Jianliang Mao — accepted AI-conference papers

2026

Guarding Force: Safety-Critical Compliant Control for Robot-Environment Interaction

Xinming Wang, Jun Yang, Jianliang Mao, Shihua Li, Yunda Yan

RA-L 2026

In this letter, we propose a safety-critical compliant control strategy designed to strictly enforce interaction force constraints during the physical interaction of robots with environments. The interaction force constraint is interpreted as a new force-constrained control barrier function (FC-CBF)

Cited by 2 Source ↗Scholar ↗

2025

Flexible Active Safety Motion Control for Robotic Obstacle Avoidance: A CBF-Guided MPC Approach

Jinhao Liu, Jun Yang, Jianliang Mao, Tianqi Zhu, Qihang Xie, Yimeng Li +2

RA-L 2025

A flexible active safety motion (FASM) control approach is proposed for collision avoidance in robot manipulators. The key feature is the use of control barrier functions (CBFs) to design flexible CBF-guided safety criteria (CBFSC) with dynamically optimized decay rates, providing both flexibility a

Cited by 21 Source ↗Scholar ↗