Development via Information Self-structuring of Sensorimotor Experience and Interaction

Abstract. We describe how current work in Artificial Intelligence is using rigorous tools from information theory, namely information distance and experience distance to organize the self-structuring of sensorimotor perception, motor control, and experiential episodes with extended temporal horizon. Experience is operationalized from an embodied agent’s own perspective as the flow of values taken by its sensors and effectors (and possibly other internal variables) over a temporal window. Such methods allow an embodied agent to acquire the sensorimotor fields and control structure of its own body, and are being applied to pursue autonomous scaffolded proximal development in the zone between the familiar experience and the unknown. 1 Introduction: Information Self-structuring in Ontogeny Modern Artificial Intelligence (AI) research has increasingly focused on adaptive, embodied agents with rich sensing capabilities situated in complex environments, that develop in their capabilities over the course of their “lifetimes ” (ontogeny

Robot Self-Characterisation of Experience Using Trajectories in Sensory-Motor Phase Space

We describe sensorimotor phase-plots constructed using information theoretical methods from raw sensor data as a way for a robotic agent to characterise its interactions and interaction history. Measurements of the position and shape of the trajectories, including fractal dimension, can be used to characterise the agent-environment interaction. 1

Robotic Control Architectures

Abstract — We describe an enactive, situated model of interaction history based around a growing, informationally selfstructured metric space of experience that is constructed and reconstructed as the robot engages in sensorimotor interactions with objects and people in its environment. The model shows aspects of development and learning through modification of the cognitive structure that forms the basis for action selection as a result of acting in the world. We describe robotic experiments showing prediction of the path of a ball and an interaction game “peekaboo”

Anticipating Future Experience using Grounded Sensorimotor Informational Relationships

Operational definitions and applications of the sensorimotor experience of an artificial embodied organism are presented along with a mathematical metric for distance between experiences based on Shannon information. We describe a simple robotic experiment that illustrates how an artificial embodied agent can use its own history of experience combined with the experience metric to predict future experience. Present sensorimotor experience is used to find the most similar past experience using the geometry of its growing and changing experience metric space. This is then used to ground the ontogeny of autonomous prospective capability in interacting with the environment, e.g. to anticipate forthcoming changes in environment based on temporally extended past experiences