Sensorimotor representation learning for an "active self" in robots: A
model survey
- URL: http://arxiv.org/abs/2011.12860v2
- Date: Tue, 12 Jan 2021 14:03:40 GMT
- Title: Sensorimotor representation learning for an "active self" in robots: A
model survey
- Authors: Phuong D.H. Nguyen, Yasmin Kim Georgie, Ezgi Kayhan, Manfred Eppe,
Verena Vanessa Hafner, and Stefan Wermter
- Abstract summary: In humans, these capabilities are thought to be related to our ability to perceive our body in space.
This paper reviews the developmental processes of underlying mechanisms of these abilities.
We propose a theoretical computational framework, which aims to allow the emergence of the sense of self in artificial agents.
- Score: 10.649413494649293
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Safe human-robot interactions require robots to be able to learn how to
behave appropriately in \sout{humans' world} \rev{spaces populated by people}
and thus to cope with the challenges posed by our dynamic and unstructured
environment, rather than being provided a rigid set of rules for operations. In
humans, these capabilities are thought to be related to our ability to perceive
our body in space, sensing the location of our limbs during movement, being
aware of other objects and agents, and controlling our body parts to interact
with them intentionally. Toward the next generation of robots with bio-inspired
capacities, in this paper, we first review the developmental processes of
underlying mechanisms of these abilities: The sensory representations of body
schema, peripersonal space, and the active self in humans. Second, we provide a
survey of robotics models of these sensory representations and robotics models
of the self; and we compare these models with the human counterparts. Finally,
we analyse what is missing from these robotics models and propose a theoretical
computational framework, which aims to allow the emergence of the sense of self
in artificial agents by developing sensory representations through
self-exploration.
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