Summary
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Humans are fast, precise, and efficient explorers of their spatial environment. The exploration and
classification takes place in an ongoing action-perception cycle that is based on the continuous
interplay of sensory information processing and goal-directed motor actions.
However, in spite of
the generally accepted importance of motor actions for perception, almost all representational
models of the environment are exclusively based on static spatial descriptions, such as spatial
schemata, maps (e.g. grid-based or geometric approaches), route graphs or qualitative spatial
representations (e.g. topological representations). We question the conceptual separation between
(dynamic) actions and (static) spatial representations and postulate an inherently sensorimotor
representation that comprises sensory as well as motor aspects, often in an inseparable fashion.
Our goal is the development of a hierarchical sensorimotor representation and
its use in a biologically inspired system for exploratory self-localization
and spatial navigation.
The sensorimotor representation is motivated by the human action-perception
cycle with its interplay of sensory information processing and goal-directed
motor actions. This concept is investigated by (i) theoretical research, (ii) psychological experiments, and (iii) modeling approaches. The latter will be
implemented in a mobile agent that operates in a VR environment, and in a
mobile robot.
In our theoretical research we will develop data
structures for spatial scene representation that are suited for integrating sensory features and motor
actions. We will investigate different levels of sensorimotor representations, from low-level to
cognitive and experience-based levels. The concepts will be compared and related to known spatial
representation schemes, including conceptions for mental spatial representation. In pilot studies
with human subjects, we will investigate performance and typical errors in navigational tasks with
respect to their dependence on motor actions and "Sensorimotor coherence" of the input. The results
will provide first qualitative information about the role of motor actions in human representation
and exploration of spatial configurations. In the system implementation, we will investigate the
integration of the sensorimotor representation with a top-down knowledge-based strategy for
efficient exploratory reasoning. This architecture will be implemented in a simulated spatial
exploration system, in a robot head, and in the mobile robot used in A6-[ReactiveSpace]. These
systems will be tested with respect to the resulting behavior and recognition capabilities. The
theoretical, behavioral, and system approaches will be pursued in continuous interaction, the
empirical results will drive and modify the theoretical work, and vice versa. In the long-term the
sensorimotor representation will be extended towards the inclusion of multi-sensory information
that includes auditory, somatosensory, and proprioceptive information, and towards the integration
of complex high-level programs for motor actions.
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