  Embodied AI is an inevitable trend that emphasizes the interaction between
intelligent entities and the real world, with broad applications in Robotics,
especially target-driven navigation. This task requires the robot to find an
object of a certain category efficiently in an unknown domestic environment.
Recent works focus on exploiting layout relationships by graph neural networks
(GNNs). However, most of them obtain robot actions directly from observations
in an end-to-end manner via an incomplete relation graph, which is not
interpretable and reliable. We decouple this task and propose ReVoLT, a
hierarchical framework: (a) an object detection visual front-end, (b) a
high-level reasoner (infers semantic sub-goals), (c) an intermediate-level
planner (computes geometrical positions), and (d) a low-level controller
(executes actions). ReVoLT operates with a multi-layer semantic-spatial
topological graph. The reasoner uses multiform structured relations as priors,
which are obtained from combinatorial relation extraction networks composed of
unsupervised GraphSAGE, GCN, and GraphRNN-based Region Rollout. The reasoner
performs with Upper Confidence Bound for Tree (UCT) to infer semantic
sub-goals, accounting for trade-offs between exploitation (depth-first
searching) and exploration (regretting). The lightweight intermediate-level
planner generates instantaneous spatial sub-goal locations via an online
constructed Voronoi local graph. The simulation experiments demonstrate that
our framework achieves better performance in the target-driven navigation tasks
and generalizes well, which has an 80% improvement compared to the existing
state-of-the-art method. The code and result video will be released at
https://ventusff.github.io/ReVoLT-website/.
