Zhiwang Weilai Appears at CeMAT, Showcasing Warehouse Picking Robot Armstrong Pro in Live Demonstration

On November 3, CeMAT Asia, the logistics technology bellwether exhibition for international logistics technology and transportation systems, opened at the Shanghai New International Expo Centre. Embodied intelligence company Zhiwang Weilai brought two generations of warehouse mobile picking robots, Armstrong and Armstrong Pro, to the show, presenting the latest progress in the real-world deployment of embodied intelligence warehouse logistics technology through live working demonstrations of "actually doing the work." The booth was designed around the industry's real-world need for "zero warehouse modification, dense-shelf picking," with a focus on demonstrating the robot's ability to adapt to existing warehouse environments without changing warehouse infrastructure. The booth recreated a real warehousing work environment, using dense shelves, narrow aisles, standard bins and a robot work area to fully present the robot's actual working process in a warehousing scenario. Dense-shelf picking places extremely high demands on a robot's spatial adaptability, target recognition, grasping precision and task scheduling. To this end, the second-generation warehouse robot Armstrong Pro increased the whole machine's degrees of freedom from 17 in the first-generation product to 22, significantly enhancing the robot's working capability at different angles and heights; its effective working height reaches 0–2.4 meters, covering the main working range of manual shelves; and it adopts a four-wheel omnidirectional chassis that supports forward and backward movement, lateral movement left and right, diagonal movement and in-place rotation, allowing it to flexibly adapt to more complex shelf layouts and narrow workspaces. An interactive iPad experience area was also specially set up on site: visitors only need to select a target item on the screen and tap "order," and a real picking instruction is immediately dispatched to Armstrong Pro's task scheduling system. As the logistics industry's demand for warehousing efficiency, flexible operations and automation capabilities continues to rise, whether a robot can adapt to existing facilities, integrate into actual processes and continuously and stably complete tasks has become an important criterion for technology moving toward industrial application. It is not difficult for a robot to complete a single grasping demonstration; what is difficult is to stably and continuously "do the work" under the complex conditions of a real warehouse. The Zhiwang Weilai team said that its biggest technical barrier in warehousing logistics is the practical deployment solutions accumulated from long-term work in warehouses. The differences in product SKUs, shelf layouts, operating rhythms and various abnormal working conditions in real warehouses cannot be simulated in a laboratory environment. Zhiwang Weilai's approach is to continuously optimize and iterate on the problems encountered in frontline robot operations, operating experience and ways of handling anomalies, and consolidate them into practical deployment solutions. Through its self-developed WISE-RL world model architecture, Zhiwang Weilai continuously accumulates transferable and generalizable general operating capabilities from real-scenario operations, driving Physical AI from the laboratory toward real productivity.