Papers
4
Total Citations
53
H-Index
3
About
Matthew Strong is a robotics researcher whose work sits at the intersection of physical human-robot interaction, tactile sensing, and 3D scene representation. His key contributions include developing frameworks for contact anticipation in robotic manipulators using onboard proximity sensors, enabling robots to safely navigate inevitable physical contact with humans. He also pioneered self-contained kinematic calibration for whole-body artificial skin, allowing accurate pose estimation of distributed sensor arrays across robot bodies. More recently, Strong has advanced the fusion of vision and touch, introducing Touch-GS, a method that uses optical tactile sensors to supervise 3D Gaussian Splatting (3DGS) scenes, and Next Best Sense, an active perception framework that guides both visual and tactile exploration. His work has garnered over 50 citations, with his 2021 paper on contact anticipation being the most cited. Strong’s research is notable for bridging the gap between collision avoidance and deliberate physical interaction, and for integrating tactile data into state-of-the-art 3D representations. His achievements position him as a rising figure in embodied AI and human-robot collaboration.
Research Focus
Key Achievements
Top Papers
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- 3Touch-GS: Visual-Tactile Supervised 3D Gaussian Splatting9 citations · 2024
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