{"id":32289,"date":"2026-01-21T17:01:38","date_gmt":"2026-01-21T17:01:38","guid":{"rendered":"https:\/\/lamarr-institute.org\/publication\/a-large-scale-dataset-for-humanoid-robotics-enabling-a-novel-data-driven-fall-prediction\/"},"modified":"2026-06-08T13:19:27","modified_gmt":"2026-06-08T13:19:27","slug":"a-large-scale-dataset-for-humanoid-robotics-enabling-a-novel-data-driven-fall-prediction","status":"publish","type":"publication","link":"https:\/\/lamarr-institute.org\/de\/publication\/a-large-scale-dataset-for-humanoid-robotics-enabling-a-novel-data-driven-fall-prediction\/","title":{"rendered":"A Large-Scale Dataset for Humanoid Robotics Enabling a Novel Data-Driven Fall Prediction"},"content":{"rendered":"<p>In this paper, we present a comprehensive dataset comprising 37.9 hours of sensor data collected from humanoid robots, including 18.3 hours of walking and 2,519 recorded falls. This extensive dataset is a valuable resource for various robotics and machine learning applications. Leveraging this data, we propose {RePro}-{TCN}, a Temporal Convolutional Network ({TCN}) enhanced with two novel extensions: Relaxed Loss Formulation and Progressive Forecasting. Predicting falls is a critical capability in humanoid robotics for implementing countermeasures such as lunging or stopping the walk. Thanks to the new dataset, we train {RePro}-{TCN} and demonstrate its superiority over previous approaches under real-world conditions that were previously unattainable.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper, we present a comprehensive dataset comprising 37.9 hours of sensor data collected from humanoid robots, including 18.3 hours of walking and 2,519 recorded falls. This extensive dataset is a valuable resource for various robotics and machine learning applications. Leveraging this data, we propose {RePro}-{TCN}, a Temporal Convolutional Network ({TCN}) enhanced with two novel extensions: Relaxed Loss Formulation and Progressive Forecasting. Predicting falls is a critical capability in [&hellip;]<\/p>\n","protected":false},"author":12,"featured_media":0,"template":"","meta":{"_acf_changed":false,"footnotes":""},"publication-type":[32],"class_list":["post-32289","publication","type-publication","status-publish","hentry","publication-type-inproceedings"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32289","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication"}],"about":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/types\/publication"}],"author":[{"embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/users\/12"}],"version-history":[{"count":0,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32289\/revisions"}],"wp:attachment":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/media?parent=32289"}],"wp:term":[{"taxonomy":"publication-type","embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication-type?post=32289"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}