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Biomim."],"published-print":{"date-parts":[[2025,7,31]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Artificial musculoskeletal systems mimic mammalian biomechanics using antagonistic muscles and rigid skeletons. They offer benefits such as adjustable stiffness, back-drivability, and muscle failure tolerance but are difficult to model and control due to redundancies across task, joint, and muscle activation spaces, compounded by complex muscle dynamics and motion-dependent moment arms. Analytical methods require detailed system knowledge and lack scalability, while model-free approaches often rely on manual tuning and rarely exploit motor redundancy. This work introduces a model-free, biologically inspired kinematic controller based on reflex circuits that self-organise via Hebbian learning driven by Spontaneous Motor Activity (SMA). These circuits are then integrated to create a computationally inexpensive task-space controller, requiring minimal training and no analytical modelling. Simulations with six- and twelve-muscle models show that the interaction between reflex circuits, morphology, and gain modulation produces coordinated muscle synergies for human-like target reaching. Unlike previous control methods, it is easily scalable, can automatically handle unknown disturbances, and compensates for inaccessible muscles without re-training or manual intervention while maintaining high control accuracy.<\/jats:p>","DOI":"10.1088\/1748-3190\/adde08","type":"journal-article","created":{"date-parts":[[2025,5,28]],"date-time":"2025-05-28T22:55:17Z","timestamp":1748472917000},"page":"046008","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Self-organising bio-inspired reflex circuits for robust motor coordination in artificial musculoskeletal systems"],"prefix":"10.1088","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8101-4217","authenticated-orcid":true,"given":"Elijah","family":"Almanzor","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6271-3394","authenticated-orcid":false,"given":"Michael","family":"Ishida","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6874-7782","authenticated-orcid":true,"given":"Arsen","family":"Abdulali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9246-7190","authenticated-orcid":true,"given":"Fumiya","family":"Iida","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2025,6,6]]},"reference":[{"key":"bbadde08bib1","doi-asserted-by":"publisher","first-page":"11267","DOI":"10.1109\/TCYB.2021.3071312","article-title":"A survey of brain-inspired intelligent robots: integration of vision, decision, motion control and musculoskeletal systems","volume":"52","author":"Qiao","year":"2021","journal-title":"IEEE Trans. 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