Indoor service robots carry medication, lab samples, and supplies through hospital corridors and move goods through warehouse aisles. They share this space with people who are not trained operators and who are often not paying attention to the robot. Navigation policies for such robots are increasingly learned through reinforcement learning or imitation. When a learned policy meets a situation outside its training data, it still produces an action, and nothing in that action tells the robot or its operator that the policy may be wrong.
Below, you will find current topics that can be worked in the context of a , a Bachelor’s or a Master’s Thesis. The context indicates the scope of the work, and the keywords give you further information about the topic and its domain.
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This topic investigates unsupervised anomaly detection for merge conflict resolution in software engineering. Existing approaches generally formulate conflict resolution as supervised classification over predefined resolution labels. However, even a large labeled dataset may contain rare, unusual, or incorrectly reconstructed cases, and the labels may not adequately represent the full diversity of merge behavior. This project will develop methods to identify anomalous merge conflicts using the available conflict representations while initially ignoring the nine resolution labels. The objective is to determine whether anomaly scores can identify unusual conflict structures, rare resolution patterns, or cases that are difficult for conventional classifiers.
This topic investigates the quality and correctness of nine automatically assigned labels for merge-conflict resolutions. The labels were reconstructed by replaying historical Git merges and mapping the tentative conflict representation to the final developer-committed file. Such mappings are reproducible and scalable, but they can be affected by formatting changes, reordered code, structural edits, ambiguous correspondence between conflict chunks, and compound resolutions. The project will develop methods to detect potentially incorrect, ambiguous, or unstable labels and will validate a sample of these cases through systematic inspection. The work combines supervised, unsupervised, and weakly supervised methods for label-quality assessment.
Language models can generate plausible code that fails its tests. This thesis investigates whether internal attention patterns predict failure before generation finishes, allowing computation to shift toward more promising candidates. The student will build on the Prober repository to train and evaluate lightweight prediction models.
Language models can generate programming exercises and solutions, but incorrect answers, weak tests, and repetitive tasks can limit their value as training data. This thesis builds on CodifieRL to generate synthetic coding tasks from real repositories and investigate whether execution-based filtering improves their quality and usefulness for training code models.