Kyriakos G. Vamvoudakis
Georgia Institute of Technology
216 Papers
305 Citations
Kyriakos G. Vamvoudakis is an academic researcher from Georgia Institute of Technology. The author has contributed to research in topics: Computer science & Reinforcement learning. The author has an hindex of 27, co-authored 153 publications. Previous affiliations of Kyriakos G. Vamvoudakis include University of California & University of Texas System.
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Papers
A Compliant, Underactuated Finger for Anthropomorphic Hands
George P. Kontoudis,Minas Liarokapis,Kyriakos G. Vamvoudakis +2 more
- 01 Jun 2019
TL;DR: A compliant, underactuated finger for the development of anthropomorphic robotic and prosthetic hands that achieves both flexion/extension and adduction/abduction on the metacarpophalangeal joint, by using two actuators.
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An Actor–Critic–Identifier Architecture for Adaptive Approximate Optimal Control
Shubhendu Bhasin,Rushikesh Kamalapurkar,Marcus Johnson,Kyriakos G. Vamvoudakis,Frank L. Lewis,Warren E. Dixon +5 more
- 07 Feb 2013
TL;DR: In this paper, an Actor-Critic-Identifier architecture for approximate HJB approximation is presented, along with an actor-critic design convergence and stability analysis simulation.
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Adaptive H∞ Tracking Control of Nonlinear Systems Using Reinforcement Learning
Hamidreza Modares,Bahare Kiumarsi,Kyriakos G. Vamvoudakis,Frank L. Lewis,Frank L. Lewis +4 more
- 01 Jan 2018
TL;DR: This chapter presents online solutions to the optimal H ∞ tracking of nonlinear systems to attenuate the effect of disturbance on the performance of the systems to obviate the requirement of the complete knowledge of the system dynamics.
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Intersection-Traffic Control of Autonomous Vehicles using Newton-Raphson Flows and Barrier Functions
TL;DR: This paper uses a flow version of the Newton-Raphson method for controlling a predicted system-output to a future reference target and guarantees safety specifications by applying to the tracking technique the framework of control barrier functions.
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Bounded rational Dubins vehicle coordination for target tracking using reinforcement learning
TL;DR: In this article , the authors address the problem of cooperative tracking of multiple heterogeneous targets by deploying multiple and heterogeneous pursuers exhibiting different decision-making capabilities, with an evader being the maximizing player and the pursuing team being the minimizing one.
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