  Reasoning about object grasp affordances allows an autonomous agent to
estimate the most suitable grasp to execute a task. While current approaches
for estimating grasp affordances are effective, their prediction is driven by
hypotheses on visual features rather than an indicator of a proposal's
suitability for an affordance task. Consequently, these works cannot guarantee
any level of performance when executing a task and, in fact, not even ensure
successful task completion. In this work, we present a pipeline for SAGAT based
on prior experiences. We visually detect a grasp affordance region to extract
multiple grasp affordance configuration candidates. Using these candidates, we
forward simulate the outcome of executing the affordance task to analyse the
relation between task outcome and grasp candidates. The relations are ranked by
performance success with a heuristic confidence function and used to build a
library of affordance task experiences. The library is later queried to perform
one-shot transfer estimation of the best grasp configuration on new objects.
Experimental evaluation shows that our method exhibits a significant
performance improvement up to 11.7% against current state-of-the-art methods on
grasp affordance detection. Experiments on a PR2 robotic platform demonstrate
our method's highly reliable deployability to deal with real-world task
affordance problems.
