6 accepted papers
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 each robot aims to efficiently seek objects of in…
Active search, in applications like environment monitoring or disaster response missions, involves autonomous agents detecting targets in a search space using decision making algorithms that adapt to the history of their observations. Active search algorithms must contend with two types of uncertain…
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
Active search refers to the problem of efficiently locating targets in an unknown environment by actively making data-collection decisions. In this paper, we are focusing on multiple aerial robots (agents) detecting targets such as gas leaks, radiation sources or human survivors of disasters. One of…
The active search for objects of interest in an unknown environment has many robotics applications including search and rescue, detecting gas leaks or locating animal poachers. Existing algorithms often prioritize the location accuracy of objects of interest while other practical issues such as the…
Phase retrieval refers to the problem of recovering real- or complex-valued vectors from magnitude measurements. The best-known algorithms for this problem are iterative in nature and rely on so-called spectral initializers that provide accurate initialization vectors. We propose a novel class of es…