Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Tadiparthi, Vaishnav"'
Autor:
Lin, Muhan, Shi, Shuyang, Guo, Yue, Chalaki, Behdad, Tadiparthi, Vaishnav, Pari, Ehsan Moradi, Stepputtis, Simon, Campbell, Joseph, Sycara, Katia
The correct specification of reward models is a well-known challenge in reinforcement learning. Hand-crafted reward functions often lead to inefficient or suboptimal policies and may not be aligned with user values. Reinforcement learning from human
Externí odkaz:
http://arxiv.org/abs/2410.17389
Autor:
Li, Huao, Mahjoub, Hossein Nourkhiz, Chalaki, Behdad, Tadiparthi, Vaishnav, Lee, Kwonjoon, Moradi-Pari, Ehsan, Lewis, Charles Michael, Sycara, Katia P
Multi-Agent Reinforcement Learning (MARL) methods have shown promise in enabling agents to learn a shared communication protocol from scratch and accomplish challenging team tasks. However, the learned language is usually not interpretable to humans
Externí odkaz:
http://arxiv.org/abs/2409.17348
Autor:
Abughaida, Amer, Gandhi, Meet, Heo, Jun, Tadiparthi, Vaishnav, Sakamoto, Yosuke, Woo, Joohyun, Bae, Sangjae
Automated docking technologies of marine boats have been enlightened by an increasing number of literature. This paper contributes to the literature by proposing a mathematical framework that automates "trailer loading" in the presence of wind distur
Externí odkaz:
http://arxiv.org/abs/2405.05426
Autor:
Le, Viet-Anh, Tadiparthi, Vaishnav, Chalaki, Behdad, Mahjoub, Hossein Nourkhiz, D'sa, Jovin, Moradi-Pari, Ehsan, Malikopoulos, Andreas A.
In this paper, we develop a control framework for the coordination of multiple robots as they navigate through crowded environments. Our framework comprises of a local model predictive control (MPC) for each robot and a social long short-term memory
Externí odkaz:
http://arxiv.org/abs/2310.06964
Autor:
Le, Viet-Anh, Chalaki, Behdad, Tadiparthi, Vaishnav, Mahjoub, Hossein Nourkhiz, D'sa, Jovin, Moradi-Pari, Ehsan
Crowd navigation has received increasing attention from researchers over the last few decades, resulting in the emergence of numerous approaches aimed at addressing this problem to date. Our proposed approach couples agent motion prediction and plann
Externí odkaz:
http://arxiv.org/abs/2309.16838
Autor:
Chalaki, Behdad, Tadiparthi, Vaishnav, Mahjoub, Hossein Nourkhiz, D'sa, Jovin, Moradi-Pari, Ehsan, Armijos, Andres S. Chavez, Li, Anni, Cassandras, Christos G.
Publikováno v:
IEEE Control Systems Letters, vol. 7, pp. 1766-1771, 2023
A lane-change maneuver on a congested highway could be severely disruptive or even infeasible without the cooperation of neighboring cars. However, cooperation with other vehicles does not guarantee that the performed maneuver will not have a negativ
Externí odkaz:
http://arxiv.org/abs/2303.05991
Autor:
Armijos, Andres S. Chavez, Li, Anni, Cassandras, Christos G., Al-Nadawi, Yasir K., Araki, Hidekazu, Chalaki, Behdad, Moradi-Pari, Ehsan, Mahjoub, Hossein Nourkhiz, Tadiparthi, Vaishnav
We derive optimal control policies for a Connected Automated Vehicle (CAV) and cooperating neighboring CAVs to carry out a lane change maneuver consisting of a longitudinal phase where the CAV properly positions itself relative to the cooperating nei
Externí odkaz:
http://arxiv.org/abs/2211.08636
Autor:
Chavez Armijos, Andres S., Li, Anni, Cassandras, Christos G., Al-Nadawi, Yasir K., Araki, Hidekazu, Chalaki, Behdad, Moradi-Pari, Ehsan, Mahjoub, Hossein Nourkhiz, Tadiparthi, Vaishnav
Publikováno v:
In Automatica July 2024 165
In this paper, we propose a refinement strategy to the well-known Physics-Informed Neural Networks (PINNs) for solving partial differential equations (PDEs) based on the concept of Optimal Transport (OT). Conventional black-box PINNs solvers have bee
Externí odkaz:
http://arxiv.org/abs/2105.12307
This paper presents a Lagrangian approach to simulating multibody dynamics in a tensegrity framework with an ability to tackle holonomic constraint violations in an energy-preserving scheme. Governing equations are described using non-minimum coordin
Externí odkaz:
http://arxiv.org/abs/2008.13772