Towards a “Brain-Guided” Cognitive Architecture - Bioinspired Approaches for Human-Centric Technologies - page 214

is effective in shaping humanlike capabilities in a humanoid robot? We are cur-
rently investigating these fundamental issues with the help of numerous playful
experiments with iCub that attempt to achieve cumulative development of proce-
dural, semantic, and episodic memories and the parallel development of a brain-
guided computational framework to organize and creatively exploit such learned
knowledge for the realization of goals.
In general, after the tryst with GOFAI, most current research in the field of
cognitive developmental robotics appreciates the fact that “sensorimotor experi-
ence precedes representation” and cognition is gradually bootstrapped through a
cumulative process of learning by interaction (physical and social) within the zone
of proximal development (Vygotsky 1978 ) of the agent. This approach indeed has
roots in Wiener's cybernetics ( 1948 ), Varela and Maturana's autopoiesis ( 1974 ),
Chiel and Beer's neuroethology ( 1997 ), Clark's situatedness ( 1997 ), Hesslow's
simulation hypothesis (Hesslow 2002 ; Hesslow and Jirenhed 2007 ), and
Thompson's enactive cognition ( 2007 ). The obvious reason to pursue this path is
because it is impossible to predict and program at design time every possible
situation in every time instance to which an artifact may be subjected to in the
future. Straight robot programming approaches work for simple machines
performing targeted functions but certainly not for general-purpose robotic com-
panions envisaged to interact with humans in unstructured environments.
Complementing the extrinsic application of specific value, the embodied/enactive
approach is also relevant from an intrinsic viewpoint of understanding our own
selves—understanding how interactions between body and the brain shape the mind
and shape action and reason. This is because in addition to the range of direct
problems typical of conventional physics, which involve computing effects of
forces on objects, brains of animals have also to deal with inverse, typically
ill-posed, problems of learning, reasoning, and choosing actions that would enable
realization of one's goals and hence ultimately survive. Strikingly, many of the
inverse problems faced by the brain to learn, reason, and generate goal-directed
behavior, together with the ability to make predictions inherent with the solution of
direct problems, are indeed analogous to the ones roboticists must solve to make
their robots act cognitively in the real world. At the same time, it is only fair to say
that in spite of extensive research scattered across multiple scientific disciplines and
prevalence of numerous machine learning techniques, the present artificial agents
still lack much of the resourcefulness, purposefulness, flexibility, and adaptability
that biological agents so effortlessly exhibit. Certainly, this points towards the need
to develop novel computational frameworks that go beyond the state of the art and
endow cognitive agents with the capability to learn cumulatively and use past
experience effectively “to connect the dots” when faced with novel situations.
Looking at the incessant loop of gaining experience and using experience,
typical of biological species that exhibit some form of cognition, learning and
reasoning can be seen as foreground and background alternating each other as
intricately depicted in the artistic creations of Escher. In an intriguing work during
the early days of embodied/enactive cognition, Mark Johnson ( 1987 ) playfully
remarked that “we are animals but we are also rational animals,” emphasizing the