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Over the years, several proprioceptive terrain classification techniques have been introduced to increase robustness or act as a fallback for traditional vision based approaches. However, they lack widespread adaptation due to various factors that include inadequate accuracy, robustness and slow run-times. In this paper, we use vehicle-terrain interaction sounds as a proprioceptive modality and propose a deep long-short term memory based recurrent model that captures both the spatial and temporal dynamics of such a problem, thereby overcoming these past limitations. Our model consists of a new convolution neural network architecture that learns deep spatial features, complemented with long-short term memory units that learn complex temporal dynamics. Experiments on two extensive datasets collected with different microphones on various indoor and outdoor terrains demonstrate state-of-the-art performance compared to existing techniques. We additionally evaluate the performance in adverse acoustic conditions with high-ambient noise and propose a noise-aware training scheme that enables learning of more generalizable models that are essential for robust real-world deployments. <\/jats:p>","DOI":"10.1177\/0278364917727062","type":"journal-article","created":{"date-parts":[[2017,8,31]],"date-time":"2017-08-31T11:29:45Z","timestamp":1504178985000},"page":"1521-1539","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":56,"title":["Deep spatiotemporal models for robust proprioceptive terrain classification"],"prefix":"10.1177","volume":"36","author":[{"given":"Abhinav","family":"Valada","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Freiburg, Germany"}]},{"given":"Wolfram","family":"Burgard","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Freiburg, Germany"}]}],"member":"179","published-online":{"date-parts":[[2017,8,31]]},"reference":[{"key":"bibr1-0278364917727062","volume-title":"Australasian conference on robotics and automation","author":"Best G","year":"2013"},{"key":"bibr2-0278364917727062","unstructured":"Boersma P, Weenink D (2013) Praat: Doing phonetics by computer [computer program]. 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