Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Gupta, Tejus"'
Autor:
Tabib, Wennie, Stecklein, John, McDowell, Caleb, Goel, Kshitij, Jonathan, Felix, Rathod, Abhishek, Kokoski, Meghan, Burkholder, Edsel, Wallace, Brian, Navarro-Serment, Luis Ernesto, Bakshi, Nikhil Angad, Gupta, Tejus, Papernick, Norman, Guttendorf, David, Kahn, Erik E., Kasemer, Jessica, Holdaway, Jesse, Schneider, Jeff
Rapid search and rescue is critical to maximizing survival rates following natural disasters. However, these efforts are challenged by the need to search large disaster zones, lack of reliability in the communications infrastructure, and a priori unk
Externí odkaz:
http://arxiv.org/abs/2410.08507
Robotic solutions for quick disaster response are essential to ensure minimal loss of life, especially when the search area is too dangerous or too vast for human rescuers. We model this problem as an asynchronous multi-agent active-search task where
Externí odkaz:
http://arxiv.org/abs/2304.02075
The current landscape of multi-agent expert imitation is broadly dominated by two families of algorithms - Behavioral Cloning (BC) and Adversarial Imitation Learning (AIL). BC approaches suffer from compounding errors, as they ignore the sequential d
Externí odkaz:
http://arxiv.org/abs/2110.08963
Autor:
Jain, Vidhi, Jena, Rohit, Li, Huao, Gupta, Tejus, Hughes, Dana, Lewis, Michael, Sycara, Katia
In a search and rescue scenario, rescuers may have different knowledge of the environment and strategies for exploration. Understanding what is inside a rescuer's mind will enable an observer agent to proactively assist them with critical information
Externí odkaz:
http://arxiv.org/abs/2011.07656
Imitation learning is well-suited for robotic tasks where it is difficult to directly program the behavior or specify a cost for optimal control. In this work, we propose a method for learning the reward function (and the corresponding policy) to mat
Externí odkaz:
http://arxiv.org/abs/2011.04709
We present an algorithm for computing class-specific universal adversarial perturbations for deep neural networks. Such perturbations can induce misclassification in a large fraction of images of a specific class. Unlike previous methods that use ite
Externí odkaz:
http://arxiv.org/abs/1912.00466