Integrating intelligence and knowledge of human factors to facilitate collaboration in manufacturing

The implementation of automation has become a common occurrence in recent years, and automated robotic systems are actively used in many manufacturing processes. However, fully automated manufacturing systems are far less common, and human operators remain prevalent. The resulting scenario is one where human and robotic operators work in close proximity, and directly affect the behavior of one another. Conversely to their robotic counterparts, human beings do not share the same level of repeatability or accuracy, and as such can be a source of uncertainty in such processes. Concurrently, the emergence of intelligent manufacturing has presented opportunities for adaptability within robotic control. This work examines relevant human factors and develops a learning model to examine how to utilize this knowledge and provide appropriate adaptability to robotic elements, with the intention of improving collaborative interaction with human colleagues, and optimized performance. The work is supported by an example case-study, which explores the application of such a control system, and its performance in a real-world production scenario

Improving human-robot-interaction utilizing learning and intelligence: a human factors-based approach

Several decades of development in the fields of robotics and automation have resulted in human-robot interaction is commonplace, and the subject of intense study. These interactions are particularly prevalent in manufacturing, where human operators (HOs) have been employed in numerous robotics and automation tasks. The presence of HOs continues to be a source of uncertainty in such systems, despite the study of human factors, in an attempt to better understand these variations in performance. Concurrent developments in intelligent manufacturing present opportunities for adaptability within robotic control. This article examines relevant human factors and develops a framework for integrating the necessary elements of intelligent control and data processing to provide appropriate adaptability to robotic elements, consequently improving collaborative interaction with human colleagues. A neural network-based learning approach is used to predict the influence on human task performance and use these predictions to make informed changes to programed behavior, and a methodology developed to explore the application of learning techniques to this area further. This article is supported by an example case study, in which a simulation model is used to explore the application of the developed system, and its performance in a real-world production scenario. The simulation results reveal that adaptability can be realized with some relatively simple techniques and models if applied in the right manner and that such adaptability is helpful to tackle the issue of performance disparity in manufacturing operations

Beating the world's best at Super Smash Bros. with deep reinforcement learning

Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.Cataloged from PDF version of thesis.Includes bibliographical references (page 29).There has been a recent explosion in the capabilities of game-playing artificial intelligence. Many classes of RL tasks, from Atari games to motor control to board games, are now solvable by fairly generic algorithms, based on deep learning, that learn to play from experience with often minimal knowledge of the specific domain of interest. In this work, we will investigate the performance of these methods on Super Smash Bros. Melee (SSBM), a popular multiplayer fighting game. The SSBM environment has complex dynamics and partial observability, making it challenging for man and machine alike. The multiplayer aspect poses an additional challenge, as the vast majority of recent advances in RL have focused on single-agent environments. Nonetheless, we will show that it is possible to train agents that are competitive against and even surpass human professionals, a new result for the video game setting..by Vlad Firoiu.S.M

Kansas Farmer and Mail & Breeze, v. 58, no. 12 (March 20, 1920)

Published as: Kansas Farmer, Vol. 1, no. 1 (May 1, 1863)-v. 57, no. 49 (Dec. 6, 1919); Kansas Farmer and Mail & Breeze, Vol. 57, no. 50 (Dec 13, 1919)-v. 64, no. 9 (Feb 27, 1926); Kansas Farmer, Mail & Breeze, Vol. 64, no. 10 (Mar. 6, 1926)-v. 70, no. 1 (Jan. 9, 1932); Kansas Farmer Continuing Mail & Breeze, Vol. 70, no. 2 (Jan. 23, 1932)-v. 76, no. 8 (Apr. 22, 1939); Kansas Farmer, Mail & Breeze, Vol. 76, no. 9 (May 6, 1939)-v. 77, no. 20 (Oct. 5, 1940); Kansas Farmer Continuing Mail & Breeze, Vol. 77, no. 21 (Oct. 19, 1940)-v. 91, no. 3 (Feb. 6, 1954).Issued by Kansas Farmer Co., 1863-1919; Kansas Farmer and Mail & Breeze, 1919-1926; Kansas Farmer, 1926-1954.Missing issues and volumes arranged chronologically by date and journal name: Kansas Farmer: vol. 6, nos. 2-8, 10 and 12 (1869); vol. 9, no. 10 (1872); vol. 14, no. 50 (1876); vol. 18, nos. 1, 12 and 13 (1880); vol. 24. no. 16 (1886); vol. 35 (1897); vol. 38 (1900); vol. 41, nos. 52 and 53 (1903); vol. 42, nos. 17 and 35 (1904); vol. 48, nos. 11 and 53 (1910); vol. 50, nos. 45-50 (1912); vol. 53 (1915); vol. 56 (1918); vol. 49, no. 39 (1919); Kansas Farmer, Continuing Mail & Breeze: vol. 73 (1935); vol. 85, nos. 9-17 (1948); and The Farmers Mail and Breeze: vol. 49, no. 39 (1919).Call number: S544.3.K3 K3

The Question: Could a multi-sensory approach to design facilitate a re-enchantment of the food industry in Britain?

This thesis explores the potential of design industries ability to re-enchant the food industry in Britain in 2007. My research is informed by the increasing evidence of the negative impact on human and biosphere wellbeing and industrialization practice in food production and marketing. I highlight the connection between design's promotion of the hegemony of visuality and the marginalization of opportunities to construct connections between food source and its quality through multi-sensory engagement. I have adapted Webber's (2000) idea of disenchantment to describe a condition .in which the deterioration of quality of food experience. I argue that industrialization has created a loss of intangible qualities and traditions that have a clear potential to provide deep sources of pleasure and meaning to participants. I have focused on the relationship between design and food in order to evidence how design has become a tool of instrumental rationality by primarily servicing the short-term economic agendas of corporate business. I argue that design's focus on the role of seduction has led to the marginalization of a latent ability to connect consumers and producers to value through their non-visual senses. I propose that a multi-sensory form of design is capable of informing the restoration/creation of a deeper and more reflective relationship with the food chain. I argue that the route to this outcome is through the re-evaluation and re-education of the role that multi-sensory aesthetics play in the construction of promoting more benign rituals of production and consumption. I use evidence of multi-sensory practice in the non-industrialized and ethical food sector as an analogy and source that could sensory awareness to the designer's portfolio. I draw on a wide range of evidence to inform and support my explanation of the origins and character of the syndrome of industrialized production, marketing and consumption. My goal is informed by a concern to demonstrate that multi-sensory design could support the viability of alternative production and consumption strategie

An agent-based reinforcement learning approach to improve human-robot-interaction in manufacturing

This work is aimed at the understanding and application of several emerging technologies as they relate to improving the interactions which occur between robotic operators and their human colleagues across a range of manufacturing processes. These interactions are problematic, as variation in performance of human beings remains one of the largest sources of disturbances within such systems, with potentially significant implications for productivity if it continues unmitigated. The problem remains for the most part unaddressed, despite these interactions becoming increasingly prevalent as the rate of adoption of automation technologies increases. By reconciling multiple areas encompassed by the wider domain of intelligent manufacturing, the presented work identifies a methodology and a set of software tools which leverage the strengths of neural-network-based reinforcement learning to develop intelligent software agents capable of adaptable behaviour in response to observed environmental changes. The methodology further focuses on developing representative simulation models for these interactions following a pattern of generalisation, to effectively represent both human and robotic elements, and facilitate implementation. By learning through their interaction with the simulated manufacturing environment, these agents can determine an appropriate policy, by which to autonomously adjust their operating parameters, as a response to changes in their human colleagues. This adaptability is demonstrated to enable the intelligent agents to determine an action policy which results in less observed idle time, along with improved leanness and overall productivity, over multiple scenarios. The findings of the work suggest that software agents that make use of a reinforcement based learning approach are well suited to the task of enabling robotic adaptability in such a way, and the developed methodology provides a platform for further development and exploration, along with numerous insights into the effective development of these agents