Seuraa
J. Joe Payne
J. Joe Payne
Senior Robotics Software Engineer, Relativity Space
Vahvistettu sähköpostiosoite verkkotunnuksessa andrew.cmu.edu - Kotisivu
Nimike
Viittaukset
Viittaukset
Vuosi
The Salted Kalman Filter: Kalman filtering on hybrid dynamical systems
NJ Kong, JJ Payne, G Council, AM Johnson
Automatica 131, 109752, 2021
292021
Saltation matrices: The essential tool for linearizing hybrid dynamical systems
NJ Kong, JJ Payne, J Zhu, AM Johnson
Proceedings of the IEEE, 2024
192024
The uncertainty aware Salted Kalman Filter: State estimation for hybrid systems with uncertain guards
JJ Payne, NJ Kong, AM Johnson
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2022
102022
Convergent iLQR for safe trajectory planning and control of legged robots
J Zhu, JJ Payne, AM Johnson
2024 IEEE International Conference on Robotics and Automation (ICRA), 8051-8057, 2024
72024
Periodic SLAM: Using cyclic constraints to improve the performance of visual-inertial SLAM on legged robots
H Kumar, JJ Payne, M Travers, AM Johnson, H Choset
2022 International Conference on Robotics and Automation (ICRA), 9477-9483, 2022
62022
Mapping distributions through hybrid dynamical systems and its application to Kalman filtering
NJ Kong, JJ Payne, G Council, AM Johnson
arXiv preprint arXiv:2007.12233, 2020
22020
Hybrid Iterative Linear Quadratic Estimation: Optimal Estimation for Hybrid Systems
JJ Payne, J Zhu, NJ Kong, AM Johnson
IEEE Robotics and Automation Letters, 2025
2025
Multi-Momentum Observer Contact Estimation for Bipedal Robots
JJ Payne, DA Hagen, D Garagić, AM Johnson
arXiv preprint arXiv:2412.03462, 2024
2024
Double-Anonymous Review for Robotics
JK Yim, P Nadan, J Zhu, A Stutt, JJ Payne, C Pavlov, AM Johnson
arXiv preprint arXiv:2406.10059, 2024
2024
State Estimation Techniques for Hybrid Dynamical Systems
JJ Payne
Carnegie Mellon University, 2024
2024
Feedback linearization for a full order Quadcopter Model
J Zhu, J Payne, N Kong
Generating a Dynamic Controller for a Flamingo Inspired Robot using Deep Reinforcement Learning
E Lu, NJ Kong, JJ Payne, AM Johnson
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Artikkelit 1–12