HIPer: A Human-Inspired Scene Perception Model for Multifunctional Mobile Robots
<|reference_start|>HIPer: A Human-Inspired Scene Perception Model for Multifunctional Mobile Robots: 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.<|reference_end|>
@article{graf2024hiper:,
    title={HIPer: A Human-Inspired Scene Perception Model for Multifunctional
  Mobile Robots},
    author={Florenz Graf, Jochen Lindermayr, Birgit Graf, Werner Kraus, and Marco
  F. Huber},
    journal={2024 IEEE Transactions on Robotics (T-RO)},
    year={2024},
    doi={10.1109/TRO.2024.3420799},
    number={IEEE T-RO 24-0146},
    archivePrefix={arXiv},
    eprint={2404.17791},
    primaryClass={cs.RO}
}