Soon-Jo Chung

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  • Robust Regression for Safe Exploration in Control

    We study the problem of safe learning and exploration in sequential control problems. The goal is to safely collect data samples from an operating environment to learn an optimal controller. A central challenge in this setting is how to quantify uncertainty in order to choose provably-safe actions that allow us to collect useful data and reduce uncertainty, thereby achieving both improved safety and optimality. To address this challenge, we present a deep robust regression model that is trained to directly predict the uncertainty bounds for safe exploration. We then show how to integrate our robust regression approach with model-based control methods by learning a dynamic model with robustness bounds. We derive generalization bounds under domain shifts for learning and connect them with safety and stability bounds in control. We demonstrate empirically that our robust regression approach can outperform conventional Gaussian process (GP) based safe exploration in settings where it is difficult to specify a good GP prior.

    06/13/2019 ∙ by Anqi Liu, et al. ∙ 1 share

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  • Distributed Bayesian Filtering using Logarithmic Opinion Pool for Dynamic Sensor Networks

    The discrete-time Distributed Bayesian Filtering (DBF) algorithm is presented for the problem of tracking a target dynamic model using a time-varying network of heterogeneous sensing agents. In the DBF algorithm, the sensing agents combine their normalized likelihood functions in a distributed manner using the logarithmic opinion pool and the dynamic average consensus algorithm. We show that each agent's estimated likelihood function globally exponentially converges to an error ball centered on the joint likelihood function of the centralized multi-sensor Bayesian filtering algorithm. We rigorously characterize the convergence, stability, and robustness properties of the DBF algorithm. Moreover, we provide an explicit bound on the time step size of the DBF algorithm that depends on the time-scale of the target dynamics, the desired convergence error bound, and the modeling and communication error bounds. Furthermore, the DBF algorithm for linear-Gaussian models is cast into a modified form of the Kalman information filter. The performance and robust properties of the DBF algorithm are validated using numerical simulations.

    12/11/2017 ∙ by Saptarshi Bandyopadhyay, et al. ∙ 0 share

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  • Neural Lander: Stable Drone Landing Control using Learned Dynamics

    Precise trajectory control near ground is difficult for multi-rotor drones, due to the complex ground effects caused by interactions between multi-rotor airflow and the environment. Conventional control methods often fail to properly account for these complex effects and fall short in accomplishing smooth landing. In this paper, we present a novel deep-learning-based robust nonlinear controller (Neural-Lander) that improves control performance of a quadrotor during landing. Our approach blends together a nominal dynamics model coupled with a Deep Neural Network (DNN) that learns the high-order interactions. We employ a novel application of spectral normalization to constrain the DNN to have bounded Lipschitz behavior. Leveraging this Lipschitz property, we design a nonlinear feedback linearization controller using the learned model and prove system stability with disturbance rejection. 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. Experimental results demonstrate that the proposed controller significantly outperforms a baseline linear proportional-derivative (PD) controller in both 1D and 3D landing cases. In particular, we show that compared to the PD controller, Neural-Lander can decrease error in z direction from 0.13m to zero, and mitigate average x and y drifts by 90 respectively, in 1D landing. Meanwhile, Neural-Lander can decrease z error from 0.12m to zero, in 3D landing. We also empirically show that the DNN generalizes well to new test inputs outside the training domain.

    11/19/2018 ∙ by Guanya Shi, et al. ∙ 0 share

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