Modeling a Robot’s Peripersonal Space and Body Schema for Adaptive Learning and Imitation (MoReSpace)

The DFG-funded project MoReSpace concentrates on research, design, development and evaluation of neurocomputational deep learning methods for intelligent robot assistants to explore human-robot interaction. In particular the project includes research into modeling a robot’s peripersonal space and body schema for adaptive learning and imitation. Our research questions focus on a) adaptive decision-making with conflicting sensations and b) on self-other transfer and imitation learning. We will develop a novel conflict-driven attention mechanism by considering psychological phenomena that involve conflicting sensations. Furthermore, we will develop learning from observation by developing a projection mechanism to map an observer’s body schema to an observed agent.

We expect the resulting framework to improve the capabilities of robotic agents to handle conflicting sensor data and to improve human-robot interaction scenarios in the context of the different morphologies of the NICO and NICOL robots. Our experiments will take place in a table-top scenario and mainly involve object manipulation experiments, including block-stacking and tool-use tasks. Together with our collaborators, we will first conduct robot-robot interaction and later human-robot-interaction experiments.

The MoReSpace project is funded by the Deutsche Forschungsgemeinschaft as part of the SPP “The active self”.

PI: Prof. Dr. Stefan Wermter
Associates: Josua Spisak, Dr. Matthias Kerzel, Dipl.-Ing. Erik Strahl, Dr. Cornelius Weber