Muscle-Targeted Robotic Assistance and Augmentation of Human Motion

This article presents a novel control framework for assisting human arm movements with a robotic device. Unlike existing collaborative and wearable robots that predominantly operate in task space or joint space, our framework focuses on controlling variables related to the muscular space, which refers to the activation and force generation of individual muscles. Translating these variables to the robot's actuation space is challenging, which hinders the development of human-centered tasks and limits the transparency of robotic assistance for the central nervous system. To address these issues, we consider the relationship between the muscular space and the robot's task space, using the kinematics and dynamics of the human limb obtained from a calibrated musculoskeletal model based on Hill's muscle model. Our framework includes two muscle-targeted methods: assistance and augmentation. The assistance method allows exercise or rehabilitation of specific muscles, while the augmentation method achieves isotropic manipulability, enabling the user to exert forces uniformly in all directions using the novel concept of a force envelope. The experiments were conducted using a haptic robot in admittance mode with a viscous environment simulating a load. The results demonstrate significant improvement in the prediction capabilities of the calibrated model compared with the baseline. Muscle-targeted assistance induces substantial changes in targeted muscular efforts. In addition, we achieve isotropic manipulability in a more precise way as compared with previous methods. In summary, our proposed control framework proves effective in assisting human arm movements with a robotic device for rehabilitation and power augmentation in human-centered applications.