# HIPer: A Human-Inspired Scene Perception Model for Multifunctional Mobile Robots

arXiv: https://arxiv.org/abs/2404.17791 · HTML: https://mlsanity.com/paper/2404.17791/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2024.

## Built on by (most cited first, 1 of 1)

- [Multi-Agent LLM Actor-Critic Framework for Social Robot Navigation](https://mlsanity.com/paper/2503.09758/) (2025)

## Abstract (arXiv)

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.