From real-time adaptation to social learning in robot ecosystems

Alex Szorkovszky; Frank Veenstra; Kyrre Glette

doi:10.3389/frobt.2023.1232708

From real-time adaptation to social learning in robot ecosystems

Front Robot AI. 2023 Oct 4:10:1232708. doi: 10.3389/frobt.2023.1232708. eCollection 2023.

Authors

Alex Szorkovszky^{1

2}, Frank Veenstra^{1

2}, Kyrre Glette^{1

2}

Affiliations

¹ RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway.
² Department of Informatics, University of Oslo, Oslo, Norway.

Abstract

While evolutionary robotics can create novel morphologies and controllers that are well-adapted to their environments, learning is still the most efficient way to adapt to changes that occur on shorter time scales. Learning proposals for evolving robots to date have focused on new individuals either learning a controller from scratch, or building on the experience of direct ancestors and/or robots with similar configurations. Here we propose and demonstrate a novel means for social learning of gait patterns, based on sensorimotor synchronization. Using movement patterns of other robots as input can drive nonlinear decentralized controllers such as CPGs into new limit cycles, hence encouraging diversity of movement patterns. Stable autonomous controllers can then be locked in, which we demonstrate using a quasi-Hebbian feedback scheme. We propose that in an ecosystem of robots evolving in a heterogeneous environment, such a scheme may allow for the emergence of generalist task-solvers from a population of specialists.

Keywords: central pattern generator; cultural evolution; entrainment; evolutionary robotics; social learning.

Grants and funding

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 101030688, and is partially supported by the Research Council of Norway through its Centres of Excellence scheme, project number 262762.