R. Steiner

Papers

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About

R. Steiner’s research centers on robotics, neural network control, and kinematic optimization, with a particular focus on solving inverse kinematics for redundant manipulators. Steiner’s most notable contribution is the development of a quasi-local solution for the inverse kinematics of a redundant robot arm, a problem critical to enabling precise, flexible motion in robotic systems. In their seminal 1990 paper, Steiner introduced a neural network mechanism that selects the most suitable joint for generating a given trajectory in a planar, four-joint robot. This approach offered a computationally efficient alternative to traditional global optimization methods, allowing for real-time adaptability in constrained workspaces. While the work has accrued 10 citations, its conceptual influence lies in bridging neural computation with robotic control, anticipating later advances in learning-based motion planning. Steiner’s research is particularly valuable for students and engineers exploring how bio-inspired algorithms can simplify complex mechanical problems, and their work remains a reference point for those developing intelligent, adaptive robotic systems in manufacturing and automation.