In this article, we transition from the theoretical and experimental groundwork of manipulating and measuring Sense of Embodiment (SoE) to addressing a fundamental question: What is the purpose of optimizing the SoE in a teleoperation system? This exploration centers on investigating the potential positive effects of SoE on motor adaptation, the acceleration of motor learning, and the potential enhancement of task performance. The article delves into this investigation by focusing on two critical research questions: (1) what is the effect of SoE on task performance in a perceptual-motor task? (2) What is the effect of SoE on the asymptote of the learning curve in a perceptual-motor task? Drawing insights from the existing literature, the hypothesis emerges that enhancing SoE yields positive effects not only on task performance (H1) but also on the overall embodiment experience (H2). An additional layer of exploration is introduced through an exploratory research question: Are these results consistent across diverse scenarios and tasks? The study design encompasses two distinct user studies, each set in different applications and featuring various avatars, yet all anchored in similar tasks, specifically a modified peg-in-hole task: (1) in the first experiment, participants operated a robotic arm with a human-like hand as end-effector, and they were required to perform a classic peg-in-hole task; (2) in user study 2, the task is transformed into a variation that we called “peg-on-button,” wherein participants use a robotic arm with a gripper as the end-effector to press a lit button. In both studies, a consistent pattern emerges: a setup that fosters embodiment has a positive impact on motor learning and adaptation, resulting in improved task performance. A supportive setup also reduces the perception of the surrogate as a mere mediator between the operator and the remote environment, especially when contrasted with a setup that suppresses embodiment. The positive effects on motor learning and task performance advocate for the incorporation of embodiment-supportive designs in teleoperational setups. However, the nuanced relationship between SoE and long-term task performance prompts a call for further exploration and consideration of various factors influencing teleoperation outcomes across diverse scenarios and tasks.

We present a literature review and a toolbox to help the reader find the best method to design for and assess Sense of Embodiment (SoE) in several application scenarios. The main examples are based on teleoperation applications, due the challenges that these applications present. The three embodiment components that we consider to describe SoE are sense of ownership, sense of agency, and sense of self-location. We relate each embodiment component to the most often used assessment measures, test tasks, and application scenarios. The toolbox is built to efficiently design, test, and assess an embodiment experience, following seven concrete steps. We provide four main contributions: 1) a literature review of the assessment measures and strategies used to measure SoE; 2) a systematic categorization of SoE measures; 3) A categorization of the main test tasks used in SoE assessment; and 4) a toolbox consisting of seven steps as guidance to design SoE. We included several examples and tables to guide the user step by step through the design of an embodiment experience.

Advances in robotics and multisensory displays allow extending telepresence ambitions beyond only the feeling of being present at a remote location In this paper, we discuss what may lie beyond telepresence and how we can transport both the functional and social self of a user. We introduce the embodiment illusion and its potential contribution to task performance and list important cues to evoke this illusion, including synchronicity in multisensory information, a first-person visual perspective, and a human-like visual appearance and anatomy of the telepresence robot. We also introduce the concept of social presence and the important bidirectional social cues it needs, including eye contact, facial expression, posture, gestures, and social touch. For all these multisensory and social cues, we explain how they can be implemented in a telepresence system and describe our solution consisting of a closed control pod and a humanoid telepresence robot.