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Humanoid Occupancy: Enabling A Generalized Multimodal Occupancy Perception System on Humanoid Robots
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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.
Forward citations
Cited by 6 Pith papers
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Humanoid-OmniOcc: Stereo-Based Full-View Occupancy Dataset for Embodied AI
Humanoid-OmniOcc delivers a large-scale panoramic stereo occupancy dataset for humanoid robots via Real2Sim2Real, with a model that outperforms monocular baselines in both unseen sim scenes and real settings.
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RobotPan: A 360$^\circ$ Surround-View Robotic Vision System for Embodied Perception
RobotPan predicts metric-scaled compact 3D Gaussians from calibrated multi-view inputs via spherical coordinates and hierarchical voxel priors for real-time 360° robotic perception and reconstruction.
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O3N: Omnidirectional Open-Vocabulary Occupancy Prediction for Urban Autonomous Agents
O3N is the first end-to-end pure-vision framework for open-vocabulary 3D occupancy from a single omnidirectional RGB image, using polar-spiral Mamba, cost aggregation, and gradient-free modality alignment.
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GEM-Occ: From Visual Geometry Evidence to Embodied Semantic Occupancy Memory
GEM-Occ converts transient visual geometry into semantic Gaussian and free-space ray evidence, fuses them into hierarchical occupancy memory, and beats prior indoor occupancy baselines on the new HIOcc benchmark.
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O3N: Omnidirectional Open-Vocabulary Occupancy Prediction for Urban Autonomous Agents
O3N is the first open-vocabulary occupancy prediction method that takes a single omnidirectional RGB image and labels 3D voxels with both seen and unseen semantic classes.
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OmniTrack++: Omnidirectional Multi-Object Tracking by Learning Large-FoV Trajectory Feedback
OmniTrack++ improves omnidirectional multi-object tracking with trajectory feedback through DynamicSSM stabilization, FlexiTrack instances, ExpertTrack Memory with Mixture-of-Experts, and adaptive Tracklet Management,...
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