Edward W. Staley

Johns Hopkins University Applied Physics Laboratory

Papers

Total Citations

H-Index

About

Edward W. Staley is a robotics researcher focused on advancing autonomous manipulation and learning from demonstration. His work centers on two key areas: intelligent joint control of robotic manipulators and cameras, and generalizable behavior learning. In his 2018 paper, Staley pioneered a deep reinforcement learning (DRL) approach that simultaneously controls a robotic arm and its viewing camera to perform reaching tasks in cluttered environments with obstacles and occluders. This work eliminates the need for complex perception, planning, and calibration pipelines, offering a streamlined solution for real-world robotic interaction. His 2022 paper introduces Primitive Imitation for Control (PICO), a novel framework that combines imitation learning with task decomposition to enable robots to generalize prior experiences to new tasks—a long-standing challenge in robotics. While his citation counts (2 each) reflect early-stage impact, these contributions are foundational to scalable, adaptive robotic systems. Staley’s research holds promise for applications in manufacturing, assistive robotics, and autonomous exploration, where robots must operate reliably in unpredictable settings. His work exemplifies a shift toward data-driven, generalizable control strategies in modern robotics.