Year: 2025
Type: article
Abstract: The resilience and flexibility of intelligent robotic solutions that are currently being developed are still insufficient, despite the dramatic rate of innovation in robotics and artificial intelligence. Their comparison with the naturally evolved intelligent physical agents that we can observe in nature (such as animals and plants) still shows enormous gaps in terms of adaptivity, dexterity and flexibility. We believe that to get robotic platforms that can truly function well in complex open-ended real-world applications, we need a paradigm change from the top-down 'Cartesian' (assuming a neat division in between mind and body) mechatronic paradigm to a deeply bioinspired paradigm leveraging on morphological computation and emergence. Although learning techniques are helpful, real-time learning from data-rich time series, interaction with an unstructured environment, and collaboration with humans-who then learn alongside the intelligent service robots-needs novel algorithms and systems. The main thesis is that while "computing" is a feature of all material systems, intelligence and meaning are created by synchronization processes among networks of independent agents that are "situated" in their surroundings. Investigating the primary unresolved issues in the field of evolutionary emergent information processing in multibody (rigid, compliant, or hybrid) systems is the goal of our research project. NOTE: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.
Authors: Enrica Zereik, Luca Zanetti, Fabio Bonsignorio
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,645
OpenAlex ID: W4415221900