Occupants Check Into NASA’s “Robot Hotel” Aboard the Space Station

The Mobile Base System moves on the Mobile Transporter rail car along truss rails covering the length of the space station. It provides a movable platform for Canadarm2 and Dextre and can access any of eight worksites that feature power connections. Credit: NASA

Storage is just as important aboard the International Space Station as it is on Earth. While the space station is about the size of a football field, the living space inside is much smaller than that. Just as you wouldn’t store garden tools in a house when you could store them in a shed outside, astronauts now have a “housing unit” in which they can store tools for use on the exterior of the space station.

On December 5, 2019, a protective storage unit for robotic tools called Robotic Tool Stowage (RiTS) was among the items launched to station as part of SpaceX’s 19th commercial resupply services mission for NASA. As part of a spacewalk on July 21, NASA astronauts Robert Behnken and Chris Cassidy installed the “robot hotel” where the tools are stored to the station’s Mobile Base System (MBS), where it will remain a permanent fixture. The MBS is a moveable platform that provides power to the external robots. This special location allows RiTS to traverse around the station alongside a robot that will use the tools it stores.

RELL Engineering Development Unit (left) pictured alongside RiTS. Credit: NASA

“RiTS provides thermal and physical protection for tools stored on the outside of station, not only freeing up room on board but also allowing the Canadian Space Agency’s Dextre robot to access them more quickly,” said RiTS Hardware Manager Mark Neuman.

The first step in the RiTS installation process involved preparing the unit inside the space station. The astronauts unpacked RiTS’ occupants from storage – two units of a tool called the Robotic External Leak Locator (RELL) – and affixed them inside RiTS’ aluminum housing.

Astronauts Robert Behnken, Doug Hurley, and Chris Cassidy prepare RiTS for installation. Credit: NASA

“RELL is a great example how robots with the right tools can simplify life for astronauts,” said Neuman. “Dextre can use RELL to detect ammonia leaks, eliminating the need for astronauts to perform the same task during a spacewalk.”

The ability to locate and repair ammonia leaks efficiently is important since ammonia is used to operate station’s cooling system.

The Mobile Base System moves on the Mobile Transporter rail car along truss rails covering the length of the space station. It provides a movable platform for Canadarm2 and Dextre and can access any of eight worksites that feature power connections. Credit: NASA

The installation of RiTS makes the leak location process much more streamlined. Before RiTS, the RELL tools were stored inside the station, and deploying RELL depended on airlock availability and involved waiting an additional 12 hours to allow for RELL’s gas analyzer to clear itself of internal gases. With RiTS, the only variable is Dextre’s availability, expediting the search for leaks.

After it was prepared on station, RiTS – loaded with the two RELL units – was sent outside with the spacewalking astronauts who attached it to the MBS. This was the first task during a spacewalk to upgrade International Space Station systems. The installation required the astronauts to mechanically attach RiTS to an available worksite socket then mate two electrical cables to unused power outlets on the MBS. The power connection was critical to enabling heaters within RiTS that keep the RELL tools from getting too cold.

RiTS installed on the space station. Credit: NASA TV

Although RiTS will be used on the station, human-robot collaborations like this have the potential to be applied to other endeavors that involve human habitats in space, including Gateway.

RiTS was developed by NASA’s Exploration & In-space Services projects division at the agency’s Goddard Space Flight Center in Greenbelt, Maryland, in partnership with NASA’s Johnson Space Center in Houston.

MIT Using Artificial Intelligence to Help Put an End to the COVID-19 Pandemic

C3.ai Digital Transformation Institute awards $5.4 million to top researchers to steer how society responds to the pandemic.

Artificial intelligence has the power to help put an end to the Covid-19 pandemic. Not only can techniques of machine learning and natural language processing be used to track and report Covid-19 infection rates, but other AI techniques can also be used to make smarter decisions about everything from when states should reopen to how vaccines are designed. Now, MIT researchers working on seven groundbreaking projects on Covid-19 will be funded to more rapidly develop and apply novel AI techniques to improve medical response and slow the pandemic spread.

Earlier this year, the C3.ai Digital Transformation Institute (C3.ai DTI) formed, with the goal of attracting the world’s leading scientists to join in a coordinated and innovative effort to advance the digital transformation of businesses, governments, and society. The consortium is dedicated to accelerating advances in research and combining machine learning, artificial intelligence, internet of things, ethics, and public policy — for enhancing societal outcomes. MIT, under the auspices of the School of Engineering, joined the C3.ai DTI consortium, along with C3.ai, Microsoft Corporation, the University of Illinois at Urbana-Champaign, the University of California at Berkeley, Princeton University, the University of Chicago, Carnegie Mellon University, and, most recently, Stanford University.

The initial call for project proposals aimed to embrace the challenge of abating the spread of Covid-19 and advance the knowledge, science, and technologies for mitigating the impact of pandemics using AI. Out of a total of 200 research proposals, 26 projects were selected and awarded $5.4 million to continue AI research to mitigate the impact of Covid-19 in the areas of medicine, urban planning, and public policy.

The first round of grant recipients was recently announced, and among them are five projects led by MIT researchers from across the Institute: Saurabh Amin, associate professor of civil and environmental engineering; Dimitris Bertsimas, the Boeing Leaders for Global Operations Professor of Management; Munther Dahleh, the William A. Coolidge Professor of Electrical Engineering and Computer Science and director of the MIT Institute for Data, Systems, and Society; David Gifford, professor of biological engineering and of electrical engineering and computer science; and Asu Ozdaglar, the MathWorks Professor of Electrical Engineering and Computer Science, head of the Department of Electrical Engineering and Computer Science, and deputy dean of academics for MIT Schwarzman College of Computing.

“We are proud to be a part of this consortium, and to collaborate with peers across higher education, industry, and health care to collectively combat the current pandemic, and to mitigate risk associated with future pandemics,” says Anantha P. Chandrakasan, dean of the School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science. “We are so honored to have the opportunity to accelerate critical Covid-19 research through resources and expertise provided by the C3.ai DTI.”

Additionally, three MIT researchers will collaborate with principal investigators from other institutions on projects blending health and machine learning. Regina Barzilay, the Delta Electronics Professor in the Department of Electrical Engineering and Computer Science, and Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science, join Ziv Bar-Joseph from Carnegie Mellon University for a project using machine learning to seek treatment for Covid-19. Aleksander Mądry, professor of computer science in the Department of Electrical Engineering and Computer Science, joins Sendhil Mullainathan of the University of Chicago for a project using machine learning to support emergency triage of pulmonary collapse due to Covid-19 on the basis of X-rays.

Bertsimas’s project develops automated, interpretable, and scalable decision-making systems based on machine learning and artificial intelligence to support clinical practices and public policies as they respond to the Covid-19 pandemic. When it comes to reopening the economy while containing the spread of the pandemic, Ozdaglar’s research provides quantitative analyses of targeted interventions for different groups that will guide policies calibrated to different risk levels and interaction patterns. Amin is investigating the design of actionable information and effective intervention strategies to support safe mobilization of economic activity and reopening of mobility services in urban systems. Dahleh’s research innovatively uses machine learning to determine how to safeguard schools and universities against the outbreak. Gifford was awarded funding for his project that uses machine learning to develop more informed vaccine designs with improved population coverage, and to develop models of Covid-19 disease severity using individual genotypes.

“The enthusiastic support of the distinguished MIT research community is making a huge contribution to the rapid start and significant progress of the C3.ai Digital Transformation Institute,” says Thomas Siebel, chair and CEO of C3.ai. “It is a privilege to be working with such an accomplished team.”

The following projects are the MIT recipients of the inaugural C3.ai DTI Awards: 

“Pandemic Resilient Urban Mobility: Learning Spatiotemporal Models for Testing, Contact Tracing, and Reopening Decisions” — Saurabh Amin, associate professor of civil and environmental engineering; and Patrick Jaillet, the Dugald C. Jackson Professor of Electrical Engineering and Computer Science

“Effective Cocktail Treatments for SARS-CoV-2 Based on Modeling Lung Single Cell Response Data” — Regina Barzilay, the Delta Electronics Professor in the Department of Electrical Engineering and Computer Science, and Tommi Jaakkola, the Thomas Siebel Professor of Electrical Engineering and Computer Science (Principal investigator: Ziv Bar-Joseph of Carnegie Mellon University)

“Toward Analytics-Based Clinical and Policy Decision Support to Respond to the Covid-19 Pandemic” — Dimitris Bertsimas, the Boeing Leaders for Global Operations Professor of Management and associate dean for business analytics; and Alexandre Jacquillat, assistant professor of operations research and statistics

“Reinforcement Learning to Safeguard Schools and Universities Against the Covid-19 Outbreak” — Munther Dahleh, the William A. Coolidge Professor of Electrical Engineering and Computer Science and director of MIT Institute for Data, Systems, and Society; and Peko Hosoi, the Neil and Jane Pappalardo Professor of Mechanical Engineering and associate dean of engineering

“Machine Learning-Based Vaccine Design and HLA Based Risk Prediction for Viral Infections” — David Gifford, professor of biological engineering and of electrical engineering and computer science

“Machine Learning Support for Emergency Triage of Pulmonary Collapse in Covid-19” — Aleksander Mądry, professor of computer science in the Department of Electrical Engineering and Computer Science (Principal investigator: Sendhil Mullainathan of the University of Chicago)

“Targeted Interventions in Networked and Multi-Risk SIR Models: How to Unlock the Economy During a Pandemic” — Asu Ozdaglar, the MathWorks Professor of Electrical Engineering and Computer Science, department head of electrical engineering and computer science, and deputy dean of academics for MIT Schwarzman College of Computing; and Daron Acemoglu, Institute Professor