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RECENT VIDEOS

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05.21.2014
42 mins
ILP Video

Resilient Cities - Design for Growth

Kent Larson
Principal Research Scientist
Director, Changing Places
MIT Media Laboratory
Accelerating urban expansion demands resilient urban design; cities must be simultaneously livable and adaptable. Networks of compact integrated neighborhoods connected by mobility-on-demand pathways offer a vision for cities where residences, offices, shops, and parks are always within a 20-minute walk; where affordable, convenient, shared-use light electric vehicles replace private cars; where congestion and local sources of air pollution are essentially eliminated; and where powerful new applications improve life for each resident, all while allowing for continued development and growth.
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05.21.2014
35 mins
ILP Video

Urban Metabolism and City Typologies

John Fernández
Full Professor, Department of Architecture and Engineering Systems
Head, Building Technology Program
Co-Director, International Design Center, Singapore University of Technology and Design
MIT Department of Architecture
Prof. Fernandez will discuss the emerging field of urban metabolism; the study of the resource intensity of urban economies. Urban metabolism endeavors to establish broad understanding of the relationship between human activities and material and energy flows. Measures of resource intensity are derived to provide pathways for urban sustainability transitions and assist designers, engineers, and policy makers with the challenges facing 21st century cities. Fernandez will present a series of research projects and results of the Urban Metabolism Group at MIT.
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05.21.2014
8 mins
ILP Video

Special Presentation: MIT Professional Education (2014 Europe Conference)

Bhaskar Pant
Executive Director
MIT Professional Education
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05.21.2014
34 mins
ILP Video

Self-Assembly and Programmable Materials

Skylar Tibbits
Lecturer
Director, Self-Assembly Lab
Founder & Principal, SJET LLC
MIT Department of Architecture
The making of our human-scale world is outdated, energy intensive, error-prone and inefficient. From the laborious mass-customization of the maker-movement to consumer product assembly lines, the construction industry and out-dated infrastructural systems, an opportunity has emerged to revolutionize the assembly of our physical world. As demonstrated across recent developments in nanotechnology, synthetic biology and the biomedical industry, the phenomena of self-assembly and programmable matter similarly offer a radical solution at much larger scales. Self-assembly is a scale-independent technology that allows components to come together on their own and transform shape or property for highly efficient and programmably adaptive systems. The combination of additive manufacturing and programmable materials, or 4D Printing, offers one technological solution for the smart assembly of our future world.
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05.21.2014
47 mins
ILP Video

Goal-directed Systems for Highly Reconfigurable Manufacturing and Human-Robot Teamwork

Brian Williams
Professor of Aeronautics and Astronautics
Director Model-based Embedded and Robotic Systems Group
Computer Science and Artificial Intelligence Laboratory, MIT
MIT Department of Aeronautics and Astronautics
Industry is driving towards rapid manufacturing of systems with increased complexity, while reducing lot size, and re-tooling time and cost. To support these trends, the Model-based Embedded and Robotic Systems (MERS) group at CSAIL, MIT is developing task-executives that enable manufacturing lines to be highly reconfigurable, and enable humans and robots to work together as teams.

The MERS group has a long track record of developing a wide range of systems that are easy for humans to interact with, that are robust and that are highly reconfigurable. We accomplish this through the development of "task executives" for autonomous systems and robots that 1) are commanded in plain English in terms of high-level goals, 2) are able to plan novel ways to achieve these goals, 3) respond intelligently to disturbances encountered when executing these novel plans, and 4) are sensitive to the risk that these novel plans may fail. Applications over the last 25 years range from NASA's Deep Space One probe to autonomous air taxis and manufacturing robots. Additional applications include automobiles, copiers, autonomous air vehicles and submarines, naval ships and smart buildings.

In the context of manufacturing, our goal is to improve the robustness and reconfigurability of manufacturing robots individually and the manufacturing line as a whole. In this talk we demonstrate progress towards three objectives: our first objective is to enable manufacturing robots to perform coordinated tasks robustly in semi-structured environments, and along side humans. In support of this, we use machine-learning technologies to allow robots to learn aspects of new tasks and new configurations on their own, and to learn to perform their tasks more efficiently. Our second objective is to make it simple, fast and cost effective to bring new tasks on line, by reconfiguring robots and the manufacturing line. This is enabled through domain independent, “generative” planning methods that construct novel solutions using composable models. Our final objective is to enable the manufacturing line, as well as individual robots, to optimize and adapt dynamically, for example through rescheduling and reallocation of tasks, while guaranteeing that the risk of task failure is within an operator specified limit. This is enabled through online stochastic methods for “chance-constrained” decision-making. We demonstrate our progress towards each objective in the context of aerospace manufacturing.
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