DeepSelf: Emergence of Event-Predictive Agency in Robots

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.

PI: Prof. Dr. Martin Butz