Daniel Lockery
Papers
2
Total Citations
12
H-Index
2
About
Daniel Lockery’s research lies at the intersection of robotics, adaptive control, and biologically inspired sensing, with a focus on developing intelligent systems for autonomous target tracking and inspection. His most cited work, “Robotic Target Tracking with Approximation Space-Based Feedback During Reinforcement Learning” (2007, 7 citations), introduces a novel framework that integrates approximation space theory with reinforcement learning to improve a robot’s ability to track moving targets in real time. Building on this, his 2008 paper “Adaptive learning by a target‐tracking system” (5 citations) details a biologically inspired target-tracking system (TTS) designed for robotic inspection applications, enabling a robot to acquire high-quality images of known targets through adaptive learning. Though his citation counts are modest, Lockery’s contributions are notable for their early fusion of reinforcement learning with spatial reasoning—a precursor to modern approaches in autonomous navigation and visual servoing. His work demonstrates a practical, application-driven approach to making robots more responsive and efficient in dynamic environments, offering valuable insights for researchers in adaptive robotics and machine learning.
Research Focus
Key Achievements
Top Papers
- 1
- 2Adaptive learning by a target‐tracking system5 citations · 2008