Tactile Transfer Learning and Object Recognition With a Multifingered Hand Using Morphology Specific Convolutional Neural Networks.

Multifingered robot hands can be extremely effective in physically exploring and recognizing objects, especially if they are extensively covered with distributed tactile sensors. Convolutional neural networks (CNNs) have been proven successful in processing high dimensional data, such as camera images, and are, therefore, very well suited to analyze distributed tactile information as well. However, a major challenge is to organize tactile inputs coming from different locations on the hand in a coherent structure that could leverage the computational properties of the CNN. Therefore, we introduce a morphology-specific CNN (MS-CNN), in which hierarchical convolutional layers are formed following the physical configuration of the tactile sensors on the robot. We equipped a four-fingered Allegro robot hand with several uSkin tactile sensors; overall, the hand is covered with 240 sensitive elements, each one measuring three-axis contact force. The MS-CNN layers process the tactile data hierarchically: at the level of small local clusters first, then each finger, and then the entire hand. We show experimentally that, after training, the robot hand can successfully recognize objects by a single touch, with a recognition rate of over 95%. Interestingly, the learned MS-CNN representation transfers well to novel tasks: by adding a limited amount of data about new objects, the network can recognize nine types of physical properties

Artificial Intelligence and Ambient Intelligence

This book includes a series of scientific papers published in the Special Issue on Artificial Intelligence and Ambient Intelligence at the journal Electronics MDPI. The book starts with an opinion paper on “Relations between Electronics, Artificial Intelligence and Information Society through Information Society Rules”, presenting relations between information society, electronics and artificial intelligence mainly through twenty-four IS laws. After that, the book continues with a series of technical papers that present applications of Artificial Intelligence and Ambient Intelligence in a variety of fields including affective computing, privacy and security in smart environments, and robotics. More specifically, the first part presents usage of Artificial Intelligence (AI) methods in combination with wearable devices (e.g., smartphones and wristbands) for recognizing human psychological states (e.g., emotions and cognitive load). The second part presents usage of AI methods in combination with laser sensors or Wi-Fi signals for improving security in smart buildings by identifying and counting the number of visitors. The last part presents usage of AI methods in robotics for improving robots’ ability for object gripping manipulation and perception. The language of the book is rather technical, thus the intended audience are scientists and researchers who have at least some basic knowledge in computer science

Tactile myography: an off-line assessment on intact subjects and one upper-limb disabled

Castellini C, Kõiva R, Pasluosta C, Viegas C, Eskofier BM. Tactile myography: an off-line assessment on intact subjects and one upper-limb disabled. Technologies / SI: Assistive Robotics. 2018;6(2): 38.Human-machine interfaces to control prosthetic devices still suffer from scarce dexterity and low reliability; for this reason, the community of assistive robotics is exploring novel solutions to the problem of myocontrol. In this work, we present experimental results pointing in the direction that one such method, namely Tactile Myography (TMG), can improve the situation. In particular, we use a shape-conformable high-resolution tactile bracelet wrapped around the forearm/residual limb to discriminate several wrist and finger activations performed by able-bodied subjects and a trans-radial amputee. Several combinations of features/classifiers were tested to discriminate among the activations. The balanced accuracy obtained by the best classifier/feature combination was on average 89.15% (able-bodied subjects) and 88.72% (amputated subject); when considering wrist activations only, the results were on average 98.44% for the able-bodied subjects and 98.72% for the amputee. The results obtained from the amputee were comparable to those obtained by the able-bodied subjects. This suggests that TMG is a viable technique for myoprosthetic control, either as a replacement of or as a companion to traditional surface electromyography

Human-Machine Interfaces using Distributed Sensing and Stimulation Systems

As the technology moves towards more natural human-machine interfaces (e.g. bionic limbs, teleoperation, virtual reality), it is necessary to develop a sensory feedback system in order to foster embodiment and achieve better immersion in the control system. Contemporary feedback interfaces presented in research use few sensors and stimulation units to feedback at most two discrete feedback variables (e.g. grasping force and aperture), whereas the human sense of touch relies on a distributed network of mechanoreceptors providing a wide bandwidth of information. To provide this type of feedback, it is necessary to develop a distributed sensing system that could extract a wide range of information during the interaction between the robot and the environment. In addition, a distributed feedback interface is needed to deliver such information to the user. This thesis proposes the development of a distributed sensing system (e-skin) to acquire tactile sensation, a first integration of distributed sensing system on a robotic hand, the development of a sensory feedback system that compromises the distributed sensing system and a distributed stimulation system, and finally the implementation of deep learning methods for the classification of tactile data. It\u2019s core focus addresses the development and testing of a sensory feedback system, based on the latest distributed sensing and stimulation techniques. To this end, the thesis is comprised of two introductory chapters that describe the state of art in the field, the objectives, and the used methodology and contributions; as well as six studies that tackled the development of human-machine interfaces

Review of machine learning methods in soft robotics

Soft robots have been extensively researched due to their flexible, deformable, and adaptive characteristics. However, compared to rigid robots, soft robots have issues in modeling, calibration, and control in that the innate characteristics of the soft materials can cause complex behaviors due to non-linearity and hysteresis. To overcome these limitations, recent studies have applied various approaches based on machine learning. This paper presents existing machine learning techniques in the soft robotic fields and categorizes the implementation of machine learning approaches in different soft robotic applications, which include soft sensors, soft actuators, and applications such as soft wearable robots. An analysis of the trends of different machine learning approaches with respect to different types of soft robot applications is presented; in addition to the current limitations in the research field, followed by a summary of the existing machine learning methods for soft robots