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Abstract-This paper introduces the use of a flexible and affordable educational robot specifically developed for the practical experimentation inherent to technological disciplines. The robot has been designed to be reconfigurable and extendible, serving as an experimental platform across several undergraduate courses. As most students have a mobile cell phone, this was used as the main control computer for the so-called CellBot, thus avoiding any need to deal with the details of microcontrollers or other embedded computing devices. Assessment results are also presented, based on a pre-and post-survey of student opinion administered to 204 science and engineering students from several universities. Among the conclusions are that 83% of the students prefer to use these low-cost robots as tools to improve their learning of the theory in several disciplines, and 71% of the students stated that they prefer to have their own robot to experiment with, instead of using a didactic kit loaned to them by the university.

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Abstract-This article proposes a reinforcement learning approach to dynamically model the player skills in applications that integrate games and rehabilitation robotic. The approach aims to match the game difficulty to the player skills, keeping proper motivation (flow) during a rehabilitation process. The traditional rehabilitation process involves repetitive exercises. Robots and serious games provide new means to improve user motivation and commitment during treatment. Each person shows different skills when facing the challenges posed by computer games. Thus, the game difficulty level should be adjusted to each player skill level. The Q-Learning algorithm was adapted in this context to modify game parameters and to assess user skills based on a performance function. This function provides a path to an individual difficulty adjustment and consequently a tool to keep the user exercising. Experiments with thirty minutes duration are presented, involving four players, and the results obtained indicate the proposed approach is feasible for modeling the user behaviour getting to capture the adaptations and trends for each player according to the game difficulties.