade at the urban scale is possible generating a georeferenced spatial data model of buildings using Geographic Information Systems. On the other hand, the environmental impact of the most usual constructive solutions to refurbishment building façades is calculated using the LCA methodology. The results obtained are merged and interpolated to the urban scale. The methodology is tested for the case study of blocks of flats in Barcelona using the open data of building stock from the Spanish Government. Firstly, this methodology provides more information in regard to urban areas as well as calculating their energy efficiency. Secondly, the study measures the renovation impact of the less efficient buildings. Finally, the results provide the basis for supporting decisions on building stock retrofitting for urban scale from a new approach, especially making the selection between various renovation scenarios much clearer.

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