Humanoid Occupancy: Enabling A Generalized Multimodal Occupancy Perception System on Humanoid Robots
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915416219058176 |
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| author | Cui, Wei Wang, Haoyu Qin, Wenkang Guo, Yijie Han, Gang Zhao, Wen Cao, Jiahang Zhang, Zhang Zhong, Jiaru Sun, Jingkai Sun, Pihai Shi, Shuai Jiang, Botuo Ma, Jiahao Wang, Jiaxu Cheng, Hao Liu, Zhichao Wang, Yang Zhu, Zheng Huang, Guan Tang, Jian Zhang, Qiang |
| author_facet | Cui, Wei Wang, Haoyu Qin, Wenkang Guo, Yijie Han, Gang Zhao, Wen Cao, Jiahang Zhang, Zhang Zhong, Jiaru Sun, Jingkai Sun, Pihai Shi, Shuai Jiang, Botuo Ma, Jiahao Wang, Jiaxu Cheng, Hao Liu, Zhichao Wang, Yang Zhu, Zheng Huang, Guan Tang, Jian Zhang, Qiang |
| contents | Humanoid robot technology is advancing rapidly, with manufacturers introducing diverse heterogeneous visual perception modules tailored to specific scenarios. Among various perception paradigms, occupancy-based representation has become widely recognized as particularly suitable for humanoid robots, as it provides both rich semantic and 3D geometric information essential for comprehensive environmental understanding. In this work, we present Humanoid Occupancy, a generalized multimodal occupancy perception system that integrates hardware and software components, data acquisition devices, and a dedicated annotation pipeline. Our framework employs advanced multi-modal fusion techniques to generate grid-based occupancy outputs encoding both occupancy status and semantic labels, thereby enabling holistic environmental understanding for downstream tasks such as task planning and navigation. To address the unique challenges of humanoid robots, we overcome issues such as kinematic interference and occlusion, and establish an effective sensor layout strategy. Furthermore, we have developed the first panoramic occupancy dataset specifically for humanoid robots, offering a valuable benchmark and resource for future research and development in this domain. The network architecture incorporates multi-modal feature fusion and temporal information integration to ensure robust perception. Overall, Humanoid Occupancy delivers effective environmental perception for humanoid robots and establishes a technical foundation for standardizing universal visual modules, paving the way for the widespread deployment of humanoid robots in complex real-world scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_20217 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Humanoid Occupancy: Enabling A Generalized Multimodal Occupancy Perception System on Humanoid Robots Cui, Wei Wang, Haoyu Qin, Wenkang Guo, Yijie Han, Gang Zhao, Wen Cao, Jiahang Zhang, Zhang Zhong, Jiaru Sun, Jingkai Sun, Pihai Shi, Shuai Jiang, Botuo Ma, Jiahao Wang, Jiaxu Cheng, Hao Liu, Zhichao Wang, Yang Zhu, Zheng Huang, Guan Tang, Jian Zhang, Qiang Robotics Artificial Intelligence Computer Vision and Pattern Recognition Humanoid robot technology is advancing rapidly, with manufacturers introducing diverse heterogeneous visual perception modules tailored to specific scenarios. Among various perception paradigms, occupancy-based representation has become widely recognized as particularly suitable for humanoid robots, as it provides both rich semantic and 3D geometric information essential for comprehensive environmental understanding. In this work, we present Humanoid Occupancy, a generalized multimodal occupancy perception system that integrates hardware and software components, data acquisition devices, and a dedicated annotation pipeline. Our framework employs advanced multi-modal fusion techniques to generate grid-based occupancy outputs encoding both occupancy status and semantic labels, thereby enabling holistic environmental understanding for downstream tasks such as task planning and navigation. To address the unique challenges of humanoid robots, we overcome issues such as kinematic interference and occlusion, and establish an effective sensor layout strategy. Furthermore, we have developed the first panoramic occupancy dataset specifically for humanoid robots, offering a valuable benchmark and resource for future research and development in this domain. The network architecture incorporates multi-modal feature fusion and temporal information integration to ensure robust perception. Overall, Humanoid Occupancy delivers effective environmental perception for humanoid robots and establishes a technical foundation for standardizing universal visual modules, paving the way for the widespread deployment of humanoid robots in complex real-world scenarios. |
| title | Humanoid Occupancy: Enabling A Generalized Multimodal Occupancy Perception System on Humanoid Robots |
| topic | Robotics Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.20217 |