Personalized Body-Machine Interfaces for Advanced Human-Robot Interaction

Matteo Macchini
EPFL, 2021
Thèse EPFL

Résumé

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.

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https://infoscience.epfl.ch/record/288651?ln=fr

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Matteo Macchini
EPFL, 2021
Thèse EPFL

Résumé

Official source

https://infoscience.epfl.ch/record/288651?ln=fr

À propos de ce résultat

Concepts associés (29)

Concepts associés

Chargement

Publications associées (118)

Publications associées

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Robotic teleoperation is fundamental to augment the resilience, precision, and force of robots with the cognition of the operator. However, current interfaces, such as joysticks and remote controllers, are often complicated to handle since they require cognitive effort and learned skills. Wearable interfaces can enable more natural and intuitive interactions with robots, which would make robotic teleoperation accessible to a larger population of users for demanding tasks, such as manipulation or search-and-rescue. The aim of this thesis is to explore solutions to simplify our interactions with robots promoting their access to a broader range of the population. To achieve this, we are presenting a soft upper body exoskeleton, called the FlyJacket, for the bidirectional control of drones. Drones can greatly benefit us as they extend our perception and range of action. The exoskeleton controls a drone by recording torso movement and, through embedded haptic feedback devices, renders either kinesthetic guidance to improve the flight performance or tactile feedback to render the sensation of flying. We developed and tested an interface to control both a simulated and a real drone. The FlyJacket is a soft exoskeleton with arm support conceived to address the challenges of adapting to different morphologies and supporting the user during flight to prevent fatigue. We demonstrated that this novel interface allowed more consistent performance than when performing the same task with a remote controller and users felt more immersed into the flight. Interacting with a robot can be greatly enhanced by having multiple channels of sensorial feedback to increase the awareness of the operator. Information on the state of the drone can be intuitively rendered with haptic feedback. To create this bidirectional interaction with the drone, the two types of haptic feedback - kinesthetic and tactile - have been explored. Kinesthetic feedback was implemented with a cable-driven system to give guidance to the userâs torso position. Performing user studies, we could determine that the embedded guidance improved the flight performance and that a quadratically shaped force feedback curve was the most adequate profile to guide the user. We also established the minimal force difference, defined the perceived magnitude of this system and studied the learning process of users. Tactile feedback was investigated to render the sensation of flying by enhancing flight awareness, realism and immersion. To this end, we developed and embedded a new type of soft actuator that was compliant and lightweight such that it remained wearable and portable. Four devices, placed on the torso, provided feedback by compressing closed air pouches against the skin rendering the sensation of air pressure. A mechanical model and simulation of the pouch device were developed to determine appropriate parameters. We evaluated whether it conveyed useful information to the user and whether it enhanced the experience of flying. We demonstrated that users were able to understand the direction of the cues without prompting, could distinguish the cues quickly, and do so with high accuracy. The device was also used in a simulated flight task and users indicated that it increased the flight realism. We believe that the contributions of this thesis provides insights to the design of intuitive interfaces for human-robot interaction and increases their accessibility to a wider range of the population.

EPFL2019

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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

Chargement

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

Chargement

EPFL2019