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Found 14 papers, 9 papers with code
Grasping Trajectory Optimization with Point Clouds
The task space of a robot is represented by a point cloud that can be obtained from depth sensors.
CLIPPER+: A Fast Maximal Clique Algorithm for Robust Global Registration
The registration problem can be formulated as a graph and solved by finding its maximum clique.
Regret Analysis of Online LQR Control via Trajectory Prediction and Tracking: Extended Version
We adopted the notion of dynamic regret to measure the performance of this proposed online LQR control method, with our main result being that the (dynamic) regret of our method is upper bounded by a constant.
Robust Data-Driven Output Feedback Control via Bootstrapped Multiplicative Noise
We propose a robust data-driven output feedback control algorithm that explicitly incorporates inherent finite-sample model estimate uncertainties into the control design.
PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient Estimation
A numerical evaluation confirms the competitive performance of our method on classical control tasks.
Risk Bounded Nonlinear Robot Motion Planning With Integrated Perception & Control
Robust autonomy stacks require tight integration of perception, motion planning, and control layers, but these layers often inadequately incorporate inherent perception and prediction uncertainties, either ignoring them altogether or making questionable assumptions of Gaussianity.
Identification of Linear Systems with Multiplicative Noise from Multiple Trajectory Data
An algorithm based on the least-squares method and multiple-trajectory data is proposed for joint estimation of the nominal system matrices and the covariance matrix of the multiplicative noise.
Approximate Midpoint Policy Iteration for Linear Quadratic Control
We present a midpoint policy iteration algorithm to solve linear quadratic optimal control problems in both model-based and model-free settings.
Robust Learning-Based Control via Bootstrapped Multiplicative Noise
Despite decades of research and recent progress in adaptive control and reinforcement learning, there remains a fundamental lack of understanding in designing controllers that provide robustness to inherent non-asymptotic uncertainties arising from models estimated with finite, noisy data.
Linear System Identification Under Multiplicative Noise from Multiple Trajectory Data
The study of multiplicative noise models has a long history in control theory but is re-emerging in the context of complex networked systems and systems with learning-based control.
Actuator Placement under Structural Controllability using Forward and Reverse Greedy Algorithms
We then obtain performance guarantees for the forward and reverse greedy algorithms applied to the general class of matroid optimization problems by exploiting properties of the objective function such as the submodularity ratio and the curvature.
Optimization and Control
Sparse optimal control of networks with multiplicative noise via policy gradient
We give algorithms for designing near-optimal sparse controllers using policy gradient with applications to control of systems corrupted by multiplicative noise, which is increasingly important in emerging complex dynamical networks.
Learning robust control for LQR systems with multiplicative noise via policy gradient
The linear quadratic regulator (LQR) problem has reemerged as an important theoretical benchmark for reinforcement learning-based control of complex dynamical systems with continuous state and action spaces.
Stochastic Optimal Power Flow Based on Data-Driven Distributionally Robust Optimization
We propose a data-driven method to solve a stochastic optimal power flow (OPF) problem based on limited information about forecast error distributions.
Optimization and Control Systems and Control
