Towards a “Brain-Guided” Cognitive Architecture - Bioinspired Approaches for Human-Centric Technologies - page 221

these problems in the robotic field, it is useful to take inspiration from studies in
animal cognition:

￿ Causal and spatial reasoning , namely, identifying useful objects in the environ-
ment that could be exploited, as tools, in the context of the otherwise

unrealizable goal.
￿ Trap tube paradigm , namely, the problem of recovering a piece of food, stored in
a transparent tube, by means of a sticklike object of sufficient length, while
avoiding a trap in the tube.
￿ Tool making : Consider the behavior of “Betty, the Caledonian crow” (Weir
et al. 2002 ; Emery and Clayton 2004 ) when she faced the problem of extracting
a food basket from the bottom of a transparent vertical tube and managed to bend
a piece of metallic wire in such a way to reach and pick up the basket.

Figure 7.6 shows iCub engaged in different kinds of scenarios. In particular, in
these scenarios iCub must learn to push. Why is pushing interesting? As a matter of
fact, this skill has been investigated extensively in studies related to understanding
of “physical causality” in primates and infants (Visalberghi and Tomasello 1997 ;
Whiten et al 2009 ; Addessi et al. 2008 ). It is also known from these studies on
animal behavior that different species are different levels of understanding of the
causality related to this task. In addition to the multiple utilities of the “push/pull”
action itself in the context of assembly operations, what makes it significant is the
sheer range of physical concepts that have to be “learned” and “abstracted” in order
to execute this action successfully in diverse environmental conditions. For exam-
ple, it has to be learned that contact is necessary to push, that object properties
influence “pushability” (balls roll faster than cubes and it does not matter what is
the color of the ball or the cube), that pushing objects gives rise to path of motion in
specific directions (the inverse applies for goal-directed pushing), that pushing can
be used to support grasping and bring objects to proximity (while working on
assembly tasks), and that there can be counterforces that block the pushed object
(similar to a goal keeper in football). The requirement to capture/learn such a wide
range of physical concepts through “playful interactions” of the baby humanoid
with different objects makes this task both interesting and challenging.
Other paradigmatic scenarios can be envisaged, in order to engage iCub in
significant goal-directed activities. One of them is assembling the tallest possible
stack from a set of available objects/toys. This scenario is useful for exploring the
computational architecture necessary to enable the robot to efficiently organize and
use its own episodic memories related to its various experiences of interacting with
different objects, all channelized towards achieving the goal of building the tallest
possible stack. Learning takes place cumulatively with the robot playing with
different combinations of objects (some previously experienced, some novel) and
it goes on in an open-ended fashion. By incrementally exploring and building stacks
with various objects, the robot has to learn about their physical properties and
relations among different objects in the context of creating the tallest stack. Since
the solution itself depends on what objects are available in the “now,” to be
successful multiple episodes of past experiences have to be remembered and