Humanoid Occupancy：在人形机器人上实现通用化多模态占据感知系统
Humanoid Occupancy: Enabling A Generalized Multimodal Occupancy Perception System on Humanoid Robots
AI总结：
本研究提出人形机器人通用多模态占据感知系统Humanoid Occupancy，整合软硬件与标注流水线，解决运动干扰等问题，构建首个专用全景占据数据集，为复杂场景部署奠定基础。
摘要：
人形机器人技术正快速发展，制造商推出了针对特定场景定制的各类异构视觉感知模块。在众多感知范式中，基于占据的表示方法因能提供全面环境理解所需的丰富语义与三维几何信息，已被公认为尤其适用于人形机器人。本研究提出了Humanoid Occupancy，这是一套通用化多模态占据感知系统，整合了软硬件组件、数据采集设备以及专用标注流水线。我们的框架采用先进的多模态融合技术，生成编码占据状态与语义标签的栅格化占据输出，从而为任务规划、导航等下游任务提供整体环境理解能力。为应对人形机器人的独特挑战，我们解决了运动学干扰与遮挡等问题，并制定了有效的传感器布局策略。此外，我们构建了首个专门面向人形机器人的全景占据数据集，为该领域未来的研发提供了有价值的基准与资源。网络架构融合了多模态特征融合与时序信息整合机制，以确保感知的鲁棒性。总体而言，Humanoid Occupancy为人形机器人提供了有效的环境感知能力，为通用视觉模块的标准化奠定了技术基础，为人形机器人在复杂现实场景中的广泛部署铺平了道路。
英文摘要：
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.
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