Daniel A. Hagen
Papers
2
Total Citations
10
H-Index
2
About
Daniel A. Hagen is a robotics researcher advancing the frontier of bio-inspired actuation and sensing for compliant, tendon-driven robotic systems. His work centers on developing machine learning frameworks that enable accurate limb posture estimation without traditional joint-mounted sensors—a critical challenge for creating more robust, lightweight, and dexterous robots. Hagen’s most cited paper, "insideOut: A Bio-Inspired Machine Learning Approach to Estimating Posture in Robots Driven by Compliant Tendons" (2021, 7 citations), introduces a novel method that infers joint angles from actuator shaft positions, mimicking biological proprioception to simplify mechanical design while maintaining control fidelity. His earlier foundational work, "A Bio-Inspired Framework for Joint Angle Estimation from Non-Collocated Sensors in Tendon-driven Systems" (2020, 3 citations), established the theoretical basis for decoupling sensor placement from joint locations, reducing limb inertia and noise. Though early in his career, Hagen’s contributions are shaping the next generation of agile, animal-like robots where sensing must be distributed and compliant. His research directly addresses the trade-off between mechanical simplicity and control precision, offering practical pathways for smaller, more resilient robotic morphologies in applications from prosthetics to exploratory robotics.
Research Focus
Key Achievements
Top Papers
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