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A benchmarking framework for embodied neuromorphic agents
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DOI：10.1038/s42256-026-01197-w.png)
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Abstract
En 中文
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. Combining soft robotics with neuromorphic engineering is a promising approach in embodied intelligence. Giulia d’Angelo et al. contribute to progress in this field by developing a framework for benchmarking neuromorphic controllers on soft robotic platforms.
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Energy science and technology
Engineering
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