Behavior-Grounded Representation of Tool Affordances
Abstract
This paper introduces a novel approach to representing
and learning tool affordances by a robot. The tool
representation described here uses a behavior-based approach
to ground the tool affordances in the behavioral repertoire of
the robot. The representation is learned during a behavioral
babbling stage in which the robot randomly chooses different
exploratory behaviors, applies them to the tool, and observes
their effects on environmental objects. The paper shows how
the autonomously learned affordance representation can be
used to solve tool-using tasks by dynamically sequencing the
exploratory behaviors based on their expected outcomes. The
quality of the learned representation was tested on extension-of-reach tool-using tasks.