ロボットにおける「アクティブ・セルフ」のための感覚運動表現学習：モデルサーベイ
Sensorimotor representation learning for an "active self" in robots: A model survey
人間の身体図式・近体空間・アクティブセルフの発達過程を概観し、対応するロボットモデルを比較した上で、自己探査による感覚表現獲得を通じて人工エージェントに自己感を生じさせる理論的計算枠組みを提案したサーベイ論文。
著者: Phuong D. H. Nguyen, Yasmin Kim Georgie, Ezgi Kayhan, Manfred Eppe, Verena Vanessa Hafner, Stefan Wermter
分類: cs.RO, cs.AI
原文アブストラクト
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.
