Sensorimotor representation learning for an "active self" in robots: A model survey

TL;DR

This paper reviews human sensorimotor development related to self-awareness and surveys robotics models of these processes, proposing a framework for developing an artificial sense of self through self-exploration.

Contribution

It provides a comprehensive survey of human and robotic models of sensorimotor self-representation and introduces a theoretical framework for emergent self-awareness in robots.

Findings

Robotics models partially replicate human body schema and peripersonal space.

Current models lack fully autonomous development of self-awareness.

Proposed framework emphasizes self-exploration for sensory representation development.

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.