Daniel Eger Passos

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About

Daniel Eger Passos is a researcher at the intersection of extended reality (XR) and robotics, with a primary focus on precision metrology and sensorimotor modeling. His most impactful work, "Measuring the Accuracy of Inside-Out Tracking in XR Devices Using a High-Precision Robotic Arm" (28 citations), establishes a rigorous, repeatable methodology for quantifying the positional and rotational accuracy of commercial AR/VR headsets. By leveraging a robotic arm as a ground-truth reference, Passos provides a critical benchmark for the XR community, enabling developers to understand the limitations of inside-out tracking in real-world applications. He further refined this approach in "A Robot-in-a-CAVE Setup for Assessing the Tracking Accuracy of AR/VR Devices," demonstrating a scalable, controlled environment for systematic evaluation. In parallel, Passos contributes to robotic perception through "Deep Learning of Proprioceptive Models for Robotic Force Estimation," where he applies neural networks to infer contact forces from internal joint data, offering a cost-effective alternative to dedicated force-torque sensors. This work addresses a fundamental challenge in adaptive robotics: achieving fast, accurate force sensing without adding hardware complexity, weight, or power consumption. Passos’s research is distinguished by its dual focus on creating robust evaluation frameworks for emerging XR technologies and developing intelligent, sensorless solutions for robotic control.