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Found 13 papers, 2 papers with code
Robust Model Based Reinforcement Learning Using $\mathcal{L}_1$ Adaptive Control
Unlike model-free approaches, MBRL algorithms learn a model of the transition function using data and use it to design a control input.
$\mathcal{L}_1$Quad: $\mathcal{L}_1$ Adaptive Augmentation of Geometric Control for Agile Quadrotors with Performance Guarantees
Quadrotors that can operate safely in the presence of imperfect model knowledge and external disturbances are crucial in safety-critical applications.
Guaranteed Nonlinear Tracking in the Presence of DNN-Learned Dynamics With Contraction Metrics and Disturbance Estimation
This paper presents an approach to trajectory-centric learning control based on contraction metrics and disturbance estimation for nonlinear systems subject to matched uncertainties.
Generalization of Safe Optimal Control Actions on Networked Multi-Agent Systems
The control action composition is achieved by taking a weighted mixture of the existing controllers according to the contribution of each component task.
$\mathcal{L}_1$ Adaptive Augmentation for Geometric Tracking Control of Quadrotors
This paper introduces an $\mathcal{L}_1$ adaptive control augmentation for geometric tracking control of quadrotors.
Tube-Certified Trajectory Tracking for Nonlinear Systems With Robust Control Contraction Metrics
This paper presents an approach towards guaranteed trajectory tracking for nonlinear control-affine systems subject to external disturbances based on robust control contraction metrics (CCM) that aims to minimize the $\mathcal L_\infty$ gain from the disturbances to nominal-actual trajectory deviations.
Distributed Algorithms for Linearly-Solvable Optimal Control in Networked Multi-Agent Systems
Distributed algorithms for both discrete-time and continuous-time linearly solvable optimal control (LSOC) problems of networked multi-agent systems (MASs) are investigated in this paper.
Cooperative Path Integral Control for Stochastic Multi-Agent Systems
Local control actions that rely only on agents' local observations are designed to optimize the joint cost functions of subsystems.
Compositionality of Linearly Solvable Optimal Control in Networked Multi-Agent Systems
The proposed approach achieves both the compositionality and optimality of control actions simultaneously within the cooperative MAS framework in both discrete- and continuous-time in a sample-efficient manner, which reduces the burden of re-computation of the optimal control solutions for the new task on the MASs.
Contraction $\mathcal{L}_1$-Adaptive Control using Gaussian Processes
We present $\mathcal{CL}_1$-$\mathcal{GP}$, a control framework that enables safe simultaneous learning and control for systems subject to uncertainties.
L1-GP: L1 Adaptive Control with Bayesian Learning
We present L1-GP, an architecture based on L1 adaptive control and Gaussian Process Regression (GPR) for safe simultaneous control and learning.
Learning Probabilistic Intersection Traffic Models for Trajectory Prediction
The safety of these agents is dependent on their ability to predict collisions with other vehicles' future trajectories for replanning and collision avoidance.
Learning to Communicate: A Machine Learning Framework for Heterogeneous Multi-Agent Robotic Systems
The encoding is useful for sharing local visual observations with other agents under communication resource constraints.
