5 papers
-DRAC: Distributionally Robust Adaptive Control
Aditya Gahlawat, Sambhu H. Karumanchi, Naira Hovakimyan
Data-driven machine learning methodologies have attracted considerable attention for the control and estimation of dynamical systems. However, such implementations suffer from a la…
Wasserstein Distributionally Robust Adaptive Covariance Steering
Aditya Gahlawat, Vivek Khatana, Duo Wang +3
We present a methodology for predictable and safe covariance steering control of uncertain nonlinear stochastic processes. The systems under consideration are subject to general un…
Robust Model Based Reinforcement Learning Using Adaptive Control
Minjun Sung, Sambhu H. Karumanchi, Aditya Gahlawat +1
We introduce -MBRL, a control-theoretic augmentation scheme for Model-Based Reinforcement Learning (MBRL) algorithms. Unlike model-free approaches, MBRL algorithms l…
Motion Primitives Based Kinodynamic RRT for Autonomous Vehicle Navigation in Complex Environments
Shubham Kedia, Sambhu Harimanas Karumanchi
In this work, we have implemented a SLAM-assisted navigation module for a real autonomous vehicle with unknown dynamics. The navigation objective is to reach a desired goal configu…
Closed-Loop Benchmarking of Stereo Visual-Inertial SLAM Systems: Understanding the Impact of Drift and Latency on Tracking Accuracy
Yipu Zhao, Justin S. Smith, Sambhu H. Karumanchi +1
Visual-inertial SLAM is essential for robot navigation in GPS-denied environments, e.g. indoor, underground. Conventionally, the performance of visual-inertial SLAM is evaluated wi…