DeepSelf: Emergence of Event-Predictive Agency in Robots

01/04/2022 to 31/03/2025

The “experience of controlling one’s own actions, and, through them,
events in the outside world” (Haggard & Chambon 2012) lies at the
heart of the Sense of Agency. While forms of agency may be found in
direct encodings of sensorimotor experiences, we propose that more
explicit, accessible forms require abstractions away from the actual
sensorimotor dynamics, i.e. events. The result may be called an
agentive self, which can become ‘aware’ of its own experiences as
well as the consequences of its actions in the world. We aim at
revealing critical computational components, including learning and
processing biases, for the development of an agentive self in robots.
Over the three years, we aim at first modeling spatial action-effect
binding, to implement a simple form of agency. We will then enhance
the architecture to model event-effect anticipations, focusing on the
anticipatory crossmodal congruency paradigm, which shows how our
minds project our body parts onto future positions before even starting
to execute the required motion to reach the position. Finally, we will
tackle tool-mediated event-effect anticipations, which we expect to
first show in experiments with human participants. Our computational
model takes ideomotor theory, comparator models, and the free
energy principle (active inference) as the point of departure. Over
recent years, including research work within the SPP’s first funding
period, we have implemented these principles in various artificial
systems and robots. Our deep active inference model enables robots
to learn generative models from continuous raw sensory information
and to plan in a model-predictive manner. Furthermore, inspired by
our contribution to theories of event-predictive cognition, we have also
implemented event-predictive systems, which convert relative
distances and orientations, into event encodings, enabling agents to
plan goal-directly on an event scale. By combining our expertise in
adaptive robotics and deep artificial neural networks (Donders) with
our expertise in experimental cognitive psychology and neurocognitive
modeling (Tübingen), we aim to isolate “the mechanisms
and prerequisites that allow an [artificial] agent to develop a self” and
the scrutinization of the “roles of agency”, fostering the development
of more effective event control. Moreover, we expect to identify core
mechanisms of self-plasticity in tool-use. Meanwhile, we envisage
improving the robot’s agentive processing abilities via the
development of compact event-predictive encodings. Beyond the
actual project, we expect to contribute to systems that can explain
their influence on the environment and that learn to identify its
causality. While we will focus on individual robots in this project, we
hope that the realization of an agentive self will also facility the
development of social interaction competencies – considerations
which we hope to discuss with other SPP projects during the second
funding phase.