290 citations · 687 across the 20 of their papers we have counts for
20 papers
Hierarchical Meta-learning-based Adaptive Controller
Fengze Xie, Guanya Shi, Michael O'Connell +2
We study how to design learning-based adaptive controllers that enable fast and accurate online adaptation in changing environments. In these settings, learning is typically done d…
Optimal Exploration for Model-Based RL in Nonlinear Systems
Andrew Wagenmaker, Guanya Shi, Kevin Jamieson
Learning to control unknown nonlinear dynamical systems is a fundamental problem in reinforcement learning and control theory. A commonly applied approach is to first explore the e…
Active Representation Learning for General Task Space with Applications in Robotics
Yifang Chen, Yingbing Huang, Simon S. Du +2
Representation learning based on multi-task pretraining has become a powerful approach in many domains. In particular, task-aware representation learning aims to learn an optimal r…
CAJun: Continuous Adaptive Jumping using a Learned Centroidal Controller
Yuxiang Yang, Guanya Shi, Xiangyun Meng +4
We present CAJun, a novel hierarchical learning and control framework that enables legged robots to jump continuously with adaptive jumping distances. CAJun consists of a high-leve…
Leveraging Predictions in Power System Frequency Control: an Adaptive Approach
Wenqi Cui, Guanya Shi, Yuanyuan Shi +1
Ensuring the frequency stability of electric grids with increasing renewable resources is a key problem in power system operations. In recent years, a number of advanced controller…
Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds
Michael O'Connell, Guanya Shi, Xichen Shi +4
Executing safe and precise flight maneuvers in dynamic high-speed winds is important for the ongoing commoditization of uninhabited aerial vehicles (UAVs). However, because the rel…
Online Optimization with Feedback Delay and Nonlinear Switching Cost
Weici Pan, Guanya Shi, Yiheng Lin +1
We study a variant of online optimization in which the learner receives -round about hitting cost and there is a multi-step nonlinear switching cost,…
Perturbation-based Regret Analysis of Predictive Control in Linear Time Varying Systems
Yiheng Lin, Yang Hu, Haoyuan Sun +3
We study predictive control in a setting where the dynamics are time-varying and linear, and the costs are time-varying and well-conditioned. At each time step, the controller rece…
Robustness and Consistency in Linear Quadratic Control with Untrusted Predictions
Tongxin Li, Ruixiao Yang, Guannan Qu +4
We study the problem of learning-augmented predictive linear quadratic control. Our goal is to design a controller that balances \textit{"consistency"}, which measures the competit…
Meta-Adaptive Nonlinear Control: Theory and Algorithms
Guanya Shi, Kamyar Azizzadenesheli, Michael O'Connell +2
We present an online multi-task learning approach for adaptive nonlinear control, which we call Online Meta-Adaptive Control (OMAC). The goal is to control a nonlinear system subje…
Meta-Learning-Based Robust Adaptive Flight Control Under Uncertain Wind Conditions
Michael O'Connell, Guanya Shi, Xichen Shi +1
Realtime model learning proves challenging for complex dynamical systems, such as drones flying in variable wind conditions. Machine learning technique such as deep neural networks…
Neural-Swarm2: Planning and Control of Heterogeneous Multirotor Swarms using Learned Interactions
Guanya Shi, Wolfgang Hönig, Xichen Shi +2
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. Such operation…
Fast Uncertainty Quantification for Deep Object Pose Estimation
Guanya Shi, Yifeng Zhu, Jonathan Tremblay +4
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 transf…
Competitive Control with Delayed Imperfect Information
Chenkai Yu, Guanya Shi, Soon-Jo Chung +2
This paper studies the impact of imperfect information in online control with adversarial disturbances. In particular, we consider both delayed state feedback and inexact predictio…
The Power of Predictions in Online Control
Chenkai Yu, Guanya Shi, Soon-Jo Chung +2
We study the impact of predictions in online Linear Quadratic Regulator control with both stochastic and adversarial disturbances in the dynamics. In both settings, we characterize…
Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems
Yashwanth Kumar Nakka, Anqi Liu, Guanya Shi +3
Learning-based control algorithms require data collection with abundant supervision for training. Safe exploration algorithms ensure the safety of this data collection process even…
Neural-Swarm: Decentralized Close-Proximity Multirotor Control Using Learned Interactions
Guanya Shi, Wolfgang Hönig, Yisong Yue +1
In this paper, we present Neural-Swarm, a nonlinear decentralized stable controller for close-proximity flight of multirotor swarms. Close-proximity control is challenging due to t…
Online Optimization with Memory and Competitive Control
Guanya Shi, Yiheng Lin, Soon-Jo Chung +2
This paper presents competitive algorithms for a novel class of online optimization problems with memory. We consider a setting where the learner seeks to minimize the sum of a hit…
Robust Regression for Safe Exploration in Control
Anqi Liu, Guanya Shi, Soon-Jo Chung +2
We study the problem of safe learning and exploration in sequential control problems. The goal is to safely collect data samples from operating in an environment, in order to learn…
Neural Lander: Stable Drone Landing Control using Learned Dynamics
Guanya Shi, Xichen Shi, Michael O'Connell +5
Precise near-ground trajectory control is difficult for multi-rotor drones, due to the complex aerodynamic effects caused by interactions between multi-rotor airflow and the enviro…