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  • Perspective
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A benchmarking framework for embodied neuromorphic agents

Nature Machine Intelligence volume 8, pages 300–312 (2026)Cite this article

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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.

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Fig. 1: A system overview of embodied soft robotics.
Fig. 2: AB-Platform.

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Code availability

All instructions necessary to build the robot, including CAD files, Gerber files and the complete list of components, are available at the following repository: https://github.com/ActiveBraid/ActiveBraidCrawler. This repository provides everything required for replication and benchmarking, ensuring accessibility and reproducibility for the research community.

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Acknowledgements

G.D’A. acknowledges the financial support from the European Union’s HORIZON-MSCA-2023-PF-01-01 research and innovation programme under the Marie Skłodowska-Curie grant agreement ENDEAVOR 101149664. J.E.P acknowledges the financial support of NeuroPAC under the NSF grant ‘AccelNet: Accelerating Research on Neuromorphic Perception, Action, and Cognition’. C.B. acknowledges the financial support of the National Biodiversity Future Center funded under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4—Call for tender no. 3138 of 16 December 2021, rectified by Decree n.3175 of 18 December 2021 of Italian Ministry of University and Research funded by the European Union–NextGenerationEU, and the PNRR MUR Project PE000013 ‘Future Artificial Intelligence Research (hereafter FAIR)’, funded by the European Union–NextGenerationEU. C.D.L. acknowledges the financial support of the Bridge Fellowship funded by the Digital Society Initiative at University of Zurich (grant G-95017-01-12). M.H. was supported by the European Union under the project ROBOPROX (CZ.02.01.01/00/22_008/0004590). C.L. acknowledges the support of NUS through the RoboLife start-up grant. G.I. acknowledges the financial support of the Swiss National Science Foundation (grant 204651).

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Authors and Affiliations

  1. Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic

    Giulia D’Angelo & Matej Hoffmann

  2. Event-Driven Perception for Robotics, Istituto Italiano di Tecnologia, Genoa, Italy

    Giulia D’Angelo & Chiara Bartolozzi

  3. Technical University of Denmark, DTU Electro City, Copenhagen, Denmark

    Jens E. Pedersen

  4. Faculty of Mechanical Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Khyber Pakhtunkhwa, Pakistan

    Taimoor Hassan

  5. BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy

    Matteo Cianchetti

  6. Department of Computer Science, University of Vermont, Burlington, VT, USA

    Josh Bongard

  7. University of Cambridge, Cambridge, UK

    Fumiya Iida

  8. Institute of Neuroinformatics, University of Zurich and ETH, Zurich, Switzerland

    Giacomo Indiveri, Chiara De Luca & Elisa Donati

  9. Department of Mechanical Engineering, National University of Singapore, Advanced Robotics Centre, Singapore, Singapore

    Cecilia Laschi

  10. Digital Society Initiative, University of Zurich, Zurich, Switzerland

    Chiara De Luca

Authors
  1. Giulia D’Angelo
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  2. Jens E. Pedersen
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  12. Elisa Donati
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Contributions

E.D., G.D’A., J.E.P., C.B. and M.H. conceived of the main idea behind the paper. The neuromorphic aspects were developed under the supervision of E.D., G.D’A., C.B., J.E.P., G.I. and C.D.L., while M.C., T.H., M.H., C.L. and J.B. contributed their expertise in soft robotics. Task design and benchmarking metrics were conceptualized by C.L., M.T., E.D. and C.D.L., alongside G.D’A., J.E.P., C.B., J.B. and G.I., respectively. Insights into the platform were provided by T.H., M.C. and C.L. The paper’s core tasks and structure were developed by E.D., G.D’A., C.B. and C.D.L., under the supervision of G.I., with continuous feedback and contributions from J.E.P., T.H., M.C., J.B., F.I., M.H. and C.L. All authors provided critical input, integrating their expertise to strengthen the paper from theoretical, methodological and applied perspectives.

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Correspondence to Giulia D’Angelo.

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D’Angelo, G., Pedersen, J.E., Hassan, T. et al. A benchmarking framework for embodied neuromorphic agents. Nat Mach Intell 8, 300–312 (2026). https://doi.org/10.1038/s42256-026-01197-w

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