Thales Vieira

Papers

Total Citations

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About

Thales Vieira is a researcher specializing in human-robot interaction, with a particular focus on natural and intuitive control interfaces that bridge human motion and robotic systems. His work centers on enabling seamless teleoperation through body gestures, leveraging cutting-edge machine learning and computer vision techniques to make robotic control more accessible and user-friendly. Among his notable contributions, Vieira has developed frameworks that allow users to intuitively manipulate robotic arm grippers using hand gestures, tracking both position and status in real time. His 2022 work introduced Thin-Plate Splines combined with Long-term Recurrent Convolutional Networks (LRCN) to enhance user-oriented control, earning 6 citations, while his earlier 2019 study on real-time hand pose tracking laid foundational groundwork for natural human-robot interfaces. Together, these works demonstrate a consistent commitment to making robotic systems responsive to natural human movement without requiring specialized equipment or training. Vieira's research addresses a critical challenge in robotics: reducing the complexity barrier between humans and machines. Though his citation profile is still growing, his interdisciplinary approach — merging deep learning, gesture recognition, and robotics — positions him as an emerging voice in accessible, intuitive human-robot collaboration research.