  Learning object affordances is an effective tool in the field of robot
learning. While the data-driven models investigate affordances of single or
paired objects, there is a gap in the exploration of affordances of compound
objects composed of an arbitrary number of objects. We propose the Multi-Object
Graph Affordance Network which models complex compound object affordances by
learning the outcomes of robot actions that facilitate interactions between an
object and a compound. Given the depth images of the objects, the object
features are extracted via convolution operations and encoded in the nodes of
graph neural networks. Graph convolution operations are used to encode the
state of the compounds, which are used as input to decoders to predict the
outcome of the object-compound interactions. After learning the compound object
affordances, given different tasks, the learned outcome predictors are used to
plan sequences of stack actions that involve stacking objects on top of each
other, inserting smaller objects into larger containers and passing through
ring-like objects through poles. We showed that our system successfully modeled
the affordances of compound objects that include concave and convex objects, in
both simulated and real-world environments. We benchmarked our system with a
baseline model to highlight its advantages.
