{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/thor-magni-act-actions-for-human-motion","title":"THÖR-MAGNI Act: Actions for Human Motion Modeling in Robot-Shared Industrial Spaces","arxiv_id":"2412.13729","date":"2024-12-18","proceeding":null,"authors":["Tiago Rodrigues de Almeida","Tim Schreiter","Andrey Rudenko","Luigi Palmieiri","Johannes A. Stork","Achim J. Lilienthal"],"abstract":"Accurate human activity and trajectory prediction are crucial for ensuring safe and reliable human-robot interactions in dynamic environments, such as industrial settings, with mobile robots. Datasets with fine-grained action labels for moving people in industrial environments with mobile robots are scarce, as most existing datasets focus on social navigation in public spaces. This paper introduces the TH\\\"OR-MAGNI Act dataset, a substantial extension of the TH\\\"OR-MAGNI dataset, which captures participant movements alongside robots in diverse semantic and spatial contexts. TH\\\"OR-MAGNI Act provides 8.3 hours of manually labeled participant actions derived from egocentric videos recorded via eye-tracking glasses. These actions, aligned with the provided TH\\\"OR-MAGNI motion cues, follow a long-tailed distribution with diversified acceleration, velocity, and navigation distance profiles. We demonstrate the utility of TH\\\"OR-MAGNI Act for two tasks: action-conditioned trajectory prediction and joint action and trajectory prediction. We propose two efficient transformer-based models that outperform the baselines to address these tasks. These results underscore the potential of TH\\\"OR-MAGNI Act to develop predictive models for enhanced human-robot interaction in complex environments.","url_abs":"https://arxiv.org/abs/2412.13729v2","url_pdf":"https://arxiv.org/pdf/2412.13729v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"thor-magni-act-actions-for-human-motion","repo_url":"https://github.com/tmralmeida/thor-magni-actions","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"thor-magni-act-actions-for-human-motion","repo_url":"https://github.com/tmralmeida/thor-magni-tools","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"social-navigation","task_name":"Social Navigation"},{"task_slug":"trajectory-prediction","task_name":"Trajectory Prediction"}],"methods":[{"method_slug":"focus","method_name":"Focus"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}