In this paper we introduce the concept of Adaptive Traversability (AT), which we define as means of autonomous motion control adapting the robot morphology-configuration of articulated parts and their compliance-to traverse unknown complex terrain with obstacles in an optimal way. We verify this concept by proposing a reinforcement learning based AT algorithm for mobile robots operating in such conditions. We demonstrate the functionality by training the AT algorithm under lab conditions on simple EUR-pallet obstacles and then testing it successfully on natural obstacles in a forest. For quantitative evaluation we define a metrics based on comparison with expert operator. Exploiting the proposed AT algorithm significantly decreases the cognitive load of the operator.

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This paper describes an affect-responsive interactive photo-frame application that offers its user a different experience with every use. It relies on visual analysis of activity levels and facial expressions of its users to select responses from a database of short video segments. This ever-growing database is automatically prepared by an offline analysis of user-uploaded videos. The resulting system matches its user's affect along dimensions of valence and arousal, and gradually adapts its response to each specific user. In an extended mode, two such systems are coupled and feed each other with visual content. The strengths and weaknesses of the system are assessed through a usability study, where a Wizard-of-Oz response logic is contrasted with the fully automatic system that uses affective and activity-based features, either alone, or in tandem.

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