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Accurately simulating these tasks is also difficult. Hence, it is crucial for robust task performance to learn how to coordinate end-effector pose and applied force, monitor execution, and react to deviations. To address these challenges, we propose a learning approach that directly infers both low- and high-level task representations from user demonstrations on the real system. We developed an unsupervised task segmentation algorithm that combines intention recognition and feature clustering to infer the skills of a task. We leverage the inferred characteristic features of each skill in a novel unsupervised anomaly detection approach to identify deviations from the intended task execution. Together, these components form a comprehensive framework capable of incrementally learning task decisions and new behaviors as new situations arise. Compared to state-of-the-art learning techniques, our approach significantly reduces the required amount of training data and computational complexity while efficiently learning complex in-contact behaviors and recovery strategies. Our proposed task segmentation and anomaly detection approaches outperform state-of-the-art methods on force-based tasks evaluated on two different robotic systems.<\/jats:p>","DOI":"10.1177\/02783649251352112","type":"journal-article","created":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T06:41:05Z","timestamp":1751956865000},"page":"369-396","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Hierarchical task decomposition for execution monitoring and error recovery: Understanding the rationale behind task demonstrations"],"prefix":"10.1177","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3579-4130","authenticated-orcid":false,"given":"Christoph","family":"Willibald","sequence":"first","affiliation":[{"name":"German Aerospace Center (DLR)"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1897-7664","authenticated-orcid":false,"given":"Dongheui","family":"Lee","sequence":"additional","affiliation":[{"name":"German Aerospace Center (DLR)"},{"name":"Technische Universit\u00e4t Wien (TU Wien)"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,7,8]]},"reference":[{"key":"e_1_3_5_2_1","article-title":"Bayesian online changepoint detection","author":"Adams RP","year":"2007","unstructured":"Adams RP, MacKay DJ (2007) Bayesian online changepoint detection. arXiv preprint arXiv:0710.3742.","journal-title":"arXiv preprint arXiv:0710.3742"},{"key":"e_1_3_5_3_1","article-title":"Unpacking failure modes of generative policies: runtime monitoring of consistency and progress","author":"Agia C","year":"2024","unstructured":"Agia C, Sinha R, Yang J, et al. 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