THÖR-MAGNI Act: Actions for Human Motion Modeling in Robot-Shared Industrial Spaces
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arXiv
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| Format: | Preprint |
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2024
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| author | de Almeida, Tiago Rodrigues Schreiter, Tim Rudenko, Andrey Palmieiri, Luigi Stork, Johannes A. Lilienthal, Achim J. |
| author_facet | de Almeida, Tiago Rodrigues Schreiter, Tim Rudenko, Andrey Palmieiri, Luigi Stork, Johannes A. Lilienthal, Achim J. |
| contents | 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 THÖR-MAGNI Act dataset, a substantial extension of the THÖR-MAGNI dataset, which captures participant movements alongside robots in diverse semantic and spatial contexts. THÖR-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 THÖR-MAGNI motion cues, follow a long-tailed distribution with diversified acceleration, velocity, and navigation distance profiles. We demonstrate the utility of THÖR-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 THÖR-MAGNI Act to develop predictive models for enhanced human-robot interaction in complex environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_13729 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | THÖR-MAGNI Act: Actions for Human Motion Modeling in Robot-Shared Industrial Spaces de Almeida, Tiago Rodrigues Schreiter, Tim Rudenko, Andrey Palmieiri, Luigi Stork, Johannes A. Lilienthal, Achim J. Robotics Human-Computer Interaction Machine Learning 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 THÖR-MAGNI Act dataset, a substantial extension of the THÖR-MAGNI dataset, which captures participant movements alongside robots in diverse semantic and spatial contexts. THÖR-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 THÖR-MAGNI motion cues, follow a long-tailed distribution with diversified acceleration, velocity, and navigation distance profiles. We demonstrate the utility of THÖR-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 THÖR-MAGNI Act to develop predictive models for enhanced human-robot interaction in complex environments. |
| title | THÖR-MAGNI Act: Actions for Human Motion Modeling in Robot-Shared Industrial Spaces |
| topic | Robotics Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2412.13729 |