In this paper, we propose a set of novel tactile descriptors to enable robotic systems to extract robust tactile information during tactile object explorations, regardless of the number of the ...tactile sensors, sensing technologies, type of exploratory movements, and duration of the objects' surface exploration. The performance and robustness of the tactile descriptors are verified by testing on four different sensing technologies (dynamic pressure sensors, accelerometers, capacitive sensors, and impedance electrode arrays) with two robotic platforms (one anthropomorphic hand and one humanoid), and with a large set of objects and materials. Using our proposed tactile descriptors, the Shadow Hand, which has multimodal robotic skin on its fingertips, successfully classified 120 materials (100% accuracy) and 30 in-hand objects (98% accuracy) with regular and irregular textural structure by executing human-like active exploratory movements on their surface. The robustness of the proposed descriptors was assessed further during the large object discrimination with a humanoid. With a large sensing area on its upper body, the humanoid classified 120 large objects with multiple weights and various textures while the objects slid between its sensitive hands, arms, and chest. The achieved 90% recognition rate shows that the proposed tactile descriptors provided robust tactile information from the large number of tactile signals for identifying large objects via their surface texture regardless of their weight.
In this paper, we propose that experimental protocols involving artificial agents, in particular the embodied humanoid robots, provide insightful information regarding social cognitive mechanisms in ...the human brain. Using artificial agents allows for manipulation and control of various parameters of behaviour, appearance and expressiveness in one of the interaction partners (the artificial agent), and for examining effect of these parameters on the other interaction partner (the human). At the same time, using artificial agents means introducing the presence of artificial, yet human-like, systems into the human social sphere. This allows for testing in a controlled, but ecologically valid, manner human fundamental mechanisms of social cognition both at the behavioural and at the neural level. This paper will review existing literature that reports studies in which artificial embodied agents have been used to study social cognition and will address the question of whether various mechanisms of social cognition (ranging from lower- to higher-order cognitive processes) are evoked by artificial agents to the same extent as by natural agents, humans in particular. Increasing the understanding of how behavioural and neural mechanisms of social cognition respond to artificial anthropomorphic agents provides empirical answers to the conundrum ‘What is a social agent?’
In this study, we present a framework that infers human activities from observations using semantic representations. The proposed framework can be utilized to address the difficult and challenging ...problem of transferring tasks and skills to humanoid robots. We propose a method that allows robots to obtain and determine a higher-level understanding of a demonstrator's behavior via semantic representations. This abstraction from observations captures the “essence” of the activity, thereby indicating which aspect of the demonstrator's actions should be executed in order to accomplish the required activity. Thus, a meaningful semantic description is obtained in terms of human motions and object properties. In addition, we validated the semantic rules obtained in different conditions, i.e., three different and complex kitchen activities: 1) making a pancake; 2) making a sandwich; and 3) setting the table. We present quantitative and qualitative results, which demonstrate that without any further training, our system can deal with time restrictions, different execution styles of the same task by several participants, and different labeling strategies. This means, the rules obtained from one scenario are still valid even for new situations, which demonstrates that the inferred representations do not depend on the task performed. The results show that our system correctly recognized human behaviors in real-time in around 87.44% of cases, which was even better than a random participant recognizing the behaviors of another human (about 76.68%). In particular, the semantic rules acquired can be used to effectively improve the dynamic growth of the ontology-based knowledge representation. Hence, this method can be used flexibly across different demonstrations and constraints to infer and achieve a similar goal to that observed. Furthermore, the inference capability introduced in this study was integrated into a joint space control loop for a humanoid robot, an iCub, for achieving similar goals to the human demonstrator online.
Monitoring of finger manipulation without disturbing the inherent functionalities is critical to understand the sense of natural touch. However, worn or attached sensors affect the natural feeling of ...the skin. We developed nanomesh pressure sensors that can monitor finger pressure without detectable effects on human sensation. The effect of the sensor on human sensation was quantitatively investigated, and the sensor-applied finger exhibits comparable grip forces with those of the bare finger, even though the attachment of a 2-micrometer-thick polymeric film results in a 14% increase in the grip force after adjusting for friction. Simultaneously, the sensor exhibits an extreme mechanical durability against cyclic shearing and friction greater than hundreds of kilopascals.
The tasks of exploring unknown workspaces and recognizing objects based on their physical properties are challenging for autonomous robots. In this paper, we present strategies solely based on ...tactile information to enable robots to accomplish such tasks. (1) An active exploration approach for the robot to explore unknown workspaces; (2) an active touch objects learning method that enables the robot to learn efficiently about unknown objects via their physical properties (stiffness, surface texture, and center of mass); and (3) an active object recognition strategy, based on the knowledge the robot has acquired. Furthermore, we propose a tactile-based approach for estimating the center of mass of rigid objects. Following the active touch for workspace exploration, the robotic system with the sense of touch in fingertips reduces the uncertainty of the workspace up to 65 and 70% compared respectively to uniform and random strategies, for a fixed number of samples. By means of the active touch learning method, the robot achieved 20 and 15% higher learning accuracy for the same number of training samples compared to uniform strategy and random strategy, respectively. Taking advantage of the prior knowledge obtained during the active touch learning, the robot took up to 15% fewer decision steps compared to the random method to achieve the same discrimination accuracy in active object discrimination task.
This article presents a holistic approach to the engineering of an artificial robot skin for robots. An example of a multimodal skin cell is given, one that supports multiple human-like sensing ...modalities, and support for skin cell network is also provided; this is essential to form large-area skin patches in order to cover the surfaces of robots. The essential elements of efficiently handling a large amount of tactile data are explained. A general control framework, which supports robots commanded in position, velocity, and torque, is provided and validated. Several applications of this robot skin will be presented, demonstrating the effectiveness and efficiency of our artificial robot skin to support a wide number of robotic platforms as well as its ease of use across different domains.
The prefrontal cortex (PFC) subserves complex cognitive abilities, including risky decision-making; the modulation of this brain area is shown to alter the way people take risks. Yet, neuromodulation ...of the PFC in relation to risk-taking behavior remains relatively less well-studied. Moreover, the psychological variables that influence such neuromodulation remain poorly understood. To address these issues, 16 participants took part in 3 experimental sessions on separate days. They received: (i) left anodal-right cathodal transcranial direct current stimulation (tDCS); (ii) left cathodal-right anodal stimulation; or (iii) sham stimulation while they completed two risk-taking tasks. They also measured on several cognitive-affective abilities and personality traits. It was revealed that left cathodal-right anodal stimulation led to significantly reduced risk-taking under a context of haste. The reduction of risk-taking (relative to sham) correlated with state and trait impulsivity, such that the effect was larger in more impulsive individuals. For these individuals, the tDCS effect size was considered to be large (generalized partial η
2
> .17). The effect of prefrontal-neuromodulation in reducing risk-taking was influenced by baseline impulsivity, reflecting a state-dependent effect of neuromodulation on the PFC. The results of this study carry important insights into the use of neuromodulation to alter higher cognition.
Non-invasive, electroencephalography (EEG)-based brain-computer interfaces (BCIs) on motor imagery movements translate the subject's motor intention into control signals through classifying the EEG ...patterns caused by different imagination tasks, e.g., hand movements. This type of BCI has been widely studied and used as an alternative mode of communication and environmental control for disabled patients, such as those suffering from a brainstem stroke or a spinal cord injury (SCI). Notwithstanding the success of traditional machine learning methods in classifying EEG signals, these methods still rely on hand-crafted features. The extraction of such features is a difficult task due to the high non-stationarity of EEG signals, which is a major cause by the stagnating progress in classification performance. Remarkable advances in deep learning methods allow end-to-end learning without any feature engineering, which could benefit BCI motor imagery applications. We developed three deep learning models: (1) A long short-term memory (LSTM); (2) a spectrogram-based convolutional neural network model (CNN); and (3) a recurrent convolutional neural network (RCNN), for decoding motor imagery movements directly from raw EEG signals without (any manual) feature engineering. Results were evaluated on our own publicly available, EEG data collected from 20 subjects and on an existing dataset known as 2b EEG dataset from "BCI Competition IV". Overall, better classification performance was achieved with deep learning models compared to state-of-the art machine learning techniques, which could chart a route ahead for developing new robust techniques for EEG signal decoding. We underpin this point by demonstrating the successful real-time control of a robotic arm using our CNN based BCI.
A wide variety of tactile (touch) sensors exist today for robotics and related applications. They make use of various transduction methods, smart materials and engineered structures, complex ...electronics, and sophisticated data processing. While highly useful in themselves, effective utilization of tactile sensors in robotics applications has been slow to come and largely remains elusive today. This paper surveys the state of the art and the research issues in this area, with the emphasis on effective utilization of tactile sensors in robotic systems. One specific with the use of tactile sensing in robotics is that the sensors have to be spread along the robot body, the way the human skin is-thus dictating varied 3-D spatio-temporal requirements, decentralized and distributed control, and handling of multiple simultaneous tactile contacts. Satisfying these requirements pose challenges to making tactile sensor modality a reality. Overcoming these challenges requires dealing with issues such as sensors placement, electronic/mechanical hardware, methods to access and acquire signals, automatic calibration techniques, and algorithms to process and interpret sensing data in real time. We survey this field from a system perspective, recognizing the fact that the system performance tends to depend on how its various components are put together. It is hoped that the survey will be of use to practitioners designing tactile sensing hardware (whole-body or large-patch sensor coverage), and to researchers working on cognitive robotics involving tactile sensing.
Abstract Risky decision-making is subserved by the frontostriatal system, which includes a network of interconnected brain regions known to be dysfunctional in patients with schizophrenia. This study ...aimed to investigate whether and to what extent patients with schizophrenia display a different pattern of risk-taking behavior relative to matched healthy controls. The Balloon Analogue Risk Task (BART) and the Risky-Gains Task were used as naturalistic measures of risk-taking behavior in 25 patients with schizophrenia and 25 controls. Results of the BART revealed that patients behaved more conservatively, and this in turn led to suboptimal risky decision-making. Consistently, patients behaved more conservatively in the Risky-Gains Task. Interestingly, however, they adjusted the pattern of risk-taking following a punished trial similar to controls. These findings indicate that patients have impaired reward but preserved punishment processing. This study complements previous studies on decision-making in schizophrenia and suggests specific rather than widespread abnormalities along the frontostriatal system in schizophrenia.
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