Giulia D´Angelo presents A benchmarking framework for embodied neuromorphic agents

Article:
D’Angelo, Giulia, et al. "A benchmarking framework for embodied neuromorphic
agents." Nature Machine Intelligence (2026).

Abstract:
Enabling robots to swiftly, robustly and efficiently interact with a dynamic
environment remains a key challenge. The robotic community can draw inspiration
from the co-adaptation and synergistic interplay between animals’ brains and
bodies, which underpins embodied intelligence. Soft robots and neuromorphic
technology offer a natural solution for such a challenge, enabling low-power,
material-based and event-driven sensorimotor processing and control that
seamlessly handles the continuous dynamic demands of embodied agents. In this
Perspective, we propose a comprehensive framework for benchmarking neuromorphic
computing (brain) that control soft robots (body), based on a suite of tasks,
essential metrics and a reproducible robotic platform. The goal is to allow
researchers to evaluate their embodied neuromorphic system with a physical
robot, in real-world scenarios. The robotic platform is accessible,
open-source,
modular and scalable, so task complexity can be gradually increased, fostering
a
standardized approach. By coupling metrics with physical implementations, this
framework will drive progress in soft robotics, neuromorphic computing and
embodied intelligence.