Zehao Wang

CoopUQ: Robust Multi-Robot Social Navigation via Uncertainty-Aware Cooperative Forecasting

Zehao Wang, Jianpeng Yao, Yuping Wang, et al.

LIBERO-PeRM: Benchmarking Personalized Robotic Manipulation

Zhixu Li, Keqian Tang, Litian Gong, et al.

Hierarchical Residual Policy Learning for Real-World Mobile Manipulation with Sparse Human Guidance

Zhefei Gong, Hefeifei Jiang, Zehao Wang, et al.

What Matters in Quality-Aware VLA Pretraining: A Benchmark and Empirical Study

Mingxuan Yan, Litian Gong, Dihong Huang, et al.

Unifying Policy Learning and State Prediction through Spatial Language Modeling

Minye Wu, Zehao Wang, Tinne Tuytelaars

Drive My Way: Preference Alignment of Vision-Language-Action Model for Personalized Driving

Zehao Wang, Huaide Jiang, Shuaiwu Dong, et al.

NavTrust: Benchmarking Trustworthiness for Embodied Navigation

Huaide Jiang, Yash Chaudhary, Yuping Wang, et al.

NavTrust is a new benchmark that tests how well AI navigation systems handle real-world problems like blurry images, sensor noise, and unclear instructions. The researchers found that current state-of-the-art navigation systems perform much worse when exposed to these corruptions, and they tested four strategies to make these systems more reliable. They also validated their findings on an actual robot to confirm the improvements work in practice.