Learning and Generalization of Behavior-Grounded
Tool Affordances
Jivko Sinapov
Department of Computer Science
Iowa State University
jsinapov@cs.iastate.edu
Alexander Stoytchev
Department of Computer Science
Iowa State University
alex@cs.iastate.edu
Abstract—This paper describes an approach which a robot
can use to learn the effects of its actions with a tool, as well
as identify which frames of reference are useful for predicting
these effects. The robot learns the tool representation during
a behavioral babbling stage in which it randomly explores the
space of its actions and perceives their effects. The experimental
results show that the robot is able to learn a compact and
accurate model of how its tool actions would affect the position
of a target object. Furthermore, the model learned by the
robot can generalize and perform well even with tools that the
robot has never seen before. Experiments were conducted in
a dynamics robot simulator. Two different learning algorithms
and five different frames of reference were evaluated based on
their generalization performance.
Index Terms—Developmental Robotics, Affordances, Learn-
ing of Affordances, Tool Affordances.
I. INTRODUCTION
The term affordance was coined by J. J. Gibson who paid
special attention in his research to environmental objects and
the types of properties that living organisms perceive about
them. Gibson defines affordances as action possibilities that
are present in the environment and can be perceived by an
individual [1]. In the context of tool use, the affordances
of a tool correspond to the possible uses that the tool
affords to an organism. Furthermore, Gibson also argues that
humans naturally perceive affordances when they encounter
environmental objects [1].
Similar observations have been made by Jean Piaget [2]
who formulated one of the most influential developmental
theories of the 20-th century. According to Piaget environ-
mental objects play an important role in human development.
In the early stages of development, the actions of a child are
directed at objects with the goal of establishing associations
between its actions and outcomes in the external world [2]. In
the terminology of Gibson this would be equivalent to trying
to learn the affordances of the objects. Similar mechanisms
are at play when the child learns to use tools [2].
In previous work, Stoytchev [3] introduced a compu-
tational framework for a behavior-grounded representation
of tools. This paper extends that model by describing an
approach which a robot can use to learn the effects of its
actions with a tool, as well as detect the features in its
sensory stream which are useful for predicting these effects.
The learned model constitutes a compact representation of
the action possibilities that the tool affords the robot. The
robot learns the representation by exploring the space of
its actions with the tool and observing their effects. The
predictive model is grounded in the robot’s own perceptual
and behavioral repertoire, allowing the robot to autonomously
test and verify its knowledge of the tool. In addition, the
framework presented in this paper allows the robot to evaluate
the predictive power of different frames of reference. There is
evidence that biological brains maintain multiple coordinate
frames in order to coordinate bodily movements [4], [5].
Gallistel, for example, suggests that intelligent behavior is
about learning how to coordinate these frames [5].
Once a model is learned, the robot is tested on how well
it can predict the consequences of its actions using this
model. Furthermore, the generalization abilities of the learned
models are evaluated. In abstract terms, generalization can
be defined as the ability to correctly estimate the values of a
given function at points in its input space for which data is
not available [6]. In this paper, this function is a model for
the observable consequences of executing a particular action
with a given tool. Different learning algorithms and frames
of reference are evaluated in the course of the experiments.
II. RELATED WORK
A. Tool Experiments with Animals
Tool-using experiments have been used for the last 90 years
to test the intelligence of primates [7], [8], [9]. Wolfgang
Ko¨hler [7] conducted one of the first systematic studies of
tool-using behaviors in primates. His experiments required
the use of tools such as sticks, hooks, boxes, and ladders in
order to complete a given task and extract a reward in the
form of food.
Povinelli et al. [8] replicated many of the experiments
conducted by Ko¨hler and analyzed the results using statistical
techniques. They concluded that chimpanzees do not reason
about actions and tool tasks in abstract terms such as mass,
force, or gravity. Another conclusion was that the primates
solved tool-using tasks by extracting simple rules from their
experience, e.g., “contact leads to movement” [8].