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.

Conventional assistive and rehabilitative robotic systems often overlook human biomechanics, particularly muscular forces, as they predominantly operate in joint or task space and focus on position and exchanged forces. Similarly, traditional manual rehabilitation techniques employed by physiotherapists struggle to obtain quantitative measurements and make precise modifications to key human variables, resulting in predominantly qualitative methods and outcomes. In response to these limitations, this article introduces an innovative assistive robot controller that operates in the muscular space, targeting specific muscles in the lower limb, and distinguishing itself from existing solutions that focus primarily on joint or task space. A key innovation of our approach is the real-time measurement of muscular forces during dynamic tasks, obtained from a calibrated musculoskeletal model. These measurements enable the establishment of a multistep closed-loop controller, with the outer loop precisely tracking the desired muscular forces. Implemented within a configurable viscous environment, the controller provides a natural response for the user. Experimental evaluations conducted using a parallel robot designed for rehabilitation demonstrate the controller’s efficacy. Incorporating the outer loop reduced the median relative error of the tracked muscular force by nearly 80% and decreased the variability of this error by over 85% compared to a pure viscous environment defined as the baseline. These findings highlight the potential applications of this control framework in areas, such as assistive robotics and precision rehabilitation. By achieving objective measurement and control, the system may enhance rehabilitation outcomes, offering tailored exercises that match the individual needs, capabilities, and engagement of each patient.

This article addresses a new way of generating compliant trajectories for control using movement primitives to allow physical human–robot interaction where parallel robots (PRs) are involved. PRs are suitable for tasks requiring precision and performance because of their robust behavior. However, two fundamental issues must be resolved to ensure safe operation: first, the force exerted on the human must be controlled and limited, and second, Type II singularities should be avoided to keep complete control of the robot. We offer a unified solution under the dynamic movement primitives (DMP) framework to tackle both tasks simultaneously. DMPs are used to get an abstract representation for movement generation and are involved in broad areas, such as imitation learning and movement recognition. For force control, we design an admittance controller intrinsically defined within the DMP structure, and subsequently, the Type II singularity evasion layer is added to the system. Both the admittance controller and the evader exploit the dynamic behavior of the DMP and its properties related to invariance and temporal coupling, and the whole system is deployed in a real PR meant for knee rehabilitation. The results show the capability of the system to perform safe rehabilitation exercises.