Papers
15
Total Citations
191
H-Index
9
About
Kieran Gilday is a robotics researcher whose work sits at the intersection of soft robotics, biomechanical hand design, and machine learning-based sensing and control. His research focuses on developing compliant, anthropomorphic robotic hands that exploit passive body dynamics to achieve robust, adaptive grasping — a philosophy inspired by the musculoskeletal architecture of the human hand. A particularly significant contribution is his development of 3D printable sensorized gelatin hydrogels for soft robotic structures (2021, 40 citations), which opened new possibilities for fabricating multimaterial soft robots with integrated sensing. Gilday has also advanced the understanding of how wrist-driven actuation and passive dynamics can enable nuanced environmental interaction without complex control schemes. His work on drift-free latent space representations for soft strain sensors addresses the nonlinear modeling challenges that have long hindered reliable tactile feedback in soft systems. More recently, he has explored predictive learning for error recovery and spatiotemporal softness perception, pushing robots toward greater autonomy. With contributions spanning sensor design, hand morphology, and machine learning tutorials for soft robotics, Gilday's cumulative output — exceeding 170 citations — makes him a valuable voice in the growing field of embodied robotic manipulation.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5Drift-Free Latent Space Representation for Soft Strain Sensors14 citations · 2020
- 6
- 7
- 8
- 9
- 10Machine Learning for Soft Robot Sensing and Control: A Tutorial Study9 citations · 2022