Knowledge about human intention can be beneficial in many disciplines of robotics, such as collaborative manufacturing, prosthetics, or encountered-type haptics. Existing intention estimation approaches are either traditional and rely on handcrafted features and heuristics, or learning-based and tailored to very specific conditions. This paper attempts to combine the best of both worlds by making recurrent neural networks adaptable to different scenarios. To achieve this, the intention estimation problem is formulated as a probabilistic classification problem and two new data sets with real-world motion and eye-tracking data are presented. Based on this data, three real-time capable classifiers with different features regarding situational awareness and additional outputs are designed and evaluated against two competing approaches. The results show that two out of three classifiers lead to improved or equivalent performance compared to traditional approaches, while good generalization is maintained.