3 accepted papers
Metric occupancy maps are widely used in autonomous robot navigation systems. However, when a robot is deployed in an unseen environment, building an accurate metric map is time-consuming. Can an autonomous robot directly navigate in previously unseen environments using coarse maps? In this work, we…
In robot navigation, generalizing quickly to unseen environments is essential. Hierarchical methods inspired by human navigation have been proposed, typically consisting of a high-level landmark proposer and a low-level controller. However, these methods either require precise high-level information
We address the problem of finding representative points of datasets by learning from multiple datasets and their ground-truth summaries. We develop a supervised subset selection framework, based on the facility location utility function, which learns to map datasets to their ground-truth representat…