Self-organising bio-inspired reflex circuits for robust motor coordination in artificial musculoskeletal systems.
Journal:
Bioinspiration & biomimetics
Published Date:
Jun 6, 2025
Abstract
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.
