Planning for Multi-Object Manipulation with Graph Neural Network Relational Classifiers

Understand

Objects rarely sit in isolation in human environments.

• As such, we'd like our robots to reason about how multiple objects relate to one another and how those relations may change as the robot interacts with the world.
• To this end, we propose a novel graph neural network framework for multi-object manipulation to predict how inter-object relations change given robot actions.
• Our model operates on partial-view point clouds and can reason about multiple objects dynamically interacting during the manipulation.