5 accepted papers
This work presents amortized path-integral policies that enable efficient and real-time active search for robotic systems. We model search as an active sensing problem where agents select actions to maximize information about target locations. Unlike previous approaches that only consider informatio…
Recent work in Bayesian Experiment Design (BED) has shown the value of using Deep Learning (DL) to obtain highly efficient adaptive experiment designs. In this paper, we argue that a central bottleneck of DL training for BED is belief explosion. Specifically, as an agent progresses deeper into an ex…
The successful application of robotic control requires intelligent decision-making to handle the long tail of complex scenarios that arise in real-world environments. Recently, Deep Reinforcement Learning (DRL) has provided a data-driven framework to automatically learn effective policies in such co…
In human-interactive applications of online learning, a human's preferences or abilities are often a function of the algorithm's recent actions. Motivated by this, a significant line of work has formalized settings where an action's loss is a function of the number of times it was played in the prio…
Multi-Agent Active Search (MAAS) is an active learning problem with the objective of locating sparse targets in an unknown environment by actively making data-collection decisions. Recently proposed algorithms, although well-motivated from a theoretical perspective, are limited in three key ways: th