We are excited to share that our team has begun field testing WeedHitter 2, the latest version of our laser weeding robot.

WeedHitter 2 features a new modular design that brings the platform closer to an industrial-ready system. The redesigned architecture supports more efficient integration, testing, maintenance, and future upgrades as we continue moving the technology from research prototypes toward practical agricultural deployment.

We are pleased to announce that two papers led by our master’s students, Changyou Miao and Li Teng, have been accepted for presentation at IEEE/RSJ IROS 2026. Both works address the challenge of robotic harvesting in complex, occluded environments using reinforcement learning, with a shared focus on sim-to-real transfer and hierarchical policy design.

A paper led by PhD student Meili Sun has been published in Artificial Intelligence in Agriculture, titled “Vision-based early fault diagnosis and self-recovery for strawberry harvesting robots”. The study presents an integrated vision framework that enables early fault diagnosis and self-recovery during robotic harvesting.

We are pleased to announce that two papers led by our master’s students have been accepted. Quan Zhao (first author) and colleagues present a VLM-driven framework for active obstacle separation in robotic harvesting.

We are delighted to announce the acceptance of a paper to IEEE ICRA 2026, marking a remarkable achievement led by Xi Chen, an undergraduate intern from China Agricultural University. Co-first authored with Master’s student Yun Wang, their work on the “Hybrid Rigid-Soft Robotic Gripper with Shape Adaptation, Uniform Force Distribution, and Self-Locking Capabilities” will be presented in Vienna.