A humanoid robot learns self-other distinction from proprioceptive-visual correspondence without identity labels or a predefined kinematic model. The resulting distinction supervises a predictive 3D self-model and supports target reaching, collision-aware motion planning, and human-to-robot motion retargeting.
@article{chen2026selfother,title={Proprioceptive-visual Correspondence Enables Self-other Distinction in Humanoid Robots},author={Chen, Yurun and Gao, Tianyuan and Ge, Yizhong and Ban, Shikun and Wang, Yizhou and Xiong, Hongkai and Zeng, Wenjun and Zhu, Wentao},year={2026},month=jun,}
RSE
A Novel Soil Moisture Retrieval Method via Combining Radiative Transfer Model and Machine Learning
Yurun Chen, Cheng Tong, Josh Qixuan Sun, Yulin Shangguan, Xiaodong Deng, Mark Crowley, Hongquan Wang, Yang Ye, Haijun Bao, and Ruqi Huang
Remote Sensing of Environment. Published. JIF 12.3 (2025), JCR Q1 (SCIE). Physics-guided, interpretable scientific machine learning with KAN , May 2026
This work combines a radiative transfer model with a Kolmogorov-Arnold Network to learn explicit soil-moisture retrieval formulas. The framework preserves physical structure, exposes relationships among key variables, and improves interpretability and real-data generalization over a conventional multilayer perceptron.
@article{chen2026soil,title={A Novel Soil Moisture Retrieval Method via Combining Radiative Transfer Model and Machine Learning},author={Chen, Yurun and Tong, Cheng and Sun, Josh Qixuan and Shangguan, Yulin and Deng, Xiaodong and Crowley, Mark and Wang, Hongquan and Ye, Yang and Bao, Haijun and Huang, Ruqi},journal={Remote Sensing of Environment},volume={338},pages={115378},year={2026},month=may,doi={10.1016/j.rse.2026.115378},}
arXiv
Towards the Harness of Embodied Agents
Qi Wang, Tianyi Wang, Chengyang Li, Shikun Ban, Yurun Chen, Yizhong Ge, Jason Qin, Chengtai Li, and Wentao Zhu
arXiv preprint · Technical Report. My contribution: Unitree G1 locomotion and manipulation deployment, closed-loop tool use, persistent scene state, and action-outcome evaluation , Aug 2026
Thea is an embodied-agent harness that unifies language models, robot policies, and physical tools in a closed-loop tool-calling framework. Persistent scene graphs and independent evaluators provide structured world state, action termination, outcome verification, and failure diagnosis for long-horizon tasks.
@techreport{wang2026thea,title={Towards the Harness of Embodied Agents},author={Wang, Qi and Wang, Tianyi and Li, Chengyang and Ban, Shikun and Chen, Yurun and Ge, Yizhong and Qin, Jason and Li, Chengtai and Zhu, Wentao},institution={Technical Report},year={2026},month=aug,}
2025
ICCV
ARMO: Autoregressive Rigging for Multi-Category Objects
ARMO predicts 3D joint locations and skeletal connectivity in a unified autoregressive framework. It is trained on OmniRig, a large-scale multi-category rigging dataset containing 79,499 meshes with skeleton and skinning annotations.
@inproceedings{sun2025armo,title={{ARMO}: Autoregressive Rigging for Multi-Category Objects},author={Sun, Mingze and Mao, Shiwei and Chen, Keyi and Chen, Yurun and Lu, Shunlin and Wang, Jingbo and Dong, Junting and Huang, Ruqi},booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},pages={7721--7730},year={2025},month=oct,}
CVPR
DRiVE: Diffusion-based Rigging Empowers Generation of Versatile and Expressive Characters
Mingze Sun, Junhao Chen, Junting Dong, Yurun Chen, Xinyu Jiang, Shiwei Mao, Puhua Jiang, Jingbo Wang, Bo Dai, and Ruqi Huang
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Jun 2025
DRiVE generates and rigs expressive 3D Gaussian characters with complex garments and hair. Its GSDiff module predicts joints as spatial distributions, while the AnimeRig dataset supplies large-scale skeleton and skinning supervision.
@inproceedings{sun2025drive,title={{DRiVE}: Diffusion-based Rigging Empowers Generation of Versatile and Expressive Characters},author={Sun, Mingze and Chen, Junhao and Dong, Junting and Chen, Yurun and Jiang, Xinyu and Mao, Shiwei and Jiang, Puhua and Wang, Jingbo and Dai, Bo and Huang, Ruqi},booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},pages={21170--21180},year={2025},month=jun,}
IEEE JSTARS
Is Nighttime Light Primarily From Human Settlements? Exploring the Spatial Relationship Between NTL and Impervious Surface in Zhejiang Province, China
Cheng Tong, Xingyu Xue, Chenhao Huang, Yurun Chen, Haijun Bao, Congmou Zhu, Yang Ye, and Binjie Chen
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2025
This study analyzes the scale-dependent relationship and spatial mismatch between nighttime light and impervious surfaces, revealing how human activity, land use, and regional development jointly shape observed nighttime-light patterns.
@article{tong2025nighttime,title={Is Nighttime Light Primarily From Human Settlements? Exploring the Spatial Relationship Between {NTL} and Impervious Surface in Zhejiang Province, China},author={Tong, Cheng and Xue, Xingyu and Huang, Chenhao and Chen, Yurun and Bao, Haijun and Zhu, Congmou and Ye, Yang and Chen, Binjie},journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},volume={18},pages={15900--15913},year={2025},doi={10.1109/JSTARS.2025.3580758},}
ISWCR
The Passive Microwave Remote Sensing in Soil Moisture Retrieval: Products, Models, Applications and Challenges
Cheng Tong, Xiaodong Deng, Yulin Shangguan, Baiyu Dong, Yurun Chen, Chenhao Huang, Luyao Zhu, Sinan Li, Yang Ye, and Hongquan Wang
International Soil and Water Conservation Research, Dec 2025
A systematic review of passive-microwave soil-moisture products, physical and machine-learning retrieval models, application areas, open challenges, and future research directions.
@article{tong2025passive,title={The Passive Microwave Remote Sensing in Soil Moisture Retrieval: Products, Models, Applications and Challenges},author={Tong, Cheng and Deng, Xiaodong and Shangguan, Yulin and Dong, Baiyu and Chen, Yurun and Huang, Chenhao and Zhu, Luyao and Li, Sinan and Ye, Yang and Wang, Hongquan},journal={International Soil and Water Conservation Research},volume={13},number={4},pages={843--859},year={2025},month=dec,doi={10.1016/j.iswcr.2025.06.006},}
2024
SIGGRAPH Asia
SRIF: Semantic Shape Registration Empowered by Diffusion-based Image Morphing and Flow Estimation
Mingze Sun, Chen Guo, Puhua Jiang, Shiwei Mao, Yurun Chen, and Ruqi Huang
In ACM SIGGRAPH Asia 2024 Conference Papers, Dec 2024
SRIF combines diffusion-based image morphing, dynamic 3D Gaussian splatting, and flow estimation to recover dense semantic correspondences and smooth interpolations between complex shapes.
@inproceedings{sun2024srif,title={{SRIF}: Semantic Shape Registration Empowered by Diffusion-based Image Morphing and Flow Estimation},author={Sun, Mingze and Guo, Chen and Jiang, Puhua and Mao, Shiwei and Chen, Yurun and Huang, Ruqi},booktitle={ACM SIGGRAPH Asia 2024 Conference Papers},pages={1--11},year={2024},month=dec,doi={10.1145/3680528.3687567},}
