Goal Oriented Behavior With a Habit-Based Adaptive Sensorimotor Map Network.
Felix M G Woolford, Matthew D Egbert
Frontiers in neurorobotics January 1, 2022 Peer reviewed DOI: 10.3389/fnbot.2022.846693 via PubMed
Summary
A new robot controller model, the ASM-network, uses networks of adaptive sensorimotor maps to generate behavior based on enactive cognition principles. It combines a mechanism for continuous motor activity from historical sensorimotor trajectories with an evaluative mechanism that reinforces or weakens those trajectories based on their support of higher-order sensorimotor coordinations. In a minimal cognition task involving object discrimination, a single robot learned through random exploration and repetition of historic trajectories that supported a pre-given network of coordinations. The robot displayed recognizable learning without explicit representations or extraneous fitness variables.
Study at a glance
| Design | experimental study |
|---|---|
| Sample size | 1 |
| Population | robot |
| Key finding | A robot using the ASM-network model can learn an object discrimination task through random exploration and repetition of historic trajectories that support a pre-given network of sensorimotor coordinations, without explicit representations or extraneous fitness variables. |
Abstract
We present a description of an ASM-network, a new habit-based robot controller model consisting of a network of adaptive sensorimotor maps. This model draws upon recent theoretical developments in enactive cognition concerning habit and agency at the sensorimotor level. It aims to provide a platform for experimental investigation into the relationship between networked organizations of habits and cognitive behavior. It does this by combining (1) a basic mechanism of generating continuous motor activity as a function of historical sensorimotor trajectories with (2) an evaluative mechanism which reinforces or weakens those historical trajectories as a function of their support of a higher-order structure of higher-order sensorimotor coordinations. After describing the model, we then present the results of applying this model in the context of a well-known minimal cognition task involving object discrimination. In our version of this experiment, an individual robot is able to learn the task through a combination of exploration through random movements and repetition of historic trajectories which support the structure of a pre-given network of sensorimotor coordinations. The experimental results illustrate how, utilizing enactive principles, a robot can display recognizable learning behavior without explicit representational mechanisms or extraneous fitness variables. Instead, our model's behavior adapts according to the internal requirements of the action-generating mechanism itself.