This paper introduces a developmental approach to
learning the binding affordances of objects by a robot.
A behavior-based framework is used to ground the affordance
representation in the behavioral repertoire of
the robot. The affordances are learned during a behavioral
babbling stage in which the robot randomly
chooses sequences of exploratory behaviors, applies
them to the objects, and detects invariants in the resulting
set of observations. The invariants are calculated
relative to the robot’s body. The approach was implemented
and tested in a dynamics robot simulator.
@InProceedings{Stoytchev2005, author = {Alexander Stoytchev}, title = {Toward Learning the Binding Affordances of Objects: A Behavior-Grounded Approach}, booktitle = {Proceedings of AAAI Symposium on Developmental Robotics}, year = {2005}, pages = {17-22}, address = {Stanford University}, month = {Mar 21-23}, }
The videos show the same sequence of exploratory behaviors executed on three different objects.
Binding with an H-frame object using the open-gripper behavior.