Max Robotics — Global Service Robot Directory + US Certification

About

Jared Glover’s research sits at the intersection of robotics, probabilistic modeling, and assistive technology, with a focus on enabling robots to perceive and interact with the physical world more reliably. His most influential work centers on object pose estimation and manipulation, where he pioneered the use of Monte Carlo methods with quaternion kernels and the Bingham distribution to solve the challenging problem of determining an object’s full 6-DOF pose in cluttered environments—a critical capability for personal service robotics. This work, cited 68 times, has become a foundational reference for researchers tackling occlusion and uncertainty in robotic grasping. Glover also made significant contributions to assistive robotics, notably developing a robotically-augmented walker for older adults (56 citations) that reduces fall risk and confusion while increasing mobility and enjoyment. His early work on learning user models of mobility-related activities through instrumented walking aids (31 citations) demonstrated how robots could adapt to individual users’ behaviors. Additionally, his probabilistic models of object geometry for grasp planning (42 citations) addressed the challenge of manipulating objects with incomplete or deformable geometry, advancing the field of dexterous manipulation. Glover’s work uniquely bridges theoretical probabilistic methods with practical applications in healthcare and service robotics.