A review on manipulation skill acquisition through teleoperation-based learning from demonstration

Manipulation skill learning and generalization have gained increasing attention due to the wide applications of robot manipulators and the spurt of robot learning techniques. Especially, the learning from demonstration method has been exploited widely and successfully in the robotic community, and it is regarded as a promising direction to realize the manipulation skill learning and generalization. In addition to the learning techniques, the immersive teleoperation enables the human to operate a remote robot with an intuitive interface and achieve the telepresence. Thus, it is a promising way to transfer manipulation skills from humans to robots by combining the learning methods and the teleoperation, and adapting the learned skills to different tasks in new situations. This review, therefore, aims to provide an overview of immersive teleoperation for skill learning and generalization to deal with complex manipulation tasks. To this end, the key technologies, e.g. manipulation skill learning, multimodal interfacing for teleoperation and telerobotic control, are introduced. Then, an overview is given in terms of the most important applications of immersive teleoperation platform for robot skill learning. Finally, this survey discusses the remaining open challenges and promising research topics

Human to robot hand motion mapping methods: review and classification

In this article, the variety of approaches proposed in literature to address the problem of mapping human to robot hand motions are summarized and discussed. We particularly attempt to organize under macro-categories the great quantity of presented methods, that are often difficult to be seen from a general point of view due to different fields of application, specific use of algorithms, terminology and declared goals of the mappings. Firstly, a brief historical overview is reported, in order to provide a look on the emergence of the human to robot hand mapping problem as a both conceptual and analytical challenge that is still open nowadays. Thereafter, the survey mainly focuses on a classification of modern mapping methods under six categories: direct joint, direct Cartesian, taskoriented, dimensionality reduction based, pose recognition based and hybrid mappings. For each of these categories, the general view that associates the related reported studies is provided, and representative references are highlighted. Finally, a concluding discussion along with the authors’ point of view regarding future desirable trends are reported.This work was supported in part by the European Commission’s Horizon 2020 Framework Programme with the project REMODEL under Grant 870133 and in part by the Spanish Government under Grant PID2020-114819GB-I00.Peer ReviewedPostprint (published version

Study and development of sensorimotor interfaces for robotic human augmentation

This thesis presents my research contribution to robotics and haptics in the context of human augmentation. In particular, in this document, we are interested in bodily or sensorimotor augmentation, thus the augmentation of humans by supernumerary robotic limbs (SRL). The field of sensorimotor augmentation is new in robotics and thanks to the combination with neuroscience, great leaps forward have already been made in the past 10 years. All of the research work I produced during my Ph.D. focused on the development and study of fundamental technology for human augmentation by robotics: the sensorimotor interface. This new concept is born to indicate a wearable device which has two main purposes, the first is to extract the input generated by the movement of the user's body, and the second to provide the somatosensory system of the user with an haptic feedback. This thesis starts with an exploratory study of integration between robotic and haptic devices, intending to combine state-of-the-art devices. This allowed us to realize that we still need to understand how to improve the interface that will allow us to feel the agency when using an augmentative robot. At this point, the path of this thesis forks into two alternative ways that have been adopted to improve the interaction between the human and the robot. In this regard, the first path we presented tackles two aspects conerning the haptic feedback of sensorimotor interfaces, which are the choice of the positioning and the effectiveness of the discrete haptic feedback. In the second way we attempted to lighten a supernumerary finger, focusing on the agility of use and the lightness of the device. One of the main findings of this thesis is that haptic feedback is considered to be helpful by stroke patients, but this does not mitigate the fact that the cumbersomeness of the devices is a deterrent to their use. Preliminary results here presented show that both the path we chose to improve sensorimotor augmentation worked: the presence of the haptic feedback improves the performance of sensorimotor interfaces, the co-positioning of haptic feedback and the input taken from the human body can improve the effectiveness of these interfaces, and creating a lightweight version of a SRL is a viable solution for recovering the grasping function

Sensorimotor experience in virtual environments

The goal of rehabilitation is to reduce impairment and provide functional improvements resulting in quality participation in activities of life, Plasticity and motor learning principles provide inspiration for therapeutic interventions including movement repetition in a virtual reality environment, The objective of this research work was to investigate functional specific measurements (kinematic, behavioral) and neural correlates of motor experience of hand gesture activities in virtual environments stimulating sensory experience (VE) using a hand agent model. The fMRI compatible Virtual Environment Sign Language Instruction (VESLI) System was designed and developed to provide a number of rehabilitation and measurement features, to identify optimal learning conditions for individuals and to track changes in performance over time. Therapies and measurements incorporated into VESLI target and track specific impairments underlying dysfunction. The goal of improved measurement is to develop targeted interventions embedded in higher level tasks and to accurately track specific gains to understand the responses to treatment, and the impact the response may have upon higher level function such as participation in life. To further clarify the biological model of motor experiences and to understand the added value and role of virtual sensory stimulation and feedback which includes seeing one\u27s own hand movement, functional brain mapping was conducted with simultaneous kinematic analysis in healthy controls and in stroke subjects. It is believed that through the understanding of these neural activations, rehabilitation strategies advantaging the principles of plasticity and motor learning will become possible. The present research assessed successful practice conditions promoting gesture learning behavior in the individual. For the first time, functional imaging experiments mapped neural correlates of human interactions with complex virtual reality hands avatars moving synchronously with the subject\u27s own hands, Findings indicate that healthy control subjects learned intransitive gestures in virtual environments using the first and third person avatars, picture and text definitions, and while viewing visual feedback of their own hands, virtual hands avatars, and in the control condition, hidden hands. Moreover, exercise in a virtual environment with a first person avatar of hands recruited insular cortex activation over time, which might indicate that this activation has been associated with a sense of agency. Sensory augmentation in virtual environments modulated activations of important brain regions associated with action observation and action execution. Quality of the visual feedback was modulated and brain areas were identified where the amount of brain activation was positively or negatively correlated with the visual feedback, When subjects moved the right hand and saw unexpected response, the left virtual avatar hand moved, neural activation increased in the motor cortex ipsilateral to the moving hand This visual modulation might provide a helpful rehabilitation therapy for people with paralysis of the limb through visual augmentation of skills. A model was developed to study the effects of sensorimotor experience in virtual environments, and findings of the effect of sensorimotor experience in virtual environments upon brain activity and related behavioral measures. The research model represents a significant contribution to neuroscience research, and translational engineering practice, A model of neural activations correlated with kinematics and behavior can profoundly influence the delivery of rehabilitative services in the coming years by giving clinicians a framework for engaging patients in a sensorimotor environment that can optimally facilitate neural reorganization

All Hands on Deck: Choosing Virtual End Effector Representations to Improve Near Field Object Manipulation Interactions in Extended Reality

Extended reality, or XR , is the adopted umbrella term that is heavily gaining traction to collectively describe Virtual reality (VR), Augmented reality (AR), and Mixed reality (MR) technologies. Together, these technologies extend the reality that we experience either by creating a fully immersive experience like in VR or by blending in the virtual and real worlds like in AR and MR. The sustained success of XR in the workplace largely hinges on its ability to facilitate efficient user interactions. Similar to interacting with objects in the real world, users in XR typically interact with virtual integrants like objects, menus, windows, and information that convolve together to form the overall experience. Most of these interactions involve near-field object manipulation for which users are generally provisioned with visual representations of themselves also called self-avatars. Representations that involve only the distal entity are called end-effector representations and they shape how users perceive XR experiences. Through a series of investigations, this dissertation evaluates the effects of virtual end effector representations on near-field object retrieval interactions in XR settings. Through studies conducted in virtual, augmented, and mixed reality, implications about the virtual representation of end-effectors are discussed, and inferences are made for the future of near-field interaction in XR to draw upon from. This body of research aids technologists and designers by providing them with details that help in appropriately tailoring the right end effector representation to improve near-field interactions, thereby collectively establishing knowledge that epitomizes the future of interactions in XR

A virtual hand assessment system for efficient outcome measures of hand rehabilitation

Previously held under moratorium from 1st December 2016 until 1st December 2021.Hand rehabilitation is an extremely complex and critical process in the medical rehabilitation field. This is mainly due to the high articulation of the hand functionality. Recent research has focused on employing new technologies, such as robotics and system control, in order to improve the precision and efficiency of the standard clinical methods used in hand rehabilitation. However, the designs of these devices were either oriented toward a particular hand injury or heavily dependent on subjective assessment techniques to evaluate the progress. These limitations reduce the efficiency of the hand rehabilitation devices by providing less effective results for restoring the lost functionalities of the dysfunctional hands. In this project, a novel technological solution and efficient hand assessment system is produced that can objectively measure the restoration outcome and, dynamically, evaluate its performance. The proposed system uses a data glove sensorial device to measure the multiple ranges of motion for the hand joints, and a Virtual Reality system to return an illustrative and safe visual assistance environment that can self-adjust with the subject’s performance. The system application implements an original finger performance measurement method for analysing the various hand functionalities. This is achieved by extracting the multiple features of the hand digits’ motions; such as speed, consistency of finger movements and stability during the hold positions. Furthermore, an advanced data glove calibration method was developed and implemented in order to accurately manipulate the virtual hand model and calculate the hand kinematic movements in compliance with the biomechanical structure of the hand. The experimental studies were performed on a controlled group of 10 healthy subjects (25 to 42 years age). The results showed intra-subject reliability between the trials (average of crosscorrelation ρ = 0.7), inter-subject repeatability across the subject’s performance (p > 0.01 for the session with real objects and with few departures in some of the virtual reality sessions). In addition, the finger performance values were found to be very efficient in detecting the multiple elements of the fingers’ performance including the load effect on the forearm. Moreover, the electromyography measurements, in the virtual reality sessions, showed high sensitivity in detecting the tremor effect (the mean power frequency difference on the right Vextensor digitorum muscle is 176 Hz). Also, the finger performance values for the virtual reality sessions have the same average distance as the real life sessions (RSQ =0.07). The system, besides offering an efficient and quantitative evaluation of hand performance, it was proven compatible with different hand rehabilitation techniques where it can outline the primarily affected parts in the hand dysfunction. It also can be easily adjusted to comply with the subject’s specifications and clinical hand assessment procedures to autonomously detect the classification task events and analyse them with high reliability. The developed system is also adaptable with different disciplines’ involvements, other than the hand rehabilitation, such as ergonomic studies, hand robot control, brain-computer interface and various fields involving hand control.Hand rehabilitation is an extremely complex and critical process in the medical rehabilitation field. This is mainly due to the high articulation of the hand functionality. Recent research has focused on employing new technologies, such as robotics and system control, in order to improve the precision and efficiency of the standard clinical methods used in hand rehabilitation. However, the designs of these devices were either oriented toward a particular hand injury or heavily dependent on subjective assessment techniques to evaluate the progress. These limitations reduce the efficiency of the hand rehabilitation devices by providing less effective results for restoring the lost functionalities of the dysfunctional hands. In this project, a novel technological solution and efficient hand assessment system is produced that can objectively measure the restoration outcome and, dynamically, evaluate its performance. The proposed system uses a data glove sensorial device to measure the multiple ranges of motion for the hand joints, and a Virtual Reality system to return an illustrative and safe visual assistance environment that can self-adjust with the subject’s performance. The system application implements an original finger performance measurement method for analysing the various hand functionalities. This is achieved by extracting the multiple features of the hand digits’ motions; such as speed, consistency of finger movements and stability during the hold positions. Furthermore, an advanced data glove calibration method was developed and implemented in order to accurately manipulate the virtual hand model and calculate the hand kinematic movements in compliance with the biomechanical structure of the hand. The experimental studies were performed on a controlled group of 10 healthy subjects (25 to 42 years age). The results showed intra-subject reliability between the trials (average of crosscorrelation ρ = 0.7), inter-subject repeatability across the subject’s performance (p > 0.01 for the session with real objects and with few departures in some of the virtual reality sessions). In addition, the finger performance values were found to be very efficient in detecting the multiple elements of the fingers’ performance including the load effect on the forearm. Moreover, the electromyography measurements, in the virtual reality sessions, showed high sensitivity in detecting the tremor effect (the mean power frequency difference on the right Vextensor digitorum muscle is 176 Hz). Also, the finger performance values for the virtual reality sessions have the same average distance as the real life sessions (RSQ =0.07). The system, besides offering an efficient and quantitative evaluation of hand performance, it was proven compatible with different hand rehabilitation techniques where it can outline the primarily affected parts in the hand dysfunction. It also can be easily adjusted to comply with the subject’s specifications and clinical hand assessment procedures to autonomously detect the classification task events and analyse them with high reliability. The developed system is also adaptable with different disciplines’ involvements, other than the hand rehabilitation, such as ergonomic studies, hand robot control, brain-computer interface and various fields involving hand control

Haptics: Science, Technology, Applications

This open access book constitutes the proceedings of the 12th International Conference on Human Haptic Sensing and Touch Enabled Computer Applications, EuroHaptics 2020, held in Leiden, The Netherlands, in September 2020. The 60 papers presented in this volume were carefully reviewed and selected from 111 submissions. The were organized in topical sections on haptic science, haptic technology, and haptic applications. This year's focus is on accessibility

Haptics Rendering and Applications

There has been significant progress in haptic technologies but the incorporation of haptics into virtual environments is still in its infancy. A wide range of the new society's human activities including communication, education, art, entertainment, commerce and science would forever change if we learned how to capture, manipulate and reproduce haptic sensory stimuli that are nearly indistinguishable from reality. For the field to move forward, many commercial and technological barriers need to be overcome. By rendering how objects feel through haptic technology, we communicate information that might reflect a desire to speak a physically- based language that has never been explored before. Due to constant improvement in haptics technology and increasing levels of research into and development of haptics-related algorithms, protocols and devices, there is a belief that haptics technology has a promising future