Papers
5
Total Citations
36
H-Index
4
About
J. Joe Payne is a leading researcher in hybrid dynamical systems and legged robotics, with a focus on state estimation, control, and human-robot interaction. His most influential contribution is the development of **saltation matrices**, a mathematical framework for linearizing hybrid systems with discontinuous transitions—critical for robots like quadrupeds that switch between aerial and contact phases. This work, published in 2024, has already garnered 17 citations, underscoring its foundational impact. Payne also pioneered the **Uncertainty Aware Salted Kalman Filter**, which improves state estimation for robots interacting with uncertain surfaces, and **Periodic SLAM**, which leverages cyclic locomotion patterns to enhance visual-inertial SLAM on legged robots. His **Convergent iLQR** method enables safe trajectory planning for underactuated dynamic maneuvers, while his recent work on **shared force-language embeddings** (2025) opens new avenues for natural human-robot communication through haptic and verbal cues. With over 36 citations across his top papers, Payne’s research bridges theory and practice, offering tools that are both mathematically rigorous and directly applicable to real-world robotic systems.
Research Focus
Key Achievements
Top Papers
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