Frontiers in neurorobotics
January 1, 2022
Gualtiero Piccinini
61 citations
Situated approaches to cognition—emphasizing embodiment, embeddedness, enaction, and affect—are often seen as opposed to computational and representational views. This paper argues they are deeply intertwined: a neurocomputational account relying on neural representations not only is compatible with situated approaches but requires them at its core. Constructing neural representations with semantic content and learning appropriate computational processes demand tight dynamic interaction between nervous system, body, and environment. Situatedness enables neurocognitive systems to build representations with original semantic content, coordinate neural vehicles with content, make content causally efficacious and determinate enough, represent distal stimuli, and misrepresent. This suggests what is needed to build artifacts with basic cognitive capacities.
Frontiers in neurorobotics
January 1, 2022
Robin L Zebrowski, Eli B Mcgraw
6 citations
Artificial intelligence research often misunderstands social cognition by ignoring the role of interaction itself. Participatory Sense-Making (PSM) provides a way to describe how social interaction works, which is useful for robotics-based artificial general intelligence (AGI). However, PSM struggles to distinguish between living sense-makers and potentially cognitive artificial systems. Sociomorphing addresses this by allowing gradations in how such systems are defined and incorporated into asymmetrical social relationships, avoiding problems of anthropomorphism. Together, PSM and sociomorphing, reconceived beyond social robotics, offer a robust framework for AGI robotics-based approaches.
Frontiers in neurorobotics
January 1, 2022
Vadim Weinstein, Basak Sakcak, Steven M Lavalle
5 citations
The authors develop a mathematical framework for cognitive systems—both artificial and natural—that aligns with enactivism, a philosophical position in cognitive science. They identify five core enactivist tenets and build a model that avoids attributing contentful symbolic representations to agents, instead treating the nervous system, body, and environment as an inseparable whole. The central concept is a sensorimotor system, a special case of a transition system. They introduce the notion of sufficiency as a foundational concept, proving a uniqueness theorem about minimal sufficient refinements, which correspond to an optimal attunement of an organism to its environment. This framework aims to make enactivist ideas accessible to computer scientists, AI researchers, and roboticists, while providing philosophers a mathematical tool for clarifying debates.
Frontiers in neurorobotics
January 1, 2022
Felix M G Woolford, Matthew D Egbert
3 citations
A new robot controller model called an ASM-network, built from adaptive sensorimotor maps, enables a robot to learn object discrimination without explicit representations or external rewards. The model combines a mechanism that generates continuous motor activity from past sensorimotor trajectories with an evaluative mechanism that reinforces trajectories supporting higher-order sensorimotor coordinations. In a minimal cognition task, a single robot learned through random exploration and repetition of supportive trajectories. The results demonstrate that recognizable learning behavior can emerge from enactive principles, adapting based on the internal requirements of the action-generating mechanism.
Frontiers in neurorobotics
January 1, 2020
Verena V Hafner, Pontus Loviken, Antonio Pico Villalpando et al.
1 citation
Body ownership and agency are two main components of the minimal self, traditionally studied in philosophy but now also in cognitive science and robotics. This review argues that mechanisms for developing motor and cognitive skills in robots also lay the foundation for an artificial self. It examines developmental processes of the minimal self in biological systems, transfers those principles to robotics, and suggests metrics for measuring agency and body ownership in an artificial self.
Frontiers in neurorobotics
January 1, 2021
Jonny Lee, Paco Calvo
Plants inspire soft robotics through their non-centralized, modular architecture and highly plastic phenotype. A holistic approach to plant bioinspiration, drawing on plant intelligence and behavior, supports an enactivist perspective that emphasizes embodiment and autonomy. Enactivist autonomy concerns the dynamics of self-producing systems like plants that create a distinction between themselves and their environment, contrasting with a diluted notion of independent operability. This distinction is relevant for evaluating limitations on existing growing robots ("growbots") that depend on external energy and material. Plant-inspired robots serve as a case study for an enactivist approach to intelligence, highlighting non-zoological forms of intelligence embodied in self-organizing, autonomous systems.