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About

Amirhossein Jabalameli is a robotics researcher whose work sits at the intersection of computer vision, manipulation, and assistive technology. His primary research areas include robotic grasping in cluttered environments, 3D pose estimation from single depth images, and adaptive control for assistive manipulators. Jabalameli’s most significant contribution is a fast, robust algorithm that estimates a gripper’s 6D pose from a single 2D depth image, enabling robots to grasp previously unseen objects in unstructured scenes by integrating geometry, reachability, and force-closure analysis—work that has garnered 13 citations. He also advanced edge-based recognition for novel object grasping and developed head-pose dependent trajectory adaptation for wheelchair-mounted robotic arms (WMRAs), aiming to increase autonomy for disabled and elderly users. His end-to-end intelligent adaptive grasping system for the UCF-MANUS assistive robot, presented in 2025, demonstrates a commitment to translating theory into practical, user-centered interfaces. By addressing the rigidity of traditional assistive interfaces, Jabalameli’s research pushes toward robots that adapt to humans, not the reverse. His work, cited across multiple venues, is shaping a future where assistive robots are both perceptually aware and intuitively controllable.