Hierarchical Planning for Long-Horizon Manipulation with Geometric and Symbolic Scene Graphs
This work addresses the problem of efficient and robust long-horizon manipulation for robotic systems, offering significant speed improvements for robot control.
This paper introduces a hierarchical planning algorithm for long-horizon manipulation tasks, utilizing a two-level scene graph representation (geometric and symbolic). The method achieved over 70% success and nearly 90% subgoal completion on a real robot in a kitchen storage task, while being four orders of magnitude faster than standard planners.
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.