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Cathy Wu is the Gilbert W. Winslow Assistant Professor of Civil and Environmental Engineering and a member of the MIT Institute for Information, Techniques, and Society.
By Kim Martineau | MIT Schwarzman Faculty of Computing
Cathy Wu is the Gilbert W. Winslow Assistant Professor of Civil and Environmental Engineering and a member of the MIT Institute for Information, Techniques, and Society. As an undergraduate, Wu gained MIT’s hardest robotics competitors, and as a graduate scholar took the College of California at Berkeley’s first-ever course on deep reinforcement studying. Now again at MIT, she’s working to enhance the circulate of robots in Amazon warehouses underneath the Science Hub, a brand new collaboration between the tech big and the MIT Schwarzman Faculty of Computing. Exterior of the lab and classroom, Wu could be discovered operating, drawing, pouring lattes at residence, and watching YouTube movies on math and infrastructure through 3Blue1Brown and Sensible Engineering. She not too long ago took a break from all of that to speak about her work.
Q: What put you on the trail to robotics and self-driving automobiles?
A: My mother and father all the time wished a physician within the household. Nonetheless, I’m dangerous at following directions and have become the flawed sort of physician! Impressed by my physics and laptop science courses in highschool, I made a decision to review engineering. I wished to assist as many individuals as a medical physician might.
At MIT, I seemed for purposes in vitality, schooling, and agriculture, however the self-driving automotive was the primary to seize me. It has but to let go! Ninety-four % of great automotive crashes are brought on by human error and will doubtlessly be prevented by self-driving automobiles. Autonomous automobiles might additionally ease site visitors congestion, save vitality, and enhance mobility.
I first realized about self-driving automobiles from Seth Teller throughout his visitor lecture for the course Cellular Autonomous Techniques Lab (MASLAB), through which MIT undergraduates compete to construct one of the best full-functioning robotic from scratch. Our ball-fetching bot, Putzputz, gained first place. From there, I took extra courses in machine studying, laptop imaginative and prescient, and transportation, and joined Teller’s lab. I additionally competed in a number of mobility-related hackathons, together with one sponsored by Hubway, now generally known as Blue Bike.
Q: You’ve explored methods to assist people and autonomous automobiles work together extra easily. What makes this drawback so exhausting?
A: Each techniques are extremely advanced, and our classical modeling instruments are woefully inadequate. Integrating autonomous automobiles into our present mobility techniques is a large enterprise. For instance, we don’t know whether or not autonomous automobiles will reduce vitality use by 40 %, or double it. We’d like extra highly effective instruments to chop by the uncertainty. My PhD thesis at Berkeley tried to do that. I developed scalable optimization strategies within the areas of robotic management, state estimation, and system design. These strategies might assist decision-makers anticipate future situations and design higher techniques to accommodate each people and robots.
Q: How is deep reinforcement studying, combining deep and reinforcement studying algorithms, altering robotics?
A: I took John Schulman and Pieter Abbeel’s reinforcement studying class at Berkeley in 2015 shortly after Deepmind printed their breakthrough paper in Nature. That they had skilled an agent through deep studying and reinforcement studying to play “House Invaders” and a collection of Atari video games at superhuman ranges. That created fairly some buzz. A yr later, I began to include reinforcement studying into issues involving combined site visitors techniques, through which just some automobiles are automated. I spotted that classical management methods couldn’t deal with the advanced nonlinear management issues I used to be formulating.
Deep RL is now mainstream nevertheless it’s not at all pervasive in robotics, which nonetheless depends closely on classical model-based management and planning strategies. Deep studying continues to be essential for processing uncooked sensor knowledge like digicam pictures and radio waves, and reinforcement studying is steadily being integrated. I see site visitors techniques as gigantic multi-robot techniques. I’m excited for an upcoming collaboration with Utah’s Division of Transportation to use reinforcement studying to coordinate automobiles with site visitors alerts, decreasing congestion and thus carbon emissions.
Q: You’ve talked concerning the MIT course, 6.007 (Indicators and Techniques), and its impression on you. What about it spoke to you?
A: The mindset. That issues that look messy could be analyzed with frequent, and typically easy, instruments. Indicators are remodeled by techniques in varied methods, however what do these summary phrases imply, anyway? A mechanical system can take a sign like gears turning at some velocity and remodel it right into a lever turning at one other velocity. A digital system can take binary digits and switch them into different binary digits or a string of letters or a picture. Monetary techniques can take information and remodel it through hundreds of thousands of buying and selling selections into inventory costs. Individuals absorb alerts daily by ads, job provides, gossip, and so forth, and translate them into actions that in flip affect society and different folks. This humble class on alerts and techniques linked mechanical, digital, and societal techniques and confirmed me how foundational instruments can reduce by the noise.
Q: In your challenge with Amazon you’re coaching warehouse robots to select up, type, and ship items. What are the technical challenges?
A: This challenge entails assigning robots to a given process and routing them there. [Professor] Cynthia Barnhart’s group is targeted on process task, and mine, on path planning. Each issues are thought of combinatorial optimization issues as a result of the answer entails a mixture of decisions. Because the variety of duties and robots will increase, the variety of attainable options grows exponentially. It’s known as the curse of dimensionality. Each issues are what we name NP Arduous; there might not be an environment friendly algorithm to resolve them. Our purpose is to plot a shortcut.
Routing a single robotic for a single process isn’t troublesome. It’s like utilizing Google Maps to search out the shortest path residence. It may be solved effectively with a number of algorithms, together with Dijkstra’s. However warehouses resemble small cities with a whole bunch of robots. When site visitors jams happen, clients can’t get their packages as rapidly. Our purpose is to develop algorithms that discover essentially the most environment friendly paths for all the robots.
Q: Are there different purposes?
A: Sure. The algorithms we check in Amazon warehouses may sooner or later assist to ease congestion in actual cities. Different potential purposes embody controlling planes on runways, swarms of drones within the air, and even characters in video video games. These algorithms is also used for different robotic planning duties like scheduling and routing.
Q: AI is evolving quickly. The place do you hope to see the massive breakthroughs coming?
A: I’d prefer to see deep studying and deep RL used to resolve societal issues involving mobility, infrastructure, social media, well being care, and schooling. Deep RL now has a toehold in robotics and industrial purposes like chip design, however we nonetheless have to be cautious in making use of it to techniques with people within the loop. Finally, we wish to design techniques for folks. Presently, we merely don’t have the proper instruments.
Q: What worries you most about AI taking over an increasing number of specialised duties?
A: AI has the potential for great good, nevertheless it might additionally assist to speed up the widening hole between the haves and the have-nots. Our political and regulatory techniques might assist to combine AI into society and decrease job losses and revenue inequality, however I fear that they’re not geared up but to deal with the firehose of AI.
Q: What’s the final nice e-book you learn?
A: “The way to Keep away from a Local weather Catastrophe,” by Invoice Gates. I completely liked the best way that Gates was in a position to take an overwhelmingly advanced matter and distill it down into phrases that everybody can perceive. His optimism evokes me to maintain pushing on purposes of AI and robotics to assist keep away from a local weather catastrophe.
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