Search Results for author: Aditya Gahlawat

Found 13 papers, 2 papers with code

Robust Model Based Reinforcement Learning Using $\mathcal{L}_1$ Adaptive Control

no code implementations21 Mar 2024 Minjun Sung, Sambhu H. Karumanchi, Aditya Gahlawat, Naira Hovakimyan

Unlike model-free approaches, MBRL algorithms learn a model of the transition function using data and use it to design a control input.

Model-based Reinforcement Learning reinforcement-learning

$\mathcal{L}_1$Quad: $\mathcal{L}_1$ Adaptive Augmentation of Geometric Control for Agile Quadrotors with Performance Guarantees

no code implementations14 Feb 2023 Zhuohuan Wu, Sheng Cheng, Pan Zhao, Aditya Gahlawat, Kasey A. Ackerman, Arun Lakshmanan, Chengyu Yang, Jiahao Yu, Naira Hovakimyan

Quadrotors that can operate safely in the presence of imperfect model knowledge and external disturbances are crucial in safety-critical applications.

Guaranteed Nonlinear Tracking in the Presence of DNN-Learned Dynamics With Contraction Metrics and Disturbance Estimation

no code implementations15 Dec 2021 Pan Zhao, Ziyao Guo, Aditya Gahlawat, Hyungsoo Kang, Naira Hovakimyan

This paper presents an approach to trajectory-centric learning control based on contraction metrics and disturbance estimation for nonlinear systems subject to matched uncertainties.

Generalization of Safe Optimal Control Actions on Networked Multi-Agent Systems

no code implementations21 Sep 2021 Lin Song, Neng Wan, Aditya Gahlawat, Chuyuan Tao, Naira Hovakimyan, Evangelos A. Theodorou

The control action composition is achieved by taking a weighted mixture of the existing controllers according to the contribution of each component task.

$\mathcal{L}_1$ Adaptive Augmentation for Geometric Tracking Control of Quadrotors

2 code implementations14 Sep 2021 Zhuohuan Wu, Sheng Cheng, Kasey A. Ackerman, Aditya Gahlawat, Arun Lakshmanan, Pan Zhao, Naira Hovakimyan

This paper introduces an $\mathcal{L}_1$ adaptive control augmentation for geometric tracking control of quadrotors.

Tube-Certified Trajectory Tracking for Nonlinear Systems With Robust Control Contraction Metrics

1 code implementation9 Sep 2021 Pan Zhao, Arun Lakshmanan, Kasey Ackerman, Aditya Gahlawat, Marco Pavone, Naira Hovakimyan

This paper presents an approach towards guaranteed trajectory tracking for nonlinear control-affine systems subject to external disturbances based on robust control contraction metrics (CCM) that aims to minimize the $\mathcal L_\infty$ gain from the disturbances to nominal-actual trajectory deviations.

Motion Planning valid

Distributed Algorithms for Linearly-Solvable Optimal Control in Networked Multi-Agent Systems

no code implementations18 Feb 2021 Neng Wan, Aditya Gahlawat, Naira Hovakimyan, Evangelos A. Theodorou, Petros G. Voulgaris

Distributed algorithms for both discrete-time and continuous-time linearly solvable optimal control (LSOC) problems of networked multi-agent systems (MASs) are investigated in this paper.

Cooperative Path Integral Control for Stochastic Multi-Agent Systems

no code implementations30 Sep 2020 Neng Wan, Aditya Gahlawat, Naira Hovakimyan, Evangelos A. Theodorou, Petros G. Voulgaris

Local control actions that rely only on agents' local observations are designed to optimize the joint cost functions of subsystems.

Compositionality of Linearly Solvable Optimal Control in Networked Multi-Agent Systems

no code implementations28 Sep 2020 Lin Song, Neng Wan, Aditya Gahlawat, Naira Hovakimyan, Evangelos A. Theodorou

The proposed approach achieves both the compositionality and optimality of control actions simultaneously within the cooperative MAS framework in both discrete- and continuous-time in a sample-efficient manner, which reduces the burden of re-computation of the optimal control solutions for the new task on the MASs.

Contraction $\mathcal{L}_1$-Adaptive Control using Gaussian Processes

no code implementations8 Sep 2020 Aditya Gahlawat, Arun Lakshmanan, Lin Song, Andrew Patterson, Zhuohuan Wu, Naira Hovakimyan, Evangelos Theodorou

We present $\mathcal{CL}_1$-$\mathcal{GP}$, a control framework that enables safe simultaneous learning and control for systems subject to uncertainties.

Gaussian Processes

L1-GP: L1 Adaptive Control with Bayesian Learning

no code implementations L4DC 2020 Aditya Gahlawat, Pan Zhao, Andrew Patterson, Naira Hovakimyan, Evangelos Theodorou

We present L1-GP, an architecture based on L1 adaptive control and Gaussian Process Regression (GPR) for safe simultaneous control and learning.

GPR regression

Learning Probabilistic Intersection Traffic Models for Trajectory Prediction

no code implementations5 Feb 2020 Andrew Patterson, Aditya Gahlawat, Naira Hovakimyan

The safety of these agents is dependent on their ability to predict collisions with other vehicles' future trajectories for replanning and collision avoidance.

Collision Avoidance Object Recognition +2

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