A neuromechanics solution for adjustable robot compliance and accuracy Abadía Tercedor, Ignacio Bruel, Alice Courtine, Grégoire Ijspeert, Auke J. Ros Vidal, Eduardo Luque Sola, Niceto Rafael Original research papers are freely accessible with registration on the Science journal’s website 12 months after publication (Final Version of the article) This work was supported by MUSCLEBOT (CNS2022-1147 135243) funded by MCIN/AEI/10.13039/501100011033/ and by NextGenerationEU/PRTR; TREMBLE-ICED (PID2023-146392NB-I00) funded by MCIN/AEI/10.13039/; DLROB (TED2021-131294B-I00) funded by MCIN/AEI/10.13039/501100011033/ and by NextGenerationEU/PRTR awarded to N.R.L.. SENSCOMP (PID2022-140095NB-I00) funded by MCIN/AEI/10.13039/501100011033/ and EU FEDER; INTARE (TED2021-131466B-I00) funded by MCIN/AEI/10.13039/501100011033/ and NextGenerationEU/PRTR awarded to E.R.. EU Human Brain Project Specific Grant Agreement 3 (H2020-RIA. 945539) awarded to A.I. and E.R. Robots have to adjust their motor behavior to changing environments and variable task requirements in order to successfully operate in the real world and physically interact with humans. Thus, robotics strives to enable a broad spectrum of adjustable motor behavior, aiming to mimic the human ability to function in unstructured scenarios. In humans, motor behavior arises from the integrative action of the central nervous system and body biomechanics; motion must be understood from a neuromechanics perspective. Nervous regions such as the cerebellum facilitate learning, adaptation and coordination of our motor responses, ultimately driven by muscle activation. Muscles, in turn, self-stabilize motion through mechanical viscoelasticity. Besides, the agonist- antagonist arrangement of muscles surrounding joints enables cocontraction, which can be regulated to enhance motion accuracy and adapt joint stiffness, thereby providing impedance modulation and broadening the motor repertoire. Here, we propose a control solution that harnesses neuromechanics to enable adjustable robot motor behavior. Our solution integrates a muscle model replicating mechanical viscoelasticity and cocontraction, together with a cerebellar network providing motor adaptation. The resulting cerebello-muscular controller drives the robot through torque commands in a feedback control loop. Changes in cocontraction modify the muscle dynamics; and the cerebellum provides motor adaptation without relying on prior analytical solutions, driving the robot in different motor tasks, including payload perturbations and operation across unknown terrains. Experimental results show that cocontraction modulates robot stiffness, performance accuracy and robustness against external perturbations. Through cocontraction modulation, our cerebello-muscular torque controller enables a broad spectrum of robot motor behavior. 2026-01-21T12:34:17Z 2026-01-21T12:34:17Z 2025 journal article Published version: Abadía Tercedor, Ignacio et al. A neuromechanics solution for adjustable robot compliance and accuracy. Science Robotics 22 Jan 2025 Vol 10, Issue 98 DOI: 10.1126/scirobotics.adp2356 https://hdl.handle.net/10481/110046 10.1126/scirobotics.adp2356 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional AAAS (American Association for the Advancement of Science)