Human-Robot Interfaces (HRIs) can be hard to master for inexperienced users, making the teleoperation of mobile robots a difficult task. The development of Body-Machine Interfaces (BoMIs) represents a promising approach to making a user more proficient, by exploiting the natural control they can exert on their own body motion. Since human motion presents individual traits due to several factors, including physical condition, age, and experience, generic BoMIs still require a significant learning time and effort to reach adequate ability in teleoperation. In this work, we present a novel approach which provides a Body-Machine Interface tailored on the specific user. Our method autonomously learns from the user their preferred strategy to control the robot, and provide a personalized body-machine mapping. We show that the proposed method can significantly reduce the duration of the training phase in teleoperation, thus allowing faster skill acquisition. We validated our approach by performing both simulation and real-world experiments with human subjects. The first involved the teleoperation of a fixed-wing simulated drone, while the second consisted in controlling a real quadrotor. We used our framework to extrapolate common and peculiar features of movements among individuals. Observing reoccurring strategies, we provide insights on how humans would naturally interface with a distal machine. Index terms-Telerobotics and Teleoperation, AI-Based Methods, Learning and Adaptive Systems, Wearable Robots Supplementary video: https://youtu.be/ssLa75f1y2Y I. INTRODUCTION The term telerobotics, derived from the greek tele (distant), refers to a system with a human-in-the-loop operator, controlling a robot situated in a separate environment [1]. Telerobotics finds relevant uses in fields where human cognition and decision-making capabilities cannot be substituted by machine intelligence, including exploration, supervision and maintenance in risky environments, such as nuclear plants [2] or during search and rescue missions [3]. Robotic systems can also be employed in tasks where augmenting the operator's perception and accuracy is needed, as in the case of minimally invasive surgery [4]. These applications require the design and implementation of control interfaces that are sufficiently powerful and intuitive Manuscript

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Drone teleoperation is usually accomplished using remote radio controllers, devices that can be hard to master for inexperienced users. Moreover, the limited amount of information fed back to the user about the robot's state, often limited to vision, can represent a bottleneck for operation in several conditions. In this work, we present a wearable interface for drone teleoperation and its evaluation through a user study. The two main features of the proposed system are a data glove to allow the user to control the drone trajectory by hand motion and a haptic system used to augment their awareness of the environment surrounding the robot. This interface can be employed for the operation of robotic systems in line of sight (LoS) by inexperienced operators and allows them to safely perform tasks common in inspection and searchand-rescue missions such as approaching walls and crossing narrow passages with limited visibility conditions. In addition to the design and implementation of the wearable interface, we performed a systematic study to assess the effectiveness of the system through three user studies (n = 36) to evaluate the users' learning path and their ability to perform tasks with limited visibility. We validated our ideas in both a simulated and a realworld environment. Our results demonstrate that the proposed system can improve teleoperation performance in different cases compared to standard remote controllers, making it a viable alternative to standard Human-Robot Interfaces.

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Figure 1: Spontaneous body motion acquisition during a robot imitation task using different viewpoints. The acquired data can be used to define personalized Body-Machine Interfaces. (top) VR-disabled conditions. (bottom) VR-enabled conditions.