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Found 8 papers, 2 papers with code
GUTS: Generalized Uncertainty-Aware Thompson Sampling for Multi-Agent Active Search
We conduct field tests using our multi-robot system in an unstructured environment with a search area of approximately 75, 000 sq.
Cost Aware Asynchronous Multi-Agent Active Search
Multi-agent active search requires autonomous agents to choose sensing actions that efficiently locate targets.
Multi-Agent Active Search using Detection and Location Uncertainty
We then build a decision making algorithm on this inference method that uses Thompson sampling to enable decentralized multi-agent active search.
Multi-Agent Active Search using Realistic Depth-Aware Noise Model
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.
Optimal Data Detection and Signal Estimation in Systems with Input Noise
Practical systems often suffer from hardware impairments that already appear during signal generation.
Asynchronous Multi Agent Active Search
Active search refers to the problem of efficiently locating targets in an unknown environment by actively making data-collection decisions, and has many applications including detecting gas leaks, radiation sources or human survivors of disasters using aerial and/or ground robots (agents).
MSE-Optimal Neural Network Initialization via Layer Fusion
To address this issue, a variety of methods that rely on random parameter initialization or knowledge distillation have been proposed in the past.
Linear Spectral Estimators and an Application to Phase Retrieval
Phase retrieval refers to the problem of recovering real- or complex-valued vectors from magnitude measurements.
