arXiv subjects

Zhigang Gao

Publications and source records attributed to Zhigang Gao.

3 recordsLinked to original sources

arXiv2026

DRIVE-Nav: Directional Reasoning, Inspection, and Verification for Efficient Open-Vocabulary Navigation

Open-Vocabulary Object Navigation (OVON) requires an embodied agent to locate a language-specified target in unknown environments. Many zero-shot methods rely on frontier-candidate reasoning under incomplete observations, while topology-aware methods reduce candidate redundancy but may still introduce panoramic inspection overhead and repeated reconsideration. We present DRIVE-Nav, a structured framework that organizes exploration around persistent directions rather than raw frontiers. By inspecting encountered directions more completely and restricting subsequent decisions to still-relevant directions within a forward 240-degree view range, DRIVE-Nav reduces redundant revisits and improves path efficiency. The framework extracts and tracks directional candidates from weighted Fast Marching Method (FMM) paths, maintains representative views for semantic inspection, and combines vision-language-guided prompt enrichment with cross-frame verification to improve grounding reliability. Experiments on HM3D-OVON, HM3Dv1, HM3Dv2, and MP3D demonstrate strong overall performance and consistent efficiency gains. On HM3D-OVON, DRIVE-Nav achieves 50.2% SR and 32.6% SPL, improving the previous best method by 1.9% SR and 5.6% SPL. It also delivers the best SPL on HM3Dv1, HM3Dv2, and MP3D and transfers to a physical humanoid robot. Real-world deployment also demonstrates its effectiveness.

cs.RO↗

arXiv2024

TSformer: A Non-autoregressive Spatial-temporal Transformers for 30-day Ocean Eddy-Resolving Forecasting

Ocean forecasting is critical for various applications and is essential for understanding air-sea interactions, which contribute to mitigating the impacts of extreme events. State-of-the-art ocean numerical forecasting systems can offer lead times of up to 10 days with a spatial resolution of 10 kilometers, although they are computationally expensive. While data-driven forecasting models have demonstrated considerable potential and speed, they often primarily focus on spatial variations while neglecting temporal dynamics. This paper presents TSformer, a novel non-autoregressive spatiotemporal transformer designed for medium-range ocean eddy-resolving forecasting, enabling forecasts of up to 30 days in advance. We introduce an innovative hierarchical U-Net encoder-decoder architecture based on 3D Swin Transformer blocks, which extends the scope of local attention computation from spatial to spatiotemporal contexts to reduce accumulation errors. TSformer is trained on 28 years of homogeneous, high-dimensional 3D ocean reanalysis datasets, supplemented by three 2D remote sensing datasets for surface forcing. Based on the near-real-time operational forecast results from 2023, comparative performance assessments against in situ profiles and satellite observation data indicate that, TSformer exhibits forecast performance comparable to leading numerical ocean forecasting models while being orders of magnitude faster. Unlike autoregressive models, TSformer maintains 3D consistency in physical motion, ensuring long-term coherence and stability in extended forecasts. Furthermore, the TSformer model, which incorporates surface auxiliary observational data, effectively simulates the vertical cooling and mixing effects induced by Super Typhoon Saola.

physics.ao-ph↗

arXiv2024

Applications of Deep Learning parameterization of Ocean Momentum Forcing

Mesoscale eddies are of utmost importance in understanding ocean dynamics and the transport of heat, salt, and nutrients. Accurate representation of these eddies in ocean models is essential for improving model predictions. However, accurately representing these mesoscale features in numerical models is challenging due to their relatively small size. In this study, we propose a convolutional neural network (CNN) that combines data-driven techniques with physical principles to develop a robust and interpretable parameterization scheme for mesoscale eddies in ocean modeling. We first analyze a high-resolution reanalysis dataset to extract subgrid eddy momentum and use machine learning algorithms to identify patterns and correlations. To ensure physical consistency, we have introduced conservation of momentum constraints in our CNN parameterization scheme through soft and hard constraints. The interpretability analysis illustrate that the pre-trained CNN parameterization shows promising results in accurately solving the resolved mean velocity at the local scale and effectively capturing the representation of unresolved subgrid turbulence processes at the global scale. Furthermore, to validate the CNN parameterization scheme offline, we conduct simulations using the MITgcm ocean model. A series of experiments is conducted to compare the performance of the model with the CNN parameterization scheme and high-resolution simulations. The offline validation using MITgcm simulations demonstrates the effectiveness of the CNN parameterization scheme in improving the representation of mesoscale eddies in the ocean model. Incorporating the CNN parameterization scheme leads to better agreement with high-resolution simulations and a more accurate representation of the kinetic energy spectra.

physics.flu-dyn↗