  We present a visually grounded hierarchical planning algorithm for
long-horizon manipulation tasks. Our algorithm offers a joint framework of
neuro-symbolic task planning and low-level motion generation conditioned on the
specified goal. At the core of our approach is a two-level scene graph
representation, namely geometric scene graph and symbolic scene graph. This
hierarchical representation serves as a structured, object-centric abstraction
of manipulation scenes. Our model uses graph neural networks to process these
scene graphs for predicting high-level task plans and low-level motions. We
demonstrate that our method scales to long-horizon tasks and generalizes well
to novel task goals. We validate our method in a kitchen storage task in both
physical simulation and the real world. Our experiments show that our method
achieved over 70% success rate and nearly 90% of subgoal completion rate on the
real robot while being four orders of magnitude faster in computation time
compared to standard search-based task-and-motion planner.
