“Imagine it, build it” at MIT

MIT class 2.679 (Electronics for Mechanical Systems II) offers a sort of alchemy that transforms students from consumers of knowledge to explorers and innovators, and equips them with a range of important new tools at their disposal, students say.

“Topics which could otherwise feel intimidating are well-scoped each week so that students come out knowing not only what a concept is, but why it’s useful and how to actually implement it,” says graduating senior Audrey Chen. “I could consistently come in with no background and come out with practical experience I could use in future projects. I’d describe the class as a series of small crash courses [each of which] answers, simply, ‘what do I need to know to do or use this thing?’”

The course takes students through the process of design, fabrication, and assembly of a printed circuit board (PCB). Ultimately, that process, which has twists and turns depending on each student’s project idea, culminates in incorporating the PCB into a device — in a sense animating that device to perform a certain function.

“The design intent of 2.679 is to empower students to ‘imagine it, build it,’” says Tonio Buonassisi, professor of mechanical engineering. “Between those two is a universe, and the purpose of this class is to aid aspiring engineers to bridge that gap.”

“Imagine it, build it” at MIT

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Imagine it, build it
Video: Department of Mechanical Engineering

Senior Jessica Lam marvels at how much she learned in the course over its one short semester, attributing that flood of education to the class labs being “incredibly well-structured.”

“I’ve found that in a lot of other labs and project-based classes, they throw a lot of information at you at once with the expectation that you already have some experience with certain software or hardware, and most of it is scaffolded and feels like a black box,” without much understanding of what is actually happening, Lam says. “In 2.679, Steve Banzaert has a better understanding of what we already know and how to build on that.”

After taking 2.679, she says she feels “a lot more confident in designing electrical systems, and I have a more comprehensive understanding of how to integrate mechanical systems and electronics.”

Banzaert, technical instructor for the course, says the class is designed to guide students along their own chosen paths of discovery, showing them that they are able to address the challenges they encounter along the way.

“Every semester we get to see really lovely examples of growth, not just in the course material but, in the best cases, in students’ understanding of what they’re really capable of,” he says.

Chen, a mechanical engineering major who is graduating early to start a position as a hardware project manager at Formlabs, agrees that the class did just that.

“Students are given tremendous freedom to pick their own final projects, allowing them to explore topics which are of special interest to them. And because each project is unique, there is less pressure to ‘perform’ in a traditional sense,” she says. “Rather, each student is learning different skills and is encouraged to get as far along with the project they choose as possible. Steve emphasized that the scope of our projects would inevitably change, because at the start you simply don’t yet know what you don’t know, and that’s totally okay!”

Banzaert says, “We try to make it very clear that, yes, we are talking about important general concepts in theory and analysis, but that’s because they are tools that engineers use to solve problems. I think maybe this focus helps remind the students of what got them here in the first place — that the reason you’re an engineer is because there’s something about the world you wish was better, that you’re the person to do it (or at least help), and, if you want to do it well, you’re going to have to learn a bunch of things so you have more tools in your toolbox.”

Senior Yasin Hamed designed a car in the class that uses computer vision to follow along a black line. The car has an attached camera that captures images and relays them to a Raspberry Pi computer that is also attached to the car. Processing the images in real time allows the car to locate the black line and turn or go straight while controlling the car’s speed.

Although Hamed, who is majoring in mechanical engineering with a minor in computer science, had built another similar system in a previous class, he says the focus in the prior class was on the software. With his 2.679 car project, he learned about “the underlying foundation,” meaning “the design of the power electronics and control circuitry which is necessary for everything else to work.”

“I derived much of the ‘enlightenment’ from this class from the little electronic bits and pieces of information I picked up along the course of the class, like learning/practicing soldering, understand how to use integrated circuits, learning how to design a PCB, etc.,” he says. “It was the collection of all of these things that benefited me the most.”

Jordan Parker-Ashe, also a senior, appreciated how 2.679 combined lessons about electronics with research and presentations from Buonassisi’s lab. “It’s great seeing engineering applied in research,” she says.

Although many of the skills she learned in the course were new to her, one was “an old foe,” she says, that 2.679 allowed her to befriend. Parker-Ashe, who is majoring in nuclear engineering, had used a computer vision program called OpenCV in her first Undergraduate Research Opportunities Program project as a first-year undergraduate.

“It was the hardest thing ever, and it really felt like an insurmountable obstacle then,” she says. “Now, to be using OpenCV in labs and homework effortlessly — It was a very full-circle moment.”

She says the class has opened up a whole new field to her, with Banzaert having “directly inspired” her to also take class 6.131 (Power Electronics), “which has been life-changing,” she says.

“2.679 helped me believe in myself, which inspired me to take 6.131, a notorious electrical engineering capstone, which has made me realize that my future lies as a nuclear-electrical engineering engineer, not just a nuclear engineer,” Parker-Ashe says. “I want to pursue electrical engineering in my future, and that just wasn’t on the table beforehand.

“Not to mention that it’s opened the doors to very rich landscapes for project ideas, creating explorations, art, stepping into new roles in group projects, etc,” she says. “I’m so glad that I’ve been able to find opportunities in Course 2  that helped give me hands-on, applied engineering experience.”

Study finds workers misjudge wage markets

Study finds workers misjudge wage markets

Many employees believe their counterparts at other firms make less in salary than is actually the case — an assumption that costs them money, according to a study co-authored by MIT scholars.

“Workers wrongly anchor their beliefs about outside options on their current wage,” says MIT economist Simon Jäger, co-author of a newly published paper detailing the study’s results.

As a top-line figure, the study indicates that workers who would experience a 10 percent wage increase by switching firms only expect a 1 percent wage increase instead, leading them to earn less than they otherwise might.

That is one of multiple related findings in the study, which also shows that workers in lower-paying firms are highly susceptible to underestimating wages at other companies; and that giving workers correct information about the salary structure in their industry makes them more likely to declare that they intend to leave their current jobs.

The study also has implications for further economics research, since economists’ job-search models generally assume workers have accurate salary information about their industries. The study was performed using data from Germany, although it quite likely applies to other countries as well.

“Misperceptions about outside options have substantial consequences on wages,” says Nina Roussille, an economist at MIT and also a co-author of the paper. “The intuition is simple: If low-wage workers do not know that they could make more elsewhere, then these workers stay put in low-wage firms. In turn, these low-wage firms do not feel the competitive pressure from the external labor market to raise their wages.”

The paper, “Worker Beliefs about Outside Options,” appears in advance online form in the Quarterly Journal of Economics. The authors are Jäger, the Silverman Family Career Development Associate Professor in MIT’s Department of Economics; Christopher Roth, a professor of economics at the University of Cologne; Roussille, an assistant professor in MIT’s Department of Economics; and Benjamin Schoefer, an associate professor of economics at the University of California at Berkeley.

Updating beliefs

To conduct the study, the researchers incorporated a survey module into the Innovation Sample of the German Socio-Economic Panel, an annual survey of a representative sample of the German population. They used their survey questions to find out the nature of worker beliefs about outside employment opportunities. The scholars then linked these findings to actual job and salary data collected from the German government’s Institute for Employment Research (IAB), with the prior consent of 558 survey respondents.

Linking those two data sources allowed the scholars to quantify the mismatch between what workers believe about industry-wide salaries, and what wages are in reality. One good piece of evidence on the compression of those beliefs is that about 56 percent of respondents believe they have a salary in between the 40th and 60th percentiles among comparable workers.

The scholars then added another element to the research project. They conducted an online experiment with 2,448 participants, giving these workers correct information about salaries at other companies, and then measuring the employees’ intention to find other job opportunities, among other things.

By adding this layer to the study, the scholars found that a 10 percentage point increase in the belief about salaries at other firms leads to a 2.6 percentage point increase in a worker intending to leave their present firm.

“This updating of beliefs causes workers to adjust their job search and wage negotiation intentions,” Roussille observes.

While the exact circumstances in every job market may vary somewhat, the researchers think the basic research findings from Germany could well apply in many other places.

“We are confident the results are representative of the German labor market,” Jäger says. “Of course, the German labor market may differ from, say, the U.S. labor market. Our intuition, though, is that, if anything, misperceptions would be even more consequential in a country like the U.S. where wages are more unequal than in Europe.”

Moreover, he adds, the recent dynamics of the U.S. job market during the Covid-19 pandemic, when many workers searched for new work and ended up in higher-paying jobs, is “consistent with the idea that workers had been stuck in low-paying jobs for a long time without realizing that there may have been better opportunities elsewhere.”

Data informing theory

The findings of Jäger, Roth, Roussille, Schoefer stand in contrast to established economic theory in this area, which has often worked from the expectation that employees have an accurate perception of industry wages and make decisions on that basis.

Roussille says the feedback the scholars have received from economics colleagues has been favorable, since other economists perceive “an opportunity to better tailor our models to reality,” as she puts it. “This follows a broader trend in economics in the past 20 to 30 years: The combination of better data collection and access with greater computing power has allowed the field to challenge longstanding but untested assumptions, learn from new empirical evidence, and build more realistic models.”

The findings have also encouraged the scholars to explore the topic further, especially by examining what the state of industry-wide wage knowledge is among employers.

“One natural follow-up to this project would be to better understand the firm side,” Jäger says. “Are firms aware of these misperceptions? Do they also hold inaccurate beliefs about the wages at their competitors?”

To this end, the researchers have already conducted a survey of managers on this topic, and intend to pursue further related work.

Support for the research was provided, in part, by the Sloan Foundation’s Working Longer Program; the Stiftung Grundeinkommen (Basic Income Foundation); and the Deutsche Forschungsgemeinschaft (German Research Foundation) under Germany’s Excellence Strategy.

Researchers help robots navigate efficiently in uncertain environments

Researchers help robots navigate efficiently in uncertain environments

If a robot traveling to a destination has just two possible paths, it needs only to compare the routes’ travel time and probability of success. But if the robot is traversing a complex environment with many possible paths, choosing the best route amid so much uncertainty can quickly become an intractable problem.

MIT researchers developed a method that could help this robot efficiently reason about the best routes to its destination. They created an algorithm for constructing roadmaps of an uncertain environment that balances the tradeoff between roadmap quality and computational efficiency, enabling the robot to quickly find a traversable route that minimizes travel time.

The algorithm starts with paths that are certain to be safe and automatically finds shortcuts the robot could take to reduce the overall travel time. In simulated experiments, the researchers found that their algorithm can achieve a better balance between planning performance and efficiency in comparison to other baselines, which prioritize one or the other.

This algorithm could have applications in areas like exploration, perhaps by helping a robot plan the best way to travel to the edge of a distant crater across the uneven surface of Mars. It could also aid a search-and-rescue drone in finding the quickest route to someone stranded on a remote mountainside.

“It is unrealistic, especially in very large outdoor environments, that you would know exactly where you can and can’t traverse. But if we have just a little bit of information about our environment, we can use that to build a high-quality roadmap,” says Yasmin Veys, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on this technique.

Veys wrote the paper with Martina Stadler Kurtz, a graduate student in the MIT Department of Aeronautics and Astronautics, and senior author Nicholas Roy, an MIT professor of aeronautics and astronautics and a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). The research will be presented at the International Conference on Robotics and Automation.

Generating graphs

To study motion planning, researchers often think about a robot’s environment like a graph, where a series of “edges,” or line segments, represent possible paths between a starting point and a goal.

Veys and her collaborators used a graph representation called the Canadian Traveler’s Problem (CTP), which draws its name from frustrated Canadian motorists who must turn back and find a new route when the road ahead is blocked by snow.

In a CTP, each edge of the graph has a weight associated with it, which represents how long that path will take to traverse, and a probability of how likely it is to be traversable. The goal in a CTP is to minimize travel time to the destination.

The researchers focused on how to automatically generate a CTP graph that effectively represents an uncertain environment.

“If we are navigating in an environment, it is possible that we have some information, so we are not just going in blind. While it isn’t a detailed navigation plan, it gives us a sense of what we are working with. The crux of this work is trying to capture that within the CTP graph,” adds Kurtz.

Their algorithm assumes this partial information — perhaps a satellite image — can be divided into specific areas (a lake might be one area, an open field another, etc.)

Each area has a probability that the robot can travel across it. For instance, it is more likely a nonaquatic robot can drive across a field than through a lake, so the probability for a field would be higher.

The algorithm uses this information to build an initial graph through open space, mapping out a conservative path that is slow but definitely traversable. Then it uses a metric the team developed to determine which edges, or shortcut paths through uncertain regions, should be added to the graph to cut down on the overall travel time.

Selecting shortcuts

By only selecting shortcuts that are likely to be traversable, the algorithm keeps the planning process from becoming needlessly complicated.

“The quality of the motion plan is dependent on the quality of graph. If that graph doesn’t have good paths in it, then the algorithm can’t give you a good plan,” Veys explains.

After testing the algorithm in more than 100 simulated experiments with increasingly complex environments, the researchers found that it could consistently outperform baseline methods that don’t consider probabilities. They also tested it using an aerial campus map of MIT to show that it could be effective in real-world, urban environments.

In the future, they want to enhance the algorithm so it can work in more than two dimensions, which could enable its use for complicated robotic manipulation problems. They are also interested in studying the mismatch between CTP graphs and the real-world environments those graphs represent.

“Robots that operate in the real world are plagued by uncertainty, whether in the available sensor data, prior knowledge about the environment, or about how other agents will behave. Unfortunately, dealing with these uncertainties incurs a high computational cost,” says Seth Hutchinson, professor and KUKA Chair for Robotics in the School of Interactive Computing at Georgia Tech, who was not involved with this research. “This work addresses these issues by proposing a clever approximation scheme that can be used to efficiently compute uncertainty-tolerant plans.”

This research was funded, in part, by the U.S. Army Research Labs under the Distributed Collaborative Intelligent Systems and Technologies Collaborative Research Alliance and by the Joseph T. Corso and Lily Corso Graduate Fellowship.

The Rise of Domain-Specific Language Models

Introduction The field of natural language processing (NLP) and language models has experienced a remarkable transformation in recent years, propelled by the advent of powerful large language models (LLMs) like GPT-4, PaLM, and Llama. These models, trained on massive datasets, have demonstrated an impressive ability to…

Life on Mars, together

Life on Mars, together

Earlier this year, Madelyn Hoying, a PhD student in the Harvard-MIT Program in Health Sciences and Technology, and Wing Lam (Nicole) Chan, an MIT senior in aeronautics and astronautics, were part of Crew 290 at the Mars Desert Research Station (MDRS), the largest and longest-running Mars analog facility in the world. Their six-person crew completed a two-week simulation under the name Project MADMEN (Martian Analysis and Detection of Microbial Environments) — an analog of potential Martian search-for-life missions. 

The mission evolved from Hoying’s NASA Revolutionary Aerospace Systems Concepts – Academic Linkage (NASA RASC-AL) challenge submission, Project ALIEN, during her time as an undergraduate student at Duquesne University. After the challenge concluded, she and her colleagues refined the mission concept and created a test plan that could be conducted in a Mars-analog environment. 

Hoying served as the crew’s commander and health and safety officer, and Chan as the crew’s journalist, documenting daily activities and how the crew experienced life on Mars. The other members of Crew 290 featured three from the original project: Hoying, Rebecca McCallin from Duquesne University, and Benjamin Kazimer from MIT Lincoln Laboratory. Chan, Anja Sheppard from the University of Michigan, and Anna Tretiakova from Boston University joined the team in the next phase. Hoying and Chan had worked together once before in 2022 in another RASC-AL competition. 

“I was initially a bit skeptical of spending two weeks in the middle of nowhere and simply being tasked with writing about what happens every day,” says Chan. “What happens on extravehicular activities (EVAs)? How and where do we live every day? What will we be eating? These doubts all went away with the adrenaline and curiosity of seeing the Martian-esque landscape and especially after putting on the EVA helmet for the first time. It truly felt like I was living on Mars and I very quickly immersed myself in the mission.” 

A unique leadership opportunity

Hoying has participated in other analog missions through MIT’s RASC-AL challenge submissions, specifically 2023’s Pale Red Dot. “I have led an analog mission in the past with [MIT AeroAstro colleague] George Lordos. We led a total crew of 11 in a dual-site mission architecture, where George led one habitat and I led the other. Pale Red Dot and Project MADMEN emphasized different features of a Martian mission, so certain aspects of this, like the extravehicular activity procedures and reporting requirements for mission support, were different.”

As commander, Hoying managed logistics, including balancing the scientific objectives of the multiple projects the crew set out to complete. “The two field experiments were soil collection for Project MADMEN and field operation of REMI, the ground-penetrating radar robot. Sometimes this led to competing requirements for EVAs, as REMI’s mass would reduce the distance that our rovers could cover before running out of battery and therefore limit the terrain types that could be reached for soil collection.” 

Hoying’s main focus was balancing the crew’s requirements for data with safety, including such considerations as who had recently been on EVA, who needed a break from carrying the heavy EVA suits, how far the team could safely travel, and how the weather impacted different areas. “The decisions for what the science goals of an EVA were, who would go on each EVA, and where they would be to collect from came down to me. Ultimately, we were able to balance all of these and satisfy the collection requirements of both field projects, even with last-minute changes due to things like weather.”

The crew makes the mission

Project MADMEN involved conducting onsite field tests of geological samples and robotic experiments for landing site selection. But the success of the mission hinged on more than just in-lab results. Hosting the mission at MDRS allowed the MADMEN crew to gain valuable insights on how individuals and teams might actually experience life on Mars, psychologically and socially. 

“We had a great crew, and as a result we had a great mission,” says Hoying. She managed the psychosocial aspect of the mission using daily questionnaires, studying the effects of contingency and emergency scenarios on metrics like quality of life.

The main living quarters for the crew is a two-story, 8-meter diameter cylinder called the “Hab.” The lower deck comprises the EVA prep room, an airlock, bathroom facilities, and a tunnel to the other structures. The upper deck houses the living quarters, including a kitchen and bunks. The close quarters only served to solidify the crew’s enthusiasm for the mission and support of each other.

“We shared almost every meal together and used the time to bond and talk about our interests. We often ended the day with social activities, whether it be talking about our backgrounds or future plans, playing games, or stargazing,” says Chan. “The most challenging part for me personally was stepping out of my comfort zone. Prior to this mission, I have not lived communally or camped before. It took me a bit to get used to living in close quarters with other people and balancing chores and tasks. I soon got used to the routine and enjoyed trying things for the first time, which made my experience a lot more rewarding, too.”

By day (or “Sol”) 3, the crew had assigned nicknames to each other in a call-sign ceremony. “It’s a tradition in other field experiences I’ve been a part of, and I wanted to carry that through for this crew. Assigning these was a night full of storytelling, laughing, and new memories, and we all agreed that the reasoning behind each nickname assignment would remain between the crew,” says Hoying (“Melon”); Chan’s call sign was “PODO.” 

Crew 290’s Martian journals close with a reflection from Chan on their out-of-this-world experience: “As we get to work tonight, we reminisce about our time here on Mars, from the first time setting foot in the station to the first time suiting up for EVAs. We’re all so grateful to be here and have learned a lot about what it takes to be a Martian during the past two weeks.” Read all of Chan’s journal updates here.

The mission was primarily sponsored by Duquesne University and the Pennsylvania Space Grant Consortium, with some travel support provided by the Massachusetts Space Grant Consortium.