# Enhancing Telexistence Control Through Assistive Manipulation and Haptic Feedback
## 2 Related Works  
The fields of teleoperation and teleexistence have evolved significantly, with each focusing on unique aspects of remote interaction. Teleoperation traditionally centers on controlling a robot remotely, while telexistence emphasizes the user's immersive experience, aiming to replicate the sense of physical presence and embodiment in a distant environment [10; 11]. This study specifically addresses the application of assistive manipulation methods and haptic feedback within telexistence systems, building on and addressing limitations observed in prior research. Numerous studies have enhanced teleoperation systems by concentrating on motion planning and control, which are crucial for remote manipulation tasks. For instance, [PERSON] et al. [12] employed dynamic goal deep reinforcement learning to seamlessly map hand movements to a robotic arm, effectively avoiding collisions. Similarly, [PERSON] et al. [13] developed a teleoperation system leveraging virtual reality (VR) to explore the efficiency of teaching robotic tasks through imitation learning. Despite their contributions, these works primarily emphasize semi-autonomous teleoperation for high-level tasks and trajectory optimization rather than offering direct assistance. Other research [11; 14] has introduced assistive approaches without incorporating machine learning (ML). In contrast, the current study employs ML to facilitate assistance. Earlier studies have used object detection to enhance user awareness of remote environments [10] or controlled robots via depth cameras [13]. In [12], assistance involved mapping relative rotations, while in [13; 15], user input predictions were used to provide contextually appropriate assistance. Additionally, the study in [16] utilized relative rotation mapping to offer assistance. This study distinguishes itself by applying object detection to compute inverse kinematic solutions for robotic arm movements. Haptic feedback is an integral component of telexistence [17], enhancing intuitive control by allowing users to experience realistic tactile interactions with remote environments [18]. [PERSON] et al. [19] introduced a telepresence system with temperature, pressure, and vibration sensors to provide users with comprehensive sensory feedback, advancing realism and task efficiency. However, this approach used a high-fidelity, multi-sensor setup, which can be complex and costly. By contrast, more straightforward implementations of haptic feedback, like the vibrotactile systems proposed by [PERSON] et al. [19], offer manageable, cost-effective alternatives but often lack the depth of immersion needed for intricate tasks. Our prior research [9] focused on motion blending techniques to enhance user control and task performance. However, it did not investigate the combined effects of haptic feedback and assistance on body ownership and task efficiency. This study addresses these gaps by integrating multi-level haptic feedback with motion blending techniques and refining the hardware setup to improve performance and embodiment. Table 1 compares the two studies to delineate the novel contributions of the current study. In addition to haptic interfaces, biofeedback-based HMIs, particularly those utilizing electromyography (EMG) signals, have garnered attention. EMG-based systems decode muscle activity to infer user intent, enabling intuitive control of robotic systems. Recent studies have demonstrated the efficacy of EMG-driven control in teleoperation contexts. For example, [PERSON] et al. developed an HMI combining surface EMG signals with data from the Microsoft Kinect sensor to control a mobile humanoid robot arm in real time [20]. Similarly, [PERSON] et al. proposed an EMG-driven strategy-assisted shared control method for robotic relemanipulation, highlighting the potential of EMG signals in enhancing teleoperation performance [21]. Our study builds on these approaches by integrating haptic feedback through force-sensitive resistors (FSRs) on a robotic gripper.