Telerobotics aims to transfer human manipulation skills and dexterity over an arbitrary distance and at an arbitrary scale to a remote workplace. A telerobotic system that is transparent enables a natural and intuitive interaction. We postulate that embodiment (with three sub-components: sense of ownership, agency, and self-location) of the robotic system leads to optimal perceptual transparency and increases task performance. However, this has not yet been investigated directly. We reason along four premises and present findings from the literature that substantiate each of them: (1) the brain can embody non-bodily objects (e.g., robotic hands), (2) embodiment can be elicited with mediated sensorimotor interaction, (3) embodiment is robust against inconsistencies between the robotic system and the operator's body, and (4) embodiment positively correlates to dexterous task performance. We use the predictive encoding theory as a framework to interpret and discuss the results reported in the literature. Numerous previous studies have shown that it is possible to induce embodiment over a wide range of virtual and real extracorporeal objects (including artificial limbs, avatars, and android robots) through mediated sensorimotor interaction. Also, embodiment can occur for non-human morphologies including for elongated arms and a tail. In accordance with the predictive encoding theory, none of the sensory modalities is critical in establishing ownership, and discrepancies in multisensory signals do not necessarily lead to loss of embodiment. However, large discrepancies in terms of multisensory synchrony or visual likeness can prohibit embodiment from occurring. The literature provides less extensive support for the link between embodiment and (dexterous) task performance. However, data gathered with prosthetic hands do indicate a positive correlation. We conclude that all four premises are supported by direct or indirect evidence in the literature, suggesting that embodiment of a remote manipulator may improve dexterous performance in telerobotics. This warrants further implementation testing of embodiment in telerobotics. We formulate a first set of guidelines to apply embodiment in telerobotics and identify some important research topics.

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A high level of dexterity is becoming increasingly important for teleoperated inspection, maintenance and repair robots. A standard test to benchmark system dexterity can advance the design, quantify possible improvements, and increase the effectiveness of such systems. Because of the wide variety of tasks and application domains ranging from dismantling explosives from a safe distance to maintenance of deep sea oil rigs, we defined a library of basic, generic tasks and selected five tests that reflect these basic tasks and for which benchmark data already exist or are easy to gather: the Box & Block test, the Purdue Pegboard test, the Minnesota Manual Dexterity test, the ISO 9382 trajectory test (based on the ISO 9382:1998 standard) and the adapted version of the screwing subtest of the IROS 2017 service robots challenge.

Haptic feedback is a desired feature in teleoperation as it can improve dexterous manipulation. Direct force feedback to the operator's hand and fingers requires complex hardware and therefore substituting force by for instance vibration is a relevant topic. In this experiment, we tested performance on a Box & Blocks task in a teleoperation setup with no feedback, direct force feedback and substituted vibration feedback. Objective performance was the same in all conditions as was the learning effect over three sessions, but participants had a clear preference for haptic feedback over no haptic feedback. The preferred type of feedback (force or vibration or both) varied over participants. In general, this study showed that haptic feedback is preferred in teleoperation, the Box & Blocks task seems not sensitive enough for our (and most) current teleoperation set-up(s), and vibration feedback as substitute for direct force feedback works well and can be used intuitively.