Search Results for author: Guanya Shi

Found 12 papers, 2 papers with code

Online Optimization with Feedback Delay and Nonlinear Switching Cost

no code implementations29 Oct 2021 Weici Pan, Guanya Shi, Yiheng Lin, Adam Wierman

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

no code implementations NeurIPS 2021 Tongxin Li, Ruixiao Yang, Guannan Qu, Guanya Shi, Chenkai Yu, Adam Wierman, Steven H. Low

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.

online learning

Meta-Adaptive Nonlinear Control: Theory and Algorithms

1 code implementation NeurIPS 2021 Guanya Shi, Kamyar Azizzadenesheli, Michael O'Connell, Soon-Jo Chung, Yisong Yue

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.

Multi-Task Learning Representation Learning

Meta-Learning-Based Robust Adaptive Flight Control Under Uncertain Wind Conditions

no code implementations2 Mar 2021 Michael O'Connell, Guanya Shi, Xichen Shi, Soon-Jo Chung

We validate our approach by flying a drone in an open air wind tunnel under varying wind conditions and along challenging trajectories.

Meta-Learning

Neural-Swarm2: Planning and Control of Heterogeneous Multirotor Swarms using Learned Interactions

no code implementations10 Dec 2020 Guanya Shi, Wolfgang Hönig, Xichen Shi, Yisong Yue, Soon-Jo Chung

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.

Motion Planning

The Power of Predictions in Online Control

no code implementations NeurIPS 2020 Chenkai Yu, Guanya Shi, Soon-Jo Chung, Yisong Yue, Adam Wierman

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

no code implementations16 Nov 2020 Guanya Shi, Yifeng Zhu, Jonathan Tremblay, Stan Birchfield, Fabio Ramos, Animashree Anandkumar, Yuke Zhu

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.

Pose Estimation

Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems

no code implementations9 May 2020 Yashwanth Kumar Nakka, Anqi Liu, Guanya Shi, Anima Anandkumar, Yisong Yue, Soon-Jo Chung

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.

Motion Planning Optimal Motion Planning +1

Online Optimization with Memory and Competitive Control

1 code implementation NeurIPS 2020 Guanya Shi, Yiheng Lin, Soon-Jo Chung, Yisong Yue, Adam Wierman

This paper presents competitive algorithms for a novel class of online optimization problems with memory.

Robust Regression for Safe Exploration in Control

no code implementations L4DC 2020 Anqi Liu, Guanya Shi, Soon-Jo Chung, Anima Anandkumar, Yisong Yue

To address this challenge, we present a deep robust regression model that is trained to directly predict the uncertainty bounds for safe exploration.

Generalization Bounds Safe Exploration

Neural Lander: Stable Drone Landing Control using Learned Dynamics

no code implementations19 Nov 2018 Guanya Shi, Xichen Shi, Michael O'Connell, Rose Yu, Kamyar Azizzadenesheli, Animashree Anandkumar, Yisong Yue, Soon-Jo Chung

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.

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