RESUMEN
Vertebrates possess a biomechanical structure with redundant muscles, enabling adaptability in uncertain and complex environments. Harnessing this inspiration, musculoskeletal systems offer advantages like variable stiffness and resilience to actuator failure and fatigue. Despite their potential, the complex structure presents modelling challenges that are difficult to explicitly formulate and control. This difficulty arises from the need for comprehensive knowledge of the musculoskeletal system, including details such as muscle arrangement, and fully accessible muscle and joint states. Whilst existing model-free methods do not need explicit formulations, they also underutilise the benefits of muscle redundancy. Consequently, they necessitate retraining in the event of muscle failure and require manual tuning of parameters to control joint stiffness limiting their applications under unknown payloads. Presented here is a model-free local inverse statics controller for musculoskeletal systems, employing a feedforward neural network trained on motor babbling data. Experiments with a musculoskeletal leg model showcase the controller's adaptability to complex structures, including mono and bi-articulate muscles. The controller can compensate for changes such as weight variations, muscle failures, and environmental interactions, retaining reasonable accuracy without the need for any additional retraining.