Ruth Taunton
Papers
1
Total Citations
7
H-Index
1
About
Ruth Taunton is a leading researcher at the intersection of robotics, haptics, and neural prosthetics, whose work focuses on restoring intuitive, context-aware control to artificial hands. Her key contributions lie in developing computational frameworks that allow prosthetic limbs to infer both their own pose and the shape of objects they interact with, using only tactile and proprioceptive feedback—a capability that mirrors human sensory integration. Her most-cited work, "Haptic SLAM for context-aware robotic hand prosthetics" (2015, 7 citations), introduces a particle-filter-based method for simultaneous hand pose and object shape estimation, addressing a critical gap in neuroprosthetic control. This research demonstrates that, even without vision, a robotic hand can achieve robust object recognition, a feat that remains challenging for most commercial prosthetics. Taunton’s work has significant implications for improving the dexterity and autonomy of assistive devices, particularly for users who lack visual feedback. Her achievements highlight a pioneering approach to merging Bayesian inference with haptic sensing, offering a path toward more natural, adaptive human-machine interaction.
Research Focus
Key Achievements
Top Papers
- 1