Does spontaneous motion lead to intuitive Body-Machine Interfaces? A fitness study of different body segments for wearable telerobotics

Human-Robot Interfaces (HRIs) represent a crucial component in telerobotic systems. Body-Machine Interfaces (BoMIs) based on body motion can feel more intuitive than standard HRIs for naive users as they leverage humans’ natural control capability over their movements. Among the different methods used to map human gestures into robot commands, data-driven approaches select a set of body segments and transform their motion into commands for the robot based on the users’ spontaneous motion patterns. Despite being a versatile and generic method, there is no scientific evidence that implementing an interface based on spontaneous motion maximizes its effectiveness. In this study, we compare a set of BoMIs based on different body segments to investigate this aspect. We evaluate the interfaces in a teleoperation task of a fixed-wing drone and observe users’ performance and feedback. To this aim, we use a framework that allows a user to control the drone with a single Inertial Measurement Unit (IMU) and without prior instructions. All the interfaces are entirely datadriven and depend on the user’s spontaneous motion. We show through a user study that selecting the body segment for a BoMI based on spontaneous motion can lead to sub-optimal performance. Based on our findings, we suggest additional metrics based on biomechanical and behavioral factors that might improve data-driven methods for the design of HRIs.

Personalized Telerobotics by Fast Machine Learning of Body-Machine Interfaces

Dario Floreano, Matteo Macchini, Fabrizio Schiano

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.

2019

The Impact of Virtual Reality and Viewpoints in Body Motion Based Drone Teleoperation

Dario Floreano, Manana Lortkipanidze, Matteo Macchini, Fabrizio Schiano

The operation of telerobotic systems can be a challenging task, requiring intuitive and efficient interfaces to enable inexperienced users to attain a high level of proficiency. Body-Machine Interfaces (BoMI) represent a promising alternative to standard control devices, such as joysticks, because they leverage intuitive body motion and gestures. It has been shown that the use of Virtual Reality (VR) and first-person view perspectives can increase the user's sense of presence in avatars. However, it is unclear if these beneficial effects occur also in the teleoperation of non-anthropomorphic robots that display motion patterns different from those of humans. Here we describe experimental results on teleoperation of a non-anthropomorphic drone showing that VR correlates with a higher sense of spatial presence, whereas viewpoints moving coherently with the robot are associated with a higher sense of embodiment. Furthermore, the experimental results show that spontaneous body motion patterns are affected by VR and viewpoint conditions in terms of variability, amplitude, and robot correlates, suggesting that the design of BoMIs for drone teleoperation must take into account the use of Virtual Reality and the choice of the viewpoint.

2021

Personalized Body-Machine Interfaces for Advanced Human-Robot Interaction

Matteo Macchini

Robotic systems are becoming more and more pervasive in modern industrial, scientific and personal activities through recent years and will play a fundamental role in the future society. Despite their increasing level of automation, teleoperation is still needed in many robotic applications. Human-Robot Interfaces (HRIs) are a central element in the diffusion of robots and are a crucial component to make them available to the use of most people. However, standard interfaces such as remote controllers still need time and extensive training to be proficiently mastered by users. Body-Machine Interfaces (BoMIs) represent a promising alternative to such devices as they leverage the innate ability of humans to finely control their movements. This thesis aims to provide new insights about humans' natural motor behaviors when interfacing with robots and to propose novel approaches for the implementation of HRIs based on body motion. The core contribution of this work is the design, realization and qualification of machine learning-based methods to translate the motion of a person into control inputs for a robot. The methods described here allowed naive users to effectively control real and simulated robots based on the processing of their body movements with a faster learning rate compared to classic interfaces. We extended our core algorithm to leverage knowledge about the natural organization of human motion resulting a more general method that was successfully applied for the control of a set of morphologically different machines. Through the implementation of an online adaptive functionality, we enabled users to modify their motor behaviour during teleoperation resulting in a lower perceived physical and cognitive workload and augmented performance. Human factors such as the sense of presence in virtual or distant environments, or the motor characteristics of distinct body areas are a key factor for advanced telerobotic applications. We studied the impact of the use of Virtual Reality for the control of non-anthropomorphic machines, and the effects of designing BoMIs based on different limbs opening to new knowledge about the effects of such factors in human-robot interaction. The design of a wearable haptic interface for drone operation showed the potential of augmenting their senses to perceive a distal environment through the sense of touch. Thanks to this novel design, nonexpert users were enabled to navigate drones through narrow passages with limited visibility, a task almost impossible to perform with standard interfaces.We believe that this thesis contributes to pave the way towards a deeper understanding of humans in human-robot interaction, as well as providing a new set of tools for the design of simple, intuitive interfaces with the goal of democratizing robotic teleoperation for the masses.

EPFL2021

Personalized Body-Machine Interfaces for Advanced Human-Robot Interaction

Matteo Macchini

EPFL2021