HIPer: A Human-Inspired Scene Perception Model for Multifunctional Mobile Robots
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866929412491968512 |
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| author | Graf, Florenz Lindermayr, Jochen Graf, Birgit Kraus, Werner Huber, Marco F. |
| author_facet | Graf, Florenz Lindermayr, Jochen Graf, Birgit Kraus, Werner Huber, Marco F. |
| contents | Taking over arbitrary tasks like humans do with a mobile service robot in open-world settings requires a holistic scene perception for decision-making and high-level control. This paper presents a human-inspired scene perception model to minimize the gap between human and robotic capabilities. The approach takes over fundamental neuroscience concepts, such as a triplet perception split into recognition, knowledge representation, and knowledge interpretation. A recognition system splits the background and foreground to integrate exchangeable image-based object detectors and SLAM, a multi-layer knowledge base represents scene information in a hierarchical structure and offers interfaces for high-level control, and knowledge interpretation methods deploy spatio-temporal scene analysis and perceptual learning for self-adjustment. A single-setting ablation study is used to evaluate the impact of each component on the overall performance for a fetch-and-carry scenario in two simulated and one real-world environment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_17791 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | HIPer: A Human-Inspired Scene Perception Model for Multifunctional Mobile Robots Graf, Florenz Lindermayr, Jochen Graf, Birgit Kraus, Werner Huber, Marco F. Robotics Taking over arbitrary tasks like humans do with a mobile service robot in open-world settings requires a holistic scene perception for decision-making and high-level control. This paper presents a human-inspired scene perception model to minimize the gap between human and robotic capabilities. The approach takes over fundamental neuroscience concepts, such as a triplet perception split into recognition, knowledge representation, and knowledge interpretation. A recognition system splits the background and foreground to integrate exchangeable image-based object detectors and SLAM, a multi-layer knowledge base represents scene information in a hierarchical structure and offers interfaces for high-level control, and knowledge interpretation methods deploy spatio-temporal scene analysis and perceptual learning for self-adjustment. A single-setting ablation study is used to evaluate the impact of each component on the overall performance for a fetch-and-carry scenario in two simulated and one real-world environment. |
| title | HIPer: A Human-Inspired Scene Perception Model for Multifunctional Mobile Robots |
| topic | Robotics |
| url | https://arxiv.org/abs/2404.17791 |