5 citations · 15 across the 6 of their papers we have counts for
7 papers
Bayesian Optimization with Rich Auxiliary Information via LLMs
Tejus Gupta, Efe Mert Karagözlü, Rohit Sonker +2
Bayesian Optimization (BO) is widely used for optimizing expensive black-box functions, yet it typically reduces each expensive experiment to an input and its objective value, disc…
Decentralized Uncertainty-Aware Active Search with a Team of Aerial Robots
Wennie Tabib, John Stecklein, Caleb McDowell +15
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…
GUTS: Generalized Uncertainty-Aware Thompson Sampling for Multi-Agent Active Search
Nikhil Angad Bakshi, Tejus Gupta, Ramina Ghods +1
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 mode…
SS-MAIL: Self-Supervised Multi-Agent Imitation Learning
Akshay Dharmavaram, Tejus Gupta, Jiachen Li +1
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 appr…
f-IRL: Inverse Reinforcement Learning via State Marginal Matching
Tianwei Ni, Harshit Sikchi, Yufei Wang +3
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…
Predicting Human Strategies in Simulated Search and Rescue Task
Vidhi Jain, Rohit Jena, Huao Li +4
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…