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An Active Parameter Learning Approach to The Identification of Safe Regions by Raghavan, Aneesh; Johansson, Karl H is a scholarly article available to read on EtoBox.

What is An Active Parameter Learning Approach to The Identification of Safe Regions about?

We consider the problem of identification of safe regions in the environment of an autonomous system. The environment is divided into a finite collections of Voronoi cells, with each cell having a representative, the Voronoi center. The extent to which each region is considered to be safe by an oracle is captured through a trust distribution. The trust placed by the oracle conditioned on the region is modeled through a Bernoulli distribution whose the parameter depends on the region. The parameters are unknown to the system. However, if the agent were to visit a given region, it will receive a binary valued random response from the oracle on whether the oracle trusts the region or not. The objective is to design a path for the agent where, by traversing through the centers of the cells, the agent is eventually able to label each cell safe or unsafe. To this end, we formulate an active parameter learning problem with the objective of minimizing visits or stays in potentially unsafe regions. The active learning problem is formulated as a finite horizon stochastic control problem where the cost function is derived utilizing the large deviations principle (LDP). The challenges associat

Author

Raghavan, Aneesh; Johansson, Karl H

Published

2024

Language

EN