What does it feel like to own, to control, and to be inside a body? The multidimensional nature of this experience together with the continuous presence of one's biological body, render both theoretical and experimental approaches problematic. Nevertheless, exploitation of immersive virtual reality has allowed a reframing of this question to whether it is possible to experience the same sensations towards a virtual body inside an immersive virtual environment as toward the biological body, and if so, to what extent. The current paper addresses these issues by referring to the Sense of Embodiment (SoE). Due to the conceptual confusion around this sense, we provide a working definition which states that SoE consists of three subcomponents: the sense of self-location, the sense of agency, and the sense of body ownership. Under this proposed structure, measures and experimental manipulations reported in the literature are reviewed and related challenges are outlined. Finally, future experimental studies are proposed to overcome those challenges, toward deepening the concept of SoE and enhancing it in virtual applications.

In the past, working spaces of humans and robots were strictly separated, but recent developments have sought to bring robots into closer interaction with humans. In this context, physical human–robot interaction represents a major challenge, as it is based on continuous bilateral information and energy exchanges which result in a mutual adaptation of the partners. To address the challenge of designing robot collaboration partners, making them as human-like as possible is an approach often adopted. In order to compare different implementations with each other, their degree of human-likeness on a continuous scale is required. So far, the human-likeness of haptic interaction partners has only been studied in the form of binary choices. In this paper, we first introduce methods that allow measuring the human-likeness of haptic interaction partners on a continuous scale. In doing so, two subjective rating methods are proposed and correlated with a task performance measure. To demonstrate the applicability and validity of the proposed measures, they are applied to a joint kinesthetic manipulation task and used to compare two different implementations of a haptic interaction partner: a feedforward model based on force replay, and a feedback model. This experiment demonstrates the use of the proposed measures in building a continuous human-likeness scale and the interpretation of the scale values achieved for formulating guidelines for future robot implementations.