Learning and Transferring Value Function for Robot Exploration in Subterranean Environments. (arXiv:2204.03140v1 [cs.RO])

In traditional robot exploration methods, the robot usually does not have
prior biases about the environment it is exploring. Thus the robot assigns
equal importance to the goals which leads to insufficient exploration
efficiency. Alternative, often a hand-tuned policy is used to tweak the value
of goals. In this paper, we present a method to learn how "good" some states
are, measured by the state value function, to provide a hint for the robot to
make exploration decisions. We propose …

arxiv exploration learning robot value