  This paper introduces an automatic affordance reasoning paradigm tailored to
minimal semantic inputs, addressing the critical challenges of classifying and
manipulating unseen classes of objects in household settings. Inspired by human
cognitive processes, our method integrates generative language models and
physics-based simulators to foster analytical thinking and creative imagination
of novel affordances. Structured with a tripartite framework consisting of
analysis, imagination, and evaluation, our system "analyzes" the requested
affordance names into interaction-based definitions, "imagines" the virtual
scenarios, and "evaluates" the object affordance. If an object is recognized as
possessing the requested affordance, our method also predicts the optimal pose
for such functionality, and how a potential user can interact with it. Tuned on
only a few synthetic examples across 3 affordance classes, our pipeline
achieves a very high success rate on affordance classification and functional
pose prediction of 8 classes of novel objects, outperforming learning-based
baselines. Validation through real robot manipulating experiments demonstrates
the practical applicability of the imagined user interaction, showcasing the
system's ability to independently conceptualize unseen affordances and interact
with new objects and scenarios in everyday settings.
