# EMG-Driven Shared Control Architecture for Human-Robot Co-Manipulation Tasks +
## 1 Introduction
This has led to approaches based on the integration of EMG signals and adaptive admittance control architectures for several applications including robotic manipulation, skill learning, and rehabilitation devices [19]. Since the admittance parameters determine how the robot responds to input signals, interacts with its environment, and coordinates its actions with those of the human operator, tuning control parameters is a critical aspect of designing control systems for human-robot collaboration. In [20], the authors emphasize the importance of carefully tuning the control parameters of the robotic system to optimize its performance and ensure effective collaboration with humans. Advanced implementations integrate components such as human motion prediction, adaptation algorithms, and virtual guidance to improve the quality of assistance [21; 22]. For instance, authors in [23] proposed using sEMG signals to encode stiffness and movement patterns for robotic skill learning and adaptive behavior. Meanwhile, in [24], an sEMG-based gain-tuned compliance controller for lower limb rehabilitation robots was developed to provide tailored assistance. Additionally, the integration of sEMG and variable admittance control has shown promise in improving human-robot collaboration through compliant interaction in manufacturing tasks [25], synchronized motions in human-robot teams [26], and eliciting greater patient engagement in rehabilitation settings [27; 28]. Enabling motion intention interpretation for exoskeleton control is also a central goal [29]. In conclusion, the use of sEMG with variable admittance promises more adaptive, safe, and effective human-robot coordination in various applications [30]. In this paper, a shared control architecture suitable for human-robot co-manipulation tasks is proposed. In detail, the human operator can dynamically change the robot operational mode by performing different contractions with his/her arm, which are recognized and classified online by monitoring sEMG signals and referring to a Support Vector Machine (SVM) algorithm [31]. The classifier output is fed into a finite-state machine algorithm that changes the variable admittance controller parameters to allow human-robot collaboration in different kinds of interaction between the robot and the environment. Devising a control algorithm that can handle different kinds of interactions in a unified manner is challenging due to the manipulator's dynamic behavior not being unique. On one hand, the end-effector should be compliant with the forces exerted by the human operator to provide a smooth and intuitive hand-guiding experience. On the other hand, low damping can cause undesirable resonance and unstable motions when the robot is in contact with stiff environments. The use of EMG sensors to modify the admittance parameters despite the type of interaction forces is a novel approach to ensure stable robot behavior with respect to approaches that only consider robot motion, such as the one in [13], which estimates unstable robot behavior through heuristics, and the one in [12], which only identifies a stable region in terms of parameters for a specific type of interaction. Alternative interfaces can be broadly categorized into physical/haptic and non-contact methods. Direct physical interfaces, such as those employing kinesthetic guidance via force/torque sensors, represent the most straightforward form of co-manipulation [32; 33]. These systems are often intuitive as they directly map the operator's applied forces to the robot's motion. However, a significant limitation, which our work aims to overcome, is the difficulty in distinguishing the operator's explicit commands (e.g., to change a control parameter) from the incidental forces exerted during the manipulation task itself or from collisions with the environment.