Speaker 1:	Thanks to all of you for coming out and braving the weather. I know it's kind of gross out there, so glad to see all here. So I basically have three points I want to make tonight, and the first one you probably already know. In fact, you may know all of these, but that there is some really amazing work going on that the students are doing at Swarthmore. And the second is that our students are engaged in a lot of what I like to call mode multi-modal learning, where you have some really applied hands-on work and students learn to solve sort of a similar set of problems using a really broad variety of tools and techniques and approaches. The last thing I want to convey is how much I've learned in my first three years teaching here and how much impact teaching here has had on my research.
	So maybe you already knew that's Swarthmore is a very exciting and immersive place and this is nothing new, in which case, the good news for you is that there is going to be lots of movies of robots in the following slides and you can just sit back and watch the robot movies.
	So I thought I'd start out with a question that I, is basically the first thing that gets asked when I have a parent of a prospective student who comes and sits down in my office and they say, why on Earth would I send my kids to a liberal arts school to learn engineering? To me, I would say the answer is that engineering really is inherently multi-disciplinary field and it actually goes really well hand-in-hand with the liberal arts approach to education. 
	So this is a picture that we drew when we were going through a recent round of departmental planning. I like to call it the Engineering Donut. What we wanted to convey is that not only do you have a lot of prominent sub-disciplines of engineering, but that they're all sort of richly connected to a lot of other disciplines out in the rest of the world. By the way, we didn't draw the lines coming out of math because that would've been everything, so we just sort of shoved it in the middle of the donut there.
	But the statistics look like this. I think about 40% of our majors in Swarthmore Engineering double major in some other field, and that's not just like physics, or math, but religion or philosophy or any number of other very liberal artsy pursuits. An additional 30% of our majors are minoring in some other field. So I think that for our students, a big part of engineering in liberal arts setting is about finding and exploiting these really deep [inaudible 00:02:32].
	I also thought it might be good to lead in a bit with my own trajectory in getting to Swarthmore. So as Ken said, my own particular interest is in the area of robot motion planning, essentially how do I get my robots to pick up that thing without bumping into stuff? Or how do I get my robot to go from Point A to Point B without running into anyone? I'm particularly interested in complex systems that have to reason about momentum and uncertainty, and things like that.
	So what I'm about to show you is footage from my Ph.D at the Robotics Institute at Carnegie Mellon University. Oh good, the sound is working. So this is the Little Dog quadruped robot. It's designed and manufactured by a company called Boston Dynamics. If you've ever seen a YouTube video of Big Dog, this is Big Dog's little brother. So this was part of the DARPA Learning Locomotion Program, which was a multi-institution competition where you have a bunch of different schools all competing to sort of walk the fastest over various types of rough terrain. So your tax-payer dollars funded the federal government to build standardized fake rocks, which were shipped to us, roughly a month late. 
	So you get your monthly batch of fake rocks and then you figure out how do you program the robot. There's nobody joy-sticking it. Basically the robot looks at the terrain, makes a map of the terrain, and says here's where I'm going to step, here's how I'm going to wiggle my feet to get around to do this. So that's basically, I spent three years in a windowless room in the basement of Carnegie Mellon making this robot walk over the fake rocks, which is totally surreal, but I actually loved it. It was great. So what you're seeing is the result of several years of that work.
	By the way, these little balls over here are motion-capture markers. So if you guys saw Avatar, you know about the motion-capture performance. It's the same system, so the computer is actually using these markers to localize both the robot and the terrain, so he can treat sensing as a solved problem and just worry about the difficult parts of planning and control.
	So before I was at Carnegie Mellon, I, as Ken said, got my Bachelor of Arts at Vassar in Cognitive Science. While I was there, I ended up interning, and eventually working full-time for a few years at this Boston-based startup called Bluefin Robotics [phonetic 00:04:50], and we made robot submarines and they looked like this. So for reference, that vehicle is about 13 feet long, about this big around, and it's job is to tow around some side scan sonar array, which is essentially just going to take fancy pictures of the sea floor. So this background image is a sonar picture of the bottom of Mass Bay, and that was actually just a pretty large shipwreck that we found one day. I forget exactly how big it was, but we were kind of surprised to see ... We weren't exactly sure if anybody else had found it.
	This is what I looked like back in those days. So I actually got to go out on the boat and get my hands dirty, and you write some software and there comes time to test the software, and you get to go out on the boat and do that. So that was great, and it was actually at Bluefin, at this company where I first became aware of Swarthmore's engineering program. So my first boss there was a guy from the class of 1998 engineering, by name of Erin Marsh, and the company actually went on to hire four more Swarthmore alums in the next few years. So I was there from 2000 to 2005, and there were five Swatties there by the time that I left.
	I've kind of noticed this as a pattern, where sort of one Swarthmore person gets their foot in the door, and then suddenly there's this entire colony of them. So I noticed there must be some cool stuff going on at Swarthmore. So at some point, two of my Swarthmore buddies and I, we decided it was time to go on to greener pastures and go to grad school, and so we all started to apply for grad school. It never occurred to us, not for a second did we think, we all have BA's from liberal art schools, maybe that's not good enough to go to the top engineering robotics program. This was just not in our heads at all, and maybe one reason for that was that their professor was a guy named Bruce Maxwell, who taught at Swarthmore for many years, and is a Swarthmore alum, and went to the Robotics Institute, and then came back and taught here.
	So I guess we sort of had our little lighthouse beacon to follow. Yeah, we all ended up going to Carnegie Mellon and getting our Ph.D's, and I never met Bruce, but towards the end of my time there, I got in touch with him and started asking him about job advice because I knew I wanted to go back and teach. Then a few weeks later, I said, oh Bruce, looks like your job is being posted right now. He said, go for it. So, all of which is to say, between my own liberal arts background and meeting and working with so many Swarthmore alums along the way, I think it's given me a unique perspective on doing a technical education from a liberal arts setting. It's been really interesting to see how that can play out in many different ways.
	With that said, let me move to talking a little bit about teaching here at Swarthmore. So far at Swarthmore, I taught one core course, which is this Fundamentals of Digital Systems course, and a number of upper-level electives in engineering. So Numerical Methods, Computer Vision, Mobile Robotics, just to name a few. In those courses, I've seen a really broad range of enrollments, from near 30 in a core course, down to maybe 10 in some of the smaller electives. But what they all share in common is a very similar structure where you have weekly homeworks usually, three to five larger projects or labs, and then this very open-ended final project at the end of the semester.
	So this is actually a clip from a mid-semester project in computer vision. The students were tasked to do, I guess it's an augmented reality game, you might call it that. So what you're seeing is a setup with a white board, a projector, and a camera, and the student can play tic-tac-toe with the computer using that setup. So here's what it looks like when the student actually goes and makes a move in tic-tac-toe. It recognizes that she's there and she's blocking the scene, and then when she gets out of the way, it finds the X and highlights it. So fun stuff like that.
	So for this particular assignment, the suggested game to implement was tic-tac-toe, just because it's kind of a easy game to implement and we're in computer vision, and we're not really there to make really complicated games. But of course, the students ran with it in all kinds of different directions, and some of them made cool tic-tac-toe AI's, and some of them added sound effects, and some of them decided to go with different games entirely. We had one group implement Battleship, which was really cool.
	So one of the things that I found at Swarthmore is that the more open-ended that you make the assignments, the more work you're going to get from the students. It's almost kind of like we're tricking them. I'm like, do whatever you want, and it's like okay, well then they go do everything. So that's cool.
	I'm about to show you another project from the Computer Vision course. So we covered texture classification in class. So essentially, if you point a camera at, a black and white camera so you don't get to cheat and use color, can you train a computer to tell the difference between grass and sand and stone and wheat, and kinds of ... We had a big database. Believe it or not, there are people out in academia who just maintain giant databases of some random textures for exactly this purpose. So we read a paper about this in class, and they had some reference implementation, and I told the students to go out and find a novel application for this texture classification technique. So for the movie that you're about to see, the stated goal of the students was to determine, scientifically, whether a watched pot never boils. So what you're seeing here is, they point the camera at the pot, and then down here is the output of the texture classification routine. So you can see we started simmer, there were more simmer, and then starting to see the boils creeping in there.
	So I think I took two things away from this. First is, that Swarthmore students will not miss an opportunity to make a good joke in completing an assignment. And the second was, in making this joke, they kind of completely shattered the requirements of the assignment and went way above and beyond. I think I asked them, take a small series of still images and see if you can do something neat. And of course, they're doing stuff with live video and they're getting a laboratory setup. It's actually kind of a really cool application I'd never considered because this rapidly turns into process monitoring stuff, and I never thought about using texture classification in process monitoring. Anyways, it turns out the pot does boil, so that's good to know. That was a fun project.
	This is another final project from Computer Vision. So again, in just about all of the courses that I teach, the final project is totally open-ended in that just about anything the students can think of that's remotely related to the class that we had over the semester, is fair game. And by the way, I did not pick the music - the students did. This is the video that they submitted, so I'm just showing it to you. So what you're seeing, is there's a computer projecting a ball on the board, and the students can actually interact with the ball by just stepping up to the board and waving their hands around. So this isn't pre-scripted, it's all sort of happening in real-time. I'm going to turn that down a little bit, sorry.
	So one of the things that I thought was really cool about this project ... I mean again, obviously there's vision going on, but the students really could draw on and ultimately expand on some of the stuff that they had learned in their physics classes and mechanics. I think to me, that's one of the great things about engineering, is that what I teach here, I sort of feel like the entirety of math and science is our playground to play in, and that's great. We've had a lot of other good final projects in Vision. We had some students doing a head-mounted augmented reality display where essentially, you wear this contraption and you walk around a computer screen, it changes the view of the computer screen. So like rotating object in 3D as you walk around, and all kinds of other fun games and stuff like that.
	A lot of what I do when I'm talking with students ... Sort of about three weeks before the end of the semester, we start discussing final project topics, and a lot of what I'm doing is negotiating down the scope because students are thinking really, really big, and then you have this fun negotiation to sort of how do you actually carve off the right sized chunk of work to do this? So I write a lot of equations on the board. I'm a very math-y person, as you can tell by math being in the middle of the donut. So a lot of what the students are doing, is they're just copying down math equations and then later they want to study, and they want to typeset it, so they want to use LaTeX to typeset it. So one of the ideas for a final project for Computer Vision was, I know let's just point a camera at the board at the end of a Zucker class, and then we'll get automatically a big LaTeX file that will have every equation perfectly typeset.
	I'm like, I'm pretty sure I've read a paper from people at Microsoft trying to do exactly that, and basically claiming that it was near impossible. So let's try and go for two plus two equals four. We got there, and that was great, but there is this sort of like, how do we get to the place that you want to be in a way that's going to make you feel like you learned something, and make me feel like you're doing something that's appropriately scoped? It's always really fun and you never quite know what you're going to get. Then you end up calling your graduate student friends to sort of see, okay is this possible for anybody, let alone by an undergraduate in three weeks at the end of a very long semester. But it's fun to do that research, it really is.
	So in my Mobile Robotics course, the first few labs sort of build up to a competition that's held about two-thirds of the way through the semester, where we do ... It's basically an autonomous robot slalom. This year, we got Larry Kesterson, who is our campus videographer, to come and shoot the event. So I'm just going to let the video do the talking for me for a minute there.
Video:	The students had been working really hard for weeks and weeks. They're spending hours in the lab and I think it's good to have a culminating event where everybody can sort of get together and get a chance to see what everybody else has been up to, and get to cheer each other on a little bit.
	This is a class where a lot of students sort of get a deeper intuition and understanding for linear algebra than they maybe got the first time that they took linear algebra. The way that it works out when you learn math in a math classroom, sometimes you don't see it applied, and having those applications can really hammer home a lot of the things that they got from the book the first time.
	So I'm an athlete here too, and I've had my fair share of loss, but this is one of the tougher ones. So we came in second, and we had high hopes for the last portal.
	Very competitive. I play softball here and I'm very emotionally invested. Definitely it's really competitive. There was a lot of pressure there at the end, very nerve racking. I did not expect to be so nervous about it, which made winning feel great.
Speaker 1:	So that video I think is still up somewhere on the Swarthmore website if you ever want to go check it out again. The lab that that takes place in is actually a newly renovated robotics lab at Hicks, and if you have a chance, I would urge you to go check out the renovations we just did last summer at Hicks. They all turned out really well and I love that lab.
	So just to follow-up a bit on that comment about the math. This is actually another great thing about teaching in the applied sciences, which is sort of I get to be there for that moment when something crystallizes, and there's this abstract concept like, what does it mean for a bunch of vectors to span a linear space? And it was totally opaque when they learned it in the math classroom, and then all of a sudden, they sort of get it, and there's geometric and physical intuition about what that means. I think to me, that's really one of the best parts of my job. It's really great to be able to do that all of the time.
	So this was a project from the Numerical Methods course and I asked the students to simulate a roller coaster using Lagrangian mechanics, so they had to define a mathematical function that makes the shape of a roller coaster and then figure out how to do the physics updates to get the thing to be physically plausible. One of the things that teaching that class really hammered home for me was that learning programming and math at the same time can somehow be this weirdly, mutually reinforcing process. So for some students, programming is really difficult. Maybe to them, equations make a lot of sense, but they don't get the how to tell the computer what to do. For some students, it's the total opposite. We do a lot of group work here, and they're working together and they get to play to their strengths, and they get to teach each other all the time, which is great.
	I think another aspect of that is that both math and programming are really formal disciplines, and it's really easy to get caught up and carried away by the formula-zone of those things if you're just pursuing it for its own sake. When you're doing it in applied context, again, something just sort of clicks, and then the students just want to make the next cool demo, and the next cool video. That's been really neat to find out.
	So this last semester, I taught a seminar course in Humanoid Robotics, which basically focused on, okay if you have a legged robot, how do you get it to walk? It was a really small course, I had just seven students in there, and teaching in that small intimate setting was really great because I got to spend a lot of time with the students. It gave me a lot of flexibility in assignments and grading. So what you're seeing here is actually a screen shot from a blog, so I had the students submit all of their weekly assignments via web blog, and basically they all got Blogger accounts or Blogspot or whatever we're calling it these days. They write some description of what they did, and they include some relevant graphs, and then there's some maybe relevant YouTube movie.
	This is a video from that course which I pulled directly off of YouTube. So the objective here is, we had a simulation of a walking robot and the idea is you want to avoid all these blue ... We called them cracks in the sidewalk, but they're just sort of places where you're not allowed to put your foot. So they had to figure some irregular pattern of footsteps to get the robot to do that, and get the robot to actually balance while it's walking.
	One of the great things about the topics that I teach is that the students really get vivid and immediate feedback about their work because at the end of the day, either the robot walk or it doesn't. It's really satisfying when it works. Sometimes things go wrong and then it's not exactly easy to know why they've gone wrong. You know that something is not working, and that can actually really be frustrating for the students, and that's where spending lots of time at office hours and lab hours and working through things with the students can help a lot. Basically, the only way I know how to teach people to program and think analytically about these things is by sitting next to them and talking it out. One of the great things about Swarthmore is that's kind of par-for-the-course around here.
	But yeah, like I said, when things work, they work and students get really excited about that and I think that immediacy really drives them to push themselves really hard. I've been amazed with what I can get out of Swarthmore students. A lot of the stuff that I've shown you is straight out of my graduate school curriculum, and I can't imagine that there's a lot of undergrads that are working at this level. As I said before, there's also a lot of good synergies between my teaching and the research as well.
	So there's three main ways, engineering students at least, can get involved in research at the college. Summer research assistantships, where we're working on some project and we enlist the students to help us out. There's also directed readings, or independent study, essentially. And then in engineering, they have the Engineering E90, which is a senior design project. If you're lucky, you can get students to work on things that push your research along too, and that can really be a good win-win situation for everybody.
	So this is a shot of myself and my student Ko Yung He [phonetic 00:21:58], and he's just graduated in May and I was lucky enough to work with him in all three of those capacities. So he did summer research with me twice. I had taught a directed reading with him, and then he just finished along with his fellow senior, Elizabeth Martin, an engineering 90 project, that is probably going to result in ... I think we're going to submit it to a top-tier robotics conference next week. I'm really excited about that. So these E90's can actually be really productive.
	This is footage of ... Has anybody ever played the Marble Maze game? It's like the table-top thing and there's two knobs, and you turn the knobs and I'm totally horrible at it. So of course, I decided I wanted to build a robot to do it. Now I actually started doing Marble Maze research with robots at Carnegie Mellon as part of a reinforcement learning thing. I wanted the robot to learn through trial-and-error, the best way to steer the ball through the maze, and believe it or not, this was not the first or even the second or even the third Marble Maze-playing robot. There is this weird tradition of doing it in machine-learning literature. 
	So I had left my equipment at Carnegie Mellon and I wanted to continue doing this type of research at Swarthmore, and this apparatus was actually built by Noah Marks over there, class of 2011, and Lucas James, and it was part of their E90 project. The system ended up working really well and they made it into a paper, and just to skip to the end here if I can actually get my mouse over there, there it is. Just so you believe me, it actually works. We do get all the way to the end of the maze. So I guess the moral of the story is, if you're really bad at something, just build a robot to do it for you.
	This is a movie of some of the work that Ko Yung did with ... I have two of these robots, they're called Darwin OP's. It's about this tall, they're really, really cute. This was last summer and basically we were spending the summer working out a lot of the interesting math behind the kinematics of this type of robot. So you get to do cool things like figure out how to make the robot balance on one foot. All of this actually came in handy ... Oop, there it goes. All of this came in handy because as Ken said, I joined a team recently for the DARPA robotics challenge, which is another multi-institution competition. 
	This one is a simulated disaster relief scenario, so imagine there's some Fukushima-type situation and something is about to go horribly wrong. You want to send in a robot to save the day. So they have this interesting obstacle course setup where there's a discreet number of tasks or challenges. So walking over rough terrain, climbing a ladder, driving a utility vehicle. They actually have one component that's breaking through a wall, which is a little scary. So my particular task on this, I am on a rather large team, and so I got a small chunk of this big team project, and my chunk is door opening.
	My own research here is trajectory optimization. I really want to make robots that have smooth human-like qualities of motion. So for instance, this trajectory here is pretty sub-optimal. It kind of just looks robotic, right? It looks like I'm a robot opening a door. By the way, this is the HUBO Plus robot. Our team I think has six of these spread out across the U.S. This one is at Drexel. Our team is actually a Drexel-lead team, but we're also working with institutions like Georgia Tech, WPI, Purdue, Ohio State, I think four others. So it's actually a really big team and we're competing with other teams from Carnegie Mellon and MIT, all over the world.
	This spring, after working with Ko Yung and Beth on their E90, we got some really good results on the trajectory optimization. We got it working a lot smoother. Essentially what happened on this project was I had some good trajectory optimization code that took about 10 minutes to generate two seconds worth of door-opening behavior, which is frankly unacceptable. I basically handed them some key Wikipedia pages, and I said I really want this to be two orders of magnitude faster. And then at the end, it kind of was, and it was really amazing.
	Also, over spring break, I went down to Georgia Tech, so this is footage from the lab at Georgia Tech. We implemented dynamically stable walking for this robot. It could totally walk before we were programming it, but we did a slightly different type of walking that's a little bit more flexible than the code that we were given. According to one of the senior colleagues on the team, he said there's basically about a dozen people in the world who are doing stuff like this. We went down to Georgia Tech and working with the other guys down there, we sort of got this done in about a week, which was pretty cool. But there's absolutely no way I would've been able to do this had I not taught that Humanoid Robotics class. 
	When you're teaching something and drilling down into every last detail because the students are asking you really precise and interesting questions about all these different parts of it, it really drives things home in a really fundamental way. Also with the trajectory optimization stuff, all of that work is really, really well-informed by this Numerical Methods course that I decided to teach more or less at the last minute, and then I learned a ton. Again, a lot of it is just driven by student questions, like what if this happened, what if I did this? You really have to get [inaudible 00:27:49] material around here. So that's really been a big, pleasant surprise to me, was how much the teaching benefits the research here.
	So I think that brings me pretty much to the end of my prepared remarks. Just to get back to where we started, I am totally thrilled about engineering and liberal arts together, and it's been really awesome to see how the students are learning, and what they're learning, and how all that is helping drive my research along. So thanks so much for your attention and I'll be happy to take any questions you have.