# EMG-Driven Shared Control Architecture for Human-Robot Co-Manipulation Tasks +
## 1 Introduction
More advanced haptic interfaces, such as fingertip devices, can provide better feedback but may introduce hardware complexity and can be less suitable for tasks requiring the operator to have a direct grip on the end-effector [34]. Non-contact interfaces offer the advantage of not requiring sensors to be worn by the operator. Vision-based systems, for example, can use markerless tracking of the operator's body to infer intent [35]. Such systems are vulnerable to environmental factors such as occlusions and lighting changes, and may create a cognitive disconnect in tasks that are fundamentally physical. Similarly, eye-tracking interfaces may provide an intuitive means of directing a robot's end-effector but they risk imposing a high cognitive load and can suffer from the problem where an unintentional gaze triggers a robot action [36]. Voice control is another hands-free modality, though its reliability can be compromised in noisy industrial settings. In this context, the EMG-based approach offers a compelling middle ground. The EMG signal is intrinsically linked to the user's neuromuscular intent to perform an action, providing a clear and direct command channel that is separate from the physical interaction forces [37]. This allows the operator to seamlessly switch control modes without interrupting the physical workflow or relying on external systems. Therefore, the main contribution of the paper lies in the design of a shared control architecture that dynamically adjusts the variable admittance controller parameters by processing the operator's EMG signals, thereby overcoming resonance and robot instability issues and facilitating various human-robot and robot-environment interactions. The effectiveness and the robustness of the approach have been experimentally validated employing a 7 DOFs manipulator equipped with a force/torque sensor, and with a group of four users wearing four sEMG sensors on their dominant upper limb. Since the manipulator used to test the proposed architecture can not be torque-controlled, a variable admittance control strategy has been chosen. This article represents a substantial extension of our previously published conference paper [38], in which we first introduced an EMG-driven shared control architecture for switching between two operational modes in a human-robot co-manipulation context. While the initial study demonstrated the feasibility of using muscle contractions to influence robot behavior, the current journal version significantly expands both the methodological framework and the experimental validation. From a methodological perspective, we introduce a third operational mode, referred to as _hold_ mode, which enhances the system's versatility in handling various interaction scenarios, particularly in tasks where the robot's end-effector must maintain a specific pose. Correspondingly, the EMG classifier is extended from two to three classes to allow the user to switch among these three new operational modes intuitively. On the experimental side, the validation protocol has been considerably extended. The system was tested with four participants, each performing ten trials, to validate consistency and ease of use. Classifier performance was evaluated using standard metrics and visualized through confusion matrices to provide a comprehensive assessment across all EMG classes. Furthermore, user feedback was collected using NASA-TLX questionnaires, allowing us to quantify subjective workload and user experience. These new results collectively support the practical value of the proposed architecture and demonstrate its applicability in a broader range of collaborative robotics tasks. Overall, the additions presented in this article enhance the robustness, generalizability, and usability of the system, moving it closer to real-world deployment in domains such as industrial co-manipulation, assistive robotics, and physical human-robot collaboration.