# EMG-Driven Shared Control Architecture for Human-Robot Co-Manipulation Tasks +
## 1 Introduction  
Collaborative robotics refers to robotic systems that directly interact with humans in a shared workspace to jointly complete tasks. These human-robot teams aim to combine the versatility and decision-making capability of human workers with the precision, strength and repeatability of robots to improve both the quality and execution time of collaborative tasks. In this scenario, intuitive user interfaces that facilitate smooth human-robot cooperation, advanced sensors and control systems are used to enable the robot to safely operate close to humans [1]. Unlike traditional industrial robots, which operate in isolated environments and have physical barriers to be separated from human operators, collaborative robots (or cobots) can dynamically adapt to people and objects that enter their workspace [2]. This flexibility allows them to be employed in various contexts, such as manufacturing and assembly operations [3], logistics or in applications such as surgery to assist physicians in medical procedures [4]. Developing effective human-robot teams presents challenges such as providing the robot with situational awareness, establishing clear communication protocols, and ensuring safety without limiting the robot's speed or range of motion to make physical interaction more intuitive [5, 6]. To address these challenges, shared control techniques, in which both the robot and the human operator are involved as active parts in the control loop [7], haveemerged as promising solutions to enable smoother interaction and to improve safety and task efficiency. By combining autonomous robot control with human decision making, shared control enhances the overall operational capabilities of the system [8]. In shared control, a human and an automated system, such as a robot, collaborate to regulate the system's behavior, offering an effective solution for various applications, particularly in human-robot interaction scenarios [9]. It is evident that the ability to facilitate physical human-robot interaction (pHRI) during collaborative tasks is a pivotal element of shared control. To manage the dynamics of these interactions, an impedance/admittance control strategy is used for controlling robots to behave as virtual dynamic systems with adjustable impedance parameters [10; 11], rather than just following their intrinsic dynamics. This allows the robot's compliance and motion synchronization capabilities to be adapted to enhance both collaboration with humans and interaction with the environment [12]; thus the robot can adjust its dynamic behavior when physically interacting with an external agent, and the desired dynamic behavior can be replicated on a non-backdrivable manipulator to improve its physical interaction capabilities [13]. To enhance the performance of human-robot collaboration, the concept of human-like variable admittance parameter regulation has been proposed, which is based on the change rate of interaction force and the study of a human arm's static and dynamic admittance parameters in human-human collaborative motion [14]. In physical human-robot interaction, the trajectory prediction of human motion also plays a pivotal role, as it allows robots to anticipate human movements and adapt their actions accordingly. This allows robots to minimize human interference, optimize task completion times, and improve overall performance during interaction [15]. Additionally, a method for variable admittance control that combines a human-like decision-making process and an adaptation algorithm, such as fuzzy inference system, to improve human-robot cooperation tasks is developed in [16]. Surface ElectroMyoGraphy (sEMG) sensors, which capture the electrical activity of muscles, play an important role in variable admittance control schemes to estimate the user's movement intention, as described in [17; 18] where the measurements of the operator's muscle activation are used to adapt the virtual damping online.