Bin Hu

Unified disturbance-aware safe kinematic control for closed-architecture robots

Fan Zhang, Jinfeng Chen, Joseph Jean-Baptiste Mvogo Ahanda, Hanz Richter, Ge Lv, Bin Hu, et al.

Verify, Repair, Repeat, or Stop? Robust Stopping for Noisy Verify-Repair Loops in LLM Agents

Yitao Wu, Si Shen, Rui Yang, Hong Peng, Bin Hu

Verify-repair loops are a standard means for large language model (LLM) agents to correct faulty plans in code generation, mathematical reasoning, and tool use. When both the verifier and the repairer are noisy, repair can damage already-correct plans, and reported acceptance kee…Read more

Neural-ESO: A Dual-Pathway Architecture for Provably Robust Learning-Based Control

Fan Zhang, Richie Suganda, Jinfeng Chen, Wenhua Liu, Hantao Fu, Bin Hu, et al.

A learning-enabled disturbance-rejection framework based on a Neural Extended State Observer (Neural-ESO) is presented in this letter. Unlike existing learning-based control methods that largely rely on the learned model once deployed, Neural-ESO adopts a dual-pathway architectur…Read more

ABot-M0.5: Unified Mobility-and-Manipulation World Action Model

Ronghan Chen, Yandan Yang, Zuojin Tang, Dongjie Huo, Tong Lin, Haoning Wu, et al.

Mobile manipulation is a key capability for general-purpose robots, yet remains challenging for current embodied learning methods. VLA policies are typically reactive and lack explicit world modeling, while existing World Action Models (WAMs) are still poorly aligned with the str…Read more

Urban Flood Risk Assessment through the Integration of Natural and Human Resilience Based on Machine Learning Models

Wenting Zhang, Bin Hu, Yongzhi Liu, Xingnan Zhang, Zhixuan Li

Flood risk assessment and mapping are considered essential tools for the improvement of flood management. This research aims to construct a more comprehensive flood assessment framework by emphasizing factors related to human resilience and integrating them with meteorological an…Read more