1 code implementation • 9 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.
2 code implementations • 14 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.
no code implementations • 13 Dec 2018 • Hyung-Jin Yoon, Huaiyu Chen, Kehan Long, Heling Zhang, Aditya Gahlawat, Donghwan Lee, Naira Hovakimyan
The encoding is useful for sharing local visual observations with other agents under communication resource constraints.
no code implementations • 5 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.
no code implementations • 8 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.
no code implementations • 28 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.
no code implementations • 30 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.
no code implementations • 18 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.
no code implementations • 21 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.
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.
no code implementations • 15 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.
no code implementations • 14 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.
no code implementations • 21 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.