Manipulation intelligence for the autonomous lab.
DexLabMan AI develops software that helps robot arms and multi-finger hands determine how to physically manipulate laboratory objects so the downstream operation can succeed. Our technology translates laboratory task requirements into functional grasp, object-orientation, approach, clearance, contact-mode, and motion requirements, then plans the manipulation strategy against the observed labware and station geometry.
Team
A small founding team out of the Colorado School of Mines robotics program.
Dr. Xiaoli Zhang
Director of the Mines Robotics Program and professor at Colorado School of Mines. 15-plus years in robotic manipulation and multiple NSF-funded dexterous-manipulation projects. Guides technical strategy and product direction.
Dr. Michael Bowman
Eight-plus years in robotic manipulation and 30-plus publications. Expertise across manipulation algorithms, perception-based grasping, dexterous control, and simulation-to-real learning and validation.
Luke Boyd
M.S., Colorado School of Mines. Three-plus years of robotic-manipulation experience, including national-laboratory work with KUKA systems. Leads prototype development, integration, validation, and customer discovery.