Daimeng Robotics and Shangsheng Electronics Sign Strategic Partnership to Advance Embodied AI in Automotive Electronics Manufacturing

On May 11, Daimeng (Shenzhen) Robotics Technology Co., Ltd. and Suzhou Shengsheng Electronics Co., Ltd. officially signed a strategic cooperation agreement. The two companies had previously collaborated on projects involving robotic manipulation and data collection, and are now elevating their partnership to a long-term, comprehensive strategic alliance. Under the agreement, the parties will deepen collaboration in key areas including deployment of embodied intelligence applications, tactile-enabled multimodal data collection, robotic manipulation model training, intelligent upgrades of production lines, and global expansion—accelerating the transition of tactile perception technologies from laboratory validation to industrial-scale implementation. In the initial phase, the collaboration will focus on scenarios in automotive electronics manufacturing, such as precision assembly, compliant grasping, complex process operations, quality inspection, and data collection. Joint validation efforts will be conducted to progressively develop embodied intelligence solutions tailored for industrial manufacturing. By accumulating real-world production line data, iterating models, and conducting engineering validations, the partnership aims to enhance robots’ stable operational capabilities in complex manufacturing environments and advance embodied intelligence from isolated technical breakthroughs toward scenario-based deployment and large-scale adoption. In April this year, Daimeng, together with Shengsheng Electronics and dozens of other domestic and international institutions, released the world’s largest tactile-enabled multimodal physical-world dataset, encompassing diverse modalities including tactile, visual, motion trajectory, and manipulation command data—accelerating the closed-loop transition of embodied intelligence from model development to industrial deployment. This strategic partnership will further integrate data collection, model training, and real-world industrial tasks, providing authentic task environments to continuously optimize robotic performance in complex manufacturing scenarios.