2 citations · 4 across the 6 of their papers we have counts for
6 papers
\textbf{PLATO}: \emph{Preintegration Learning from Accurate Trajectory Observations} for Neural Inertial Odometry
Haoying Li, Qihang Liu, Yifan Peng +2
Neural inertial odometry has demonstrated strong potential for motion estimation in challenging environments, yet inertial-only preintegration remains sensitive to IMU bias and unc…
Learning Neural Controllers with Optimality and Stability Guarantees Using Input-Output Dissipativity
Han Wang, Keyan Miao, Diego Madeira +1
Deep learning methods have demonstrated significant potential for addressing complex nonlinear control problems. For real-world safety-critical tasks, however, it is crucial to pro…
Data-Enabled Predictive Control for Nonlinear Systems Based on a Koopman Bilinear Realization
Zuxun Xiong, Zhenyi Yuan, Keyan Miao +3
This paper extends the Willems' Fundamental Lemma to nonlinear control-affine systems using the Koopman bilinear realization. This enables us to bypass the Extended Dynamic Mode De…
Opt-ODENet: A Neural ODE Framework with Differentiable QP Layers for Safe and Stable Control Design (longer version)
Keyan Miao, Liqun Zhao, Han Wang +2
Designing controllers that achieve task objectives while ensuring safety is a key challenge in control systems. This work introduces Opt-ODENet, a Neural ODE framework with a diffe…
Stable and Safe Human-aligned Reinforcement Learning through Neural Ordinary Differential Equations
Liqun Zhao, Keyan Miao, Konstantinos Gatsis +1
Reinforcement learning (RL) excels in applications such as video games, but ensuring safety as well as the ability to achieve the specified goals remains challenging when using RL…
Learning Robust State Observers using Neural ODEs (longer version)
Keyan Miao, Konstantinos Gatsis
Relying on recent research results on Neural ODEs, this paper presents a methodology for the design of state observers for nonlinear systems based on Neural ODEs, learning Luenberg…