Search Results for author:
Found 12 papers, 2 papers with code
Online Optimization with Feedback Delay and Nonlinear Switching Cost
We study a variant of online optimization in which the learner receives $k$-round $\textit{delayed feedback}$ about hitting cost and there is a multi-step nonlinear switching cost, i. e., costs depend on multiple previous actions in a nonlinear manner.
Robustness and Consistency in Linear Quadratic Control with Untrusted Predictions
Motivated by online learning methods, we design a self-tuning policy that adaptively learns the trust parameter $\lambda$ with a competitive ratio that depends on $\varepsilon$ and the variation of system perturbations and predictions.
Meta-Adaptive Nonlinear Control: Theory and Algorithms
We provide instantiations of our approach under varying conditions, leading to the first non-asymptotic end-to-end convergence guarantee for multi-task nonlinear control.
Meta-Learning-Based Robust Adaptive Flight Control Under Uncertain Wind Conditions
We validate our approach by flying a drone in an open air wind tunnel under varying wind conditions and along challenging trajectories.
Neural-Swarm2: Planning and Control of Heterogeneous Multirotor Swarms using Learned Interactions
We present Neural-Swarm2, a learning-based method for motion planning and control that allows heterogeneous multirotors in a swarm to safely fly in close proximity.
The Power of Predictions in Online Control
We study the impact of predictions in online Linear Quadratic Regulator control with both stochastic and adversarial disturbances in the dynamics.
Fast Uncertainty Quantification for Deep Object Pose Estimation
Deep learning-based object pose estimators are often unreliable and overconfident especially when the input image is outside the training domain, for instance, with sim2real transfer.
Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems
The Info-SNOC algorithm is used to compute a sub-optimal pool of safe motion plans that aid in exploration for learning unknown residual dynamics under safety constraints.
Neural-Swarm: Decentralized Close-Proximity Multirotor Control Using Learned Interactions
We design a stable nonlinear tracking controller using the learned model.
Online Optimization with Memory and Competitive Control
This paper presents competitive algorithms for a novel class of online optimization problems with memory.
Robust Regression for Safe Exploration in Control
To address this challenge, we present a deep robust regression model that is trained to directly predict the uncertainty bounds for safe exploration.
Neural Lander: Stable Drone Landing Control using Learned Dynamics
To the best of our knowledge, this is the first DNN-based nonlinear feedback controller with stability guarantees that can utilize arbitrarily large neural nets.
