| Summary: | Parsing of action sequences is the process of
segmenting observed behavior into individual actions. In
robotics, this process is critical for imitation learning from
observation and for representing an observed behavior in a
form that may be communicated to a human. In this paper,
we develop a model for action parsing, based on our understanding
of principles of grounded cognitive processes,
such as perceptual decision making, behavioral organization,
and memory formation.We present a neural-dynamic
architecture, in which action sequences are parsed using
a mathematical and conceptual framework for embodied
cognition—the Dynamic Field Theory. In this framework,
we introduce a novel mechanism, which allows us to detect
and memorize actions that are extended in time and
are parametrized by the target object of an action. The core
properties of the architecture are demonstrated in a set of
simple, proof-of-concept experiments.
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