Complex Human Tasks Research Articles

Research on LFD System of Humanoid Dual-Arm Robot

Although robots have been widely used in a variety of fields, the idea of enabling them to perform multiple tasks in the same way that humans do remains a difficulty. To solve this, we investigate the learning from demonstration (LFD) system with our independently designed symmetrical humanoid dual-arm robot. We present a novel action feature matching algorithm. This algorithm accurately transforms human demonstration data into task models that robots can directly execute, considerably improving LFD’s generalization capabilities. In our studies, we used motion capture cameras to capture human demonstration actions, which included combinations of simple actions (the action layer) and a succession of complicated operational tasks (the task layer). For the action layer data, we employed Gaussian mixture models (GMM) for processing and constructing an action primitive library. As for the task layer data, we created a “keyframe” segmentation method to transform this data into a series of action primitives and build another action primitive library. Guided by our algorithm, the robot successfully imitated complex human tasks. Results show its excellent task learning and execution, providing an effective solution for robots to learn from human demonstrations and significantly advancing robot technology.

Unlocking digital archives: cross-disciplinary perspectives on AI and born-digital data

Co-authored by a Computer Scientist and a Digital Humanist, this article examines the challenges faced by cultural heritage institutions in the digital age, which have led to the closure of the vast majority of born-digital archival collections. It focuses particularly on cultural organizations such as libraries, museums and archives, used by historians, literary scholars and other Humanities scholars. Most born-digital records held by cultural organizations are inaccessible due to privacy, copyright, commercial and technical issues. Even when born-digital data are publicly available (as in the case of web archives), users often need to physically travel to repositories such as the British Library or the Bibliothèque Nationale de France to consult web pages. Provided with enough sample data from which to learn and train their models, AI, and more specifically machine learning algorithms, offer the opportunity to improve and ease the access to digital archives by learning to perform complex human tasks. These vary from providing intelligent support for searching the archives to automate tedious and time-consuming tasks. In this article, we focus on sensitivity review as a practical solution to unlock digital archives that would allow archival institutions to make non-sensitive information available. This promise to make archives more accessible does not come free of warnings for potential pitfalls and risks: inherent errors, "black box" approaches that make the algorithm inscrutable, and risks related to bias, fake, or partial information. Our central argument is that AI can deliver its promise to make digital archival collections more accessible, but it also creates new challenges - particularly in terms of ethics. In the conclusion, we insist on the importance of fairness, accountability and transparency in the process of making digital archives more accessible.

Taxonomy of Adaptive Neuro-Fuzzy Inference System in Modern Engineering Sciences.

Adaptive Neuro-Fuzzy Inference System (ANFIS) blends advantages of both Artificial Neural Networks (ANNs) and Fuzzy Logic (FL) in a single framework. It provides accelerated learning capacity and adaptive interpretation capabilities to model complex patterns and apprehends nonlinear relationships. ANFIS has been applied and practiced in various domains and provided solutions to commonly recurring problems with improved time and space complexity. Standard ANFIS has certain limitations such as high computational expense, loss of interpretability in larger inputs, curse of dimensionality, and selection of appropriate membership functions. This paper summarizes that the standard ANFIS is unsuitable for complex human tasks that require precise handling of machines and systems. The state-of-the-art and practice research questions have been discussed, which primarily focus on the applicability of ANFIS in the diversifying field of engineering sciences. We conclude that the standard ANFIS architecture is vastly improved when amalgamated with metaheuristic techniques and further moderated with nature-inspired algorithms through calibration and tuning of parameters. It is significant in adapting and automating complex engineering tasks that currently depend on human discretion, prominent in the mechanical, electrical, and geological fields.

The intelligent reflex

ABSTRACTThe seeming distinction between motor and cognitive skills has hinged on the fact that the former are automatic and non-propositional (knowing-how), whereas the latter are slow and deliberative (knowing-that). Here, the physiological and behavioral phenomenon of long-latency stretch reflexes is used to show that “knowing-that” can be incorporated into “knowing-how,” either immediately or through learning. The experimental demonstration that slow computations can, with practice, be cached for fast retrieval, without the need for re-computation, dissolves the intellectualist/anti-intellectualist distinction: All complex human tasks, at any level of expertise, are a combination of intelligent reflexes and deliberative decisions.

FPGA implementation of deep-learning recurrent neural networks with sub-millisecond real-time latency for BCI-decoding of large-scale neural sensors (104 nodes).

Advances in neurotechnology are expected to provide access to thousands of neural channel recordings including neuronal spiking, multiunit activity and local field potentials. In addition, recent studies have shown that deep learning, in particular recurrent neural networks (RNNs), provide promising approaches for decoding of large-scale neural data. These approaches involve computationally intensive algorithms with millions of parameters. In this context, an important challenge in the application of neural decoding to next generation brain-computer interfaces for complex human tasks is the development of low-latency real-time implementations. We demonstrate a Field-Programmable Gate Array (FPGA) implementation of Long Short-Term Memory (LSTM) RNNs for decoding 10,000 channels of neural data on a mobile lowpower embedded system platform called "NeuroCoder". We provide a proof of concept in the context of decoding 20dimensional spectrotemporal representation of spoken words from simulated 10,000 neural channels. In this particular case, the LSTM model included 4,042,420 parameters. In addition to providing multiple communication interfaces for the BCI system, the NeuroCoder platform can achieve sub-millisecond real-time latencies.

Toward Deep Reinforcement Learning Without a Simulator: An Autonomous Steering Example

We propose a scheme for training a computerized agent to perform complex human tasks such as highway steering. The scheme is designed to follow a natural learning process whereby a human instructor teaches a computerized trainee. It enables leveraging the weak supervision abilities of a (human) instructor, who, while unable to perform well herself at the required task, can provide coherent and learnable instantaneous reward signals to the computerized trainee. The learning process consists of three supervised elements followed by reinforcement learning. The supervised learning stages are: (i) supervised imitation learning; (ii) supervised reward induction; and (iii) supervised safety module construction. We implemented this scheme using deep convolutional networks and applied it to successfully create a computerized agent capable of autonomous highway steering over the well-known racing game Assetto Corsa. We demonstrate that the use of all components is essential to effectively carry out reinforcement learning of the steering task using vision alone, without access to a driving simulator internals, and operating in wall-clock time.

An architecture for immersive evaluation of complex human tasks

We investigate how the movement measurement technologies developed for virtual reality applications can be applied to track in real-time the full body posture of a human being. The accuracy of this information is of definite importance to evaluate the feasibility of complex tasks involving human beings. We present a full body movement measurement approach. It provides a realistic conversion in real-time with a reasonable number of sensors. Associated with the hand movement measurement and correction algorithms we provide a pertinent visual and vibrotactile feedback to the performer.

Robot evolution: the development of anthrobotics

From the Publisher: Since the creation of the first modern robots in the 1950s, robotics has developed rapidly and in diverse directions; the term robot (from the Czech word for drudgery) now applies to a spectrum of creations, from limbs bolted to factory floors to computer-driven bipeds with human-like capabilities. But the urge to create mechanical men to perform mundane, repetitive, and even complex human tasks is nearly as old as civilization itself. The ancient Greeks built automata, as did the Egyptians and the Japanese. Leonardo da Vinci designed men, and entertainment robots were all the rage in eighteenth-century Europe. Robot Evolution is unique in robotics literature, at once a comprehensive pictorial history of robots and a technical guide to robot designs, devices, and systems. Author and robot expert Mark E. Rosheim reviews and describes the gamut of robot mechanisms, from ancient to state-of-the-art, from subcomponents such as joints, grippers, and actuators to completely integrated systems equipped with artificial intelligence, sensors, and autonomous mobility. Rosheim chronicles the development and increasing complexity of these systems, using the kinesiology of human body parts as a framework for evaluating the kinematics of robotic components and explaining how these components are used to emulate human motion. Particular emphasis is placed on the most advanced current devices and promising experimental designs. Supplemented with hundreds of photographs, drawings, and illustrated tables, Robot Evolution is written in a clear, forthright style and organized to provide quick and easy access to information. Separate chapters are devoted to robot arms, wrists, hands, and legs, and each chapter contains examples of several different design approaches to the same problem or component. The advantages and disadvantages of each design are discussed in detail along with preferred applications and specific functions of each device. An annotated bib