Jacqueline Kory-Westlund

Social robots as language learning companions for children

Language learning is, by nature, a social, interactive, interpersonal, activity. Children learn language not only by listening, but through active communication with a social actor. Social interaction is critical for language learning.

Thus, if we want to build technology to support young language learners, one intriguing direction is to use robots. Robots can be designed to use the same kinds of social, interactive behaviors that humans use—their physical presence and embodiment give them a leg up in social, interpersonal tasks compared to virtual agents or simple apps and games. They combine the adaptability, customizability, and scalability of technology with the embodied, situated world in which we operate.

The robot we used in these projects is called the DragonBot. Designed and built in the Personal Robots Group, it's a squash-and-stretch robot specifically designed to be an expressive, friendly creature. An Android phone displays an animated face and runs control software. The phone's sensors can be used to capture audio and video, which we can stream to another computer so a teleoperator can figure out what the robot should do next, or, in other projects, as input for various behavior modules, such as speech entrainment or affect recognition. We can stream live human speech, with the pitch shifted up to sound more child-like, to play on the robot, or playback recorded audio files.

Here is a video showing the original DragonBot robot, with a brief rundown of its cool features.

Social robots as informants

This was one of the very first projects I worked on at MIT! Funded by an NSF cyberlearning grant, the goal of this study and the studies following were to explore several questions regarding preschool children's word learning from social robots, namely:

• What can make a robot an effective language learning companion?
• What design features of the robots positively impact children's learning and attitudes?

In this study, we wanted to explore how different nonverbal social behaviors impacted children's perceptions of the robot as an informant and social companion.

We set up two robots. One was contingently responsive to the child—e.g., it would look at the child when the child spoke, it might nod and smile at the right times. The other robot was not contingent—it might be looking somewhere over there while the child was speaking, and while it was just as expressive, the timing of its nodding and smiling had nothing to do with what the child was doing.

For this study, the robots were both teleoperated by humans. I was one of the teleoperators—it was like controlling a robotic muppet!

Each child who participated in the study got to talk with both robots at the same time. The robots presented some facts about unusual animals (i.e., opportunities for the child to learn). We did some assessments and activities designed to give us insight into how the child thought about the robots and how willing they might be to learn new information from each robot—i.e., did the contingency of the robot's nonverbal behavior affect whether kids would treat the robots as equally reliable informants?

We found that children treated both robots as interlocutors and as informants from whom they could seek information. However, children were especially attentive and receptive to whichever robot displayed the greater nonverbal contingency. This selective information seeking is consistent with other recent research showing that children are, first, quite sensitive to their interlocutor's nonverbal signals, and use those signals as cues when determining which informants they question or endorse.

In sum: This study provided evidence that children show sensitivity to a robot's nonverbal social cues, like they are with humans, and they will use this information when deciding if a robot is a credible informant, as they do with humans.

Links

• Video showing this project
• This work is discussed on the Personal Robots Group site and the MIT Media Lab site
• I wrote about this project for the MIT Media Lab blog: Robots for Kids: Designing Social Machines That Support Children's Learning.
• That blog post was republished on IEEE Spectrum: Robots for Kids: Designing Social Machines That Support Children's Learning.

Publications

• Breazeal, C., Harris, P., DeSteno, D., Kory, J., Dickens, L., & Jeong, S. (2016). Young children treat robots as informants. Topics in Cognitive Science, pp. 1-11. [PDF]

• Kory, J., Jeong, S., & Breazeal, C. L. (2013). Robotic learning companions for early language development. In J. Epps, F. Chen, S. Oviatt, & K. Mase (Eds.), Proceedings of the 15th ACM on International conference on multimodal interaction, (pp. 71-72). ACM: New York, NY. [on ACM]

Word learning with social robots

We did two studies specifically looking at children's rapid learning of new words. Would kids learn words with a robot as well as they do from a human? Would they attend to the robot's nonverbal social cues, like they do with humans?

Study 1: Simple word learning

This study was pretty straightforward: Children looked at pictures of unfamiliar animals with a woman, with a tablet, and with a social robot. The interlocutor provided the names of the new animals—new words for the kids to learn. In this simple word-learning task, children learned new words equally well from all three interlocutors. We also found that children appraised the robot as an active, social partner.

In sum: This study provided evidence that children will learn from social robots, and will think of them as social partners. Great!

With that baseline in place, we compared preschoolers' learning of new words from a human and from a social robot in a somewhat more complex learning task...

Study 2: Slightly less simple word learning

When learning from human partners, children pay attention to nonverbal signals, such as gaze and bodily orientation, to figure out what a person is looking at and why. They may follow gaze to determine what object or event triggered another's emotion, or to learn about the goal of another's ongoing action. They also follow gaze in language learning, using the speaker's gaze to figure out what new objects are being referred to or named. Would kids do that with robots, too? Children viewed two images of unfamiliar animals at once, and their interlocutor (human or robot) named one of the animals. Children needed to monitor the interlocutor's non-verbal cues (gaze and bodily orientation) to determine which picture was being referred to.

We added one more condition. How "big" of actions might the interlocutor need to do for the child to figure out what picture was being referred to? Half the children saw the images close together, so the interlocutor's cues were similar regardless of which animal was being attended to and named. The other half saw the images farther apart, which meant the interlocutor's cues were "bigger" and more distinct.

As you might expect, when the images were presented close together, children subsequently identified the correct animals at chance level with both interlocutors. So ... the nonverbal cues weren't distinct enough.

When the images were presented further apart, children identified the correct animals at better than chance level from both interlocutors. Now it was easier to see where the interlocutor was looking!

Children learned equally well from the robot and the human. Thus, this study provided evidence that children will attend to a social robot's nonverbal cues during word learning as a cue to linguistic reference, as they do with people.

Links

• Video showing this project
• This work is discussed on the Personal Robots Group site and the MIT Media Lab site
• I wrote about this project for the MIT Media Lab blog: Making new (robot) friends: Understanding children's relationships with social robots.
• That blog post was republished on IEEE Spectrum: Robots for Kids: Designing Social Machines That Support Children's Learning.

Publications

• Kory-Westlund, J., Dickens, L., Jeong, S., Harris, P., DeSteno, D., & Breazeal, C. (2015). A Comparison of children learning from robots, tablets, and people. In Proceedings of New Friends: The 1st International Conference on Social Robots in Therapy and Education. [talk] [PDF]

• Kory-Westlund., J. M., Dickens, L., Jeong, S., Harris, P. L., DeSteno, D., & Breazeal, C. L. (2017). Children use non-verbal cues to learn new words from robots as well as people. International Journal of Child-Computer Interaction. [PDF]

Click here to see the video showing this project!

Study Overview

For my master's thesis at the MIT Media Lab, I created a social robotic learning companion that played a storytelling game with young kids.

Children’s oral language skills in preschool can predict their academic success later in life. Helping children improve their language and vocabulary skills early on could help them succeed later. Furthermore, language learning is a highly social, interactive activity. When creating technology to support children's language learning, technology that leverages the same social cues and social presence that people do—such as a social robot—will likely provide more benefit than using technology that ignores the critical social aspects of language learning.

As such, in this project, I examined the potential of a social robotic learning companion to support children's early long-term language development.

Study

The robot was designed as a social character, engaging children as a peer, not as a teacher, within a relational, dialogic context. The robot targeted the social, interactive nature of language learning through a storytelling game that the robot and child played together. The game was on a tablet—the tablet showed a couple characters that the robot or child could move around while telling their story, much like digital stick puppets. During the game, the robot introduced new vocabulary words and modeled good story narration skills.

Furthermore, because children may learn better when appropriately challenged, we asked whether a robot that Matched the “level” of complexity of the language it used to the general language ability of the child might help children improve more. For half the children, the robot told easier or harder stories based on an assessment of the child’s general language ability.

17 preschool children played the storytelling game with the robot eight times each over a two-month period.

I evaluated children's perceptions of the robot and the game, as well as whether the robot's matching influenced (i) whether children learned new words from the robot, (ii) the complexity and style of stories children told, and (iii) the similarity of children’s stories to the robot’s stories. I expected that children would learn more from a robot that matched, and that they would copy its stories and narration style more than they would with a robot that did not match. Children’s language use was tracked across sessions.

Results

I found that all children learned new vocabulary words, created new stories during the game, and enjoyed playing with the robot. In addition, children in the Matched condition maintained or increased the amount and diversity of the language they used during interactions with the robot more than children who played with the Unmatched robot.

Understanding how the robot influences children’s language, and how a robot could support language development will inform the design of future learning/teaching companions that engage children as peers in educational play.

Links

• This work is also on the Personal Robots Group site and the MIT Media Lab site
• New Scientist covered this work: Kindergarten bots teach language to tots
• I wrote about this project for the MIT Media Lab blog: Making new (robot) friends: Understanding children's relationships with social robots.
• That blog post was republished on IEEE Spectrum: Robots for Kids: Designing Social Machines That Support Children's Learning.

Publications

• Kory, J. (2014). Storytelling with robots: Effects of robot language level on children's language learning. Master's Thesis, Media Arts and Sciences, Massachusetts Institute of Technology, Cambridge, MA. [PDF]

• Kory, J., & Breazeal, C. (2014). Storytelling with Robots: Learning Companions for Preschool Children’s Language Development. In P. A. Vargas & R. Aylett (Eds.), Proceedings of the 23rd IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). IEEE: Washington, DC. [PDF]

• Kory-Westlund, J., & Breazeal, C. (2015). The Interplay of Robot Language Level with Children's Language Learning during Storytelling. In J. A. Adams, W. Smart, B. Mutlu, & L. Takayama (Eds.), Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction: Extended Abstracts (pp. 65-66). [on ACM]

• Kory-Westlund, J. (2015). Telling Stories with Green the DragonBot: A Showcase of Children's Interactions Over Two Months. In J. A. Adams, W. Smart, B. Mutlu, & L. Takayama (Eds.), Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction: Extended Abstracts (p. 263). [on ACM] [PDF] [Video] Winner of Best Video Award.

• Kory-Westlund, J. M., & Breazeal, C. (2019). Exploring the effects of a social robot's speech entrainment and backstory on young children's emotion, rapport, relationships, and learning. Frontiers in Robotics and AI, 6. [PDF] [online]

Me, fencing in a competition at Vassar College in 2010.

Fencing: More than parries and ripostes.

I used a fence a lot. For ten years, I picked up a foil and went on guard on the strip two, three, sometimes five days a week. Or more, with tournaments on weekends. I was not alone: my teammates did the same. Yet despite the dedication so many of us gave to the sport, the first coach I had, George Platt, used to say that when it came to life versus fencing, "It's just fencing!"

However important the sport is, in the end, "it's just fencing!"

It's as important as you make it to you. The rest of your life, well, that matters too.

I was thinking about this recently in relation to other life decisions, balancing time and energy. And I realized: I really did learn a lot, being a fencer.

Priorities, commitment, and time management

I learned how to make something a priority. How to commit to something.

I was never late to practice, and only missed a day if I was coughing and running a fever. I gave up other clubs, movie nights, Halloween parties, and much more because I had practice, or I had to sleep, we were leaving at 4am for a competition tomorrow.

But remember, it's just fencing.

My coaches in college always stressed that academics came first. If you had a huge test that day, or if you had to a class that happen to clash with practice times, well, there was no help for it; academics came first.

But being busy was no excuse to skip practice. After all, we were all college students; we all had homework and tests and classes. By joining the fencing team, I was saying, this is a priority for me. I'm going to put time and energy into this. Joining the team meant that other things that could have been priorities -- other clubs, social events -- were not so high on my personal list. Fencing was. So I made time for it.

Sometime later, I was working on a academic project. My professor told me, "It's your project; if it matters to you, it can happen, it can be good. But it's your project. If you don't care about it, if you don't make it happen, well, it won't happen. And since it's your project, no one else will care."

Fencing was like that. If you didn't care how many bouts you won, if you didn't care how well you fenced, well, guess what, no one else would really care, either. Your teammates or your coach might be disappointed. But you're the one most invested in what you're doing.

Waiting for a bout to start at the Denver North America Cup event in 2005.

Related to that: When I was fifteen or so, I was fencing in a local competition, a direct elimination bout against a woman of about the same skill level as me. We kept tying the score: 4-5, 10-9. The last round, I won. My dad said it was because I cared. It was partly endurance, too. But if you want to win, you'll put in more effort and go farther. You have to enjoy it. You have to be a good athlete. And you have to be competitive. I remember George saying once that if you don't care when you lose -- if you aren't upset that you lost -- then you didn't care about winning, either.

Failure, adaptation, and emotion regulation

When you fence, you make a lot of mistakes. You get hit, over and over, in the same way, by the same opponent, because you keep making the same mistake. It's frustrating. You lose a bout 0-5 because you kept making the same stupid mistake. Sometimes to a girl you used to beat 5-0. And the thing about fencing is that it's such an individual sport. If you lose, it's all on you. Sure, sometimes the referee makes bad calls. Sometimes the other girl just is a better fencer than you. But not always.

There are two parts to dealing with this. First, the practical side: You lost this touch. Or you lost this bout. What did you do and why didn't it work? Critically evaluate your actions. See the mistakes, or the places where someone out-fenced you. Try to improve. Adapt.

Me, fencing at a Bay Cup event in 2004.

George always taught that if what you're doing isn't working, do something else. Change something. Change anything. Sometimes, if you find yourself doing the same wrong thing over and over, it doesn't matter what else, so long as it's different: a different parry or attack, different timing or distance. Don't get stuck. Don't let your opponent score the same way twice. If what you're doing isn't working, change what you are doing.

The second part is emotional and mental. In a pool round in a tournament, you only have 5 or 6 bouts. You just lost one 0-5. You can't let that negatively affect the next bout. You have to move past it. Re-focus. You can't be flustered and upset when you step back on the strip.

I learned to consciously regulate my emotions and mental state, using combinations of music on my ipod, self-talk, and habits before and during competitions to reinforce states and moods that I empirically found to lead to me fencing better. You can't lose your cool. For me, I fenced best when balanced: Not too excited. Not too calm. Not too upset. Focused. Edged. Finding that state, keeping it, and regaining it was as critical to my performance as good hydration.

Practice and preparation

George also used to say that it was the practice you did six months ago that matters most in your competition today. And day of, I had my routines. You warm up before a competition. That isn't just to prepare your muscles - it was also part of getting ready mentally. Getting your mind in the right space. It was about eating well, and sleeping well -- not sacrificing an upcoming tournament to one evening off. If that meant missing parties, other events, whatever -- well, preparation was key. That was what commitment was. Sleeping was part of that. Eating, hydrating, training.

When taking a ballroom dance class two years ago, I realized I'd learned something else from all that practice: How to practice. You learn it slow, practice it perfectly, under control, slowly, until eventually, at top speed during a bout, you do okay. You can't practice sloppy and expect that when it matters you'll be any less sloppy. Practice perfect.

A group of fencers at George Platt's Swordplay Fencing club in 2006.

Lessons learned

My senior year at Vassar, there was controversy over whether varsity sports should count for academic credit. Suffice to say, one piece of the argument was that yes, you learn a lot doing a sport. If credits equate to learning, you learn as much -- if not more! -- in a sport as you do in other classes. You may learn different things. But you do learn.

(As a side note, the divisions between disciplines, quantifying or categorizing learning, and deciding what "counts" as an academic class don't always make sense to me.)

I learned to prioritize. To commit. To fail. To persevere. To adapt. To prepare.

I learned about the difference between achieving success and achieving excellence. I learned about confidence.

Ten years of competitive fencing. Wonderful coaches, great teammates, and a lot of things learned. Time well spent, I'd say.

What are you doing?

A feeling common among senior undergraduates (and senior high school students, and junior undergrads, etc) is the your-life's-about-to-start-what-are-you-going-to-do pressure. The common questions one faces include but are not limited to: What are you doing post-college? Are you getting a job? Where are you going to live? What about grad school? Will you stay in academia? What about high-paying tech/business/etc jobs?

Surprise: That feeling of uncertainty doesn't always go away after graduation, or even after a year. Probably not even after five, but I haven't gotten that far yet. I may be more on track than some. I've set my sights on a career in science and research, the next step of which will, for me, be grad school. But I'm sure I'm more uncertain than others.

So, from a student who's been there, here are some thoughts on...

College, Internships, and Figuring Out You Want To Do With Your Life

You already know that there are a lot of questions to answer.

For example:

If you're considering a STEM career, like me, then a lot of people will say you have two options -- academia or industry. Even before you try to tackle which of these you might like, though, you may need to figure out what specific area you want to enter -- if you're a computer scientist, would you want to develop algorithms? Would you rather work on security applications, or distributed networks, or use your CS knowledge to program laser space robots, or any of thousands of other options?

Some programs of study prepare you for specific careers; others leave you with a remarkably open-ended future.

So... how might you even start figuring out your life?

The most important thing to know

You do not have to do the same thing forever.

That's important, so I'll say it again:

You do not have to do the same thing forever.

If you pick a career direction now, you aren't stuck with it for the next forty years. People change jobs. People change careers. I had a particularly good role model in this regard: my father has owned a sailing school, consulted for small businesses, recorded punk bands, and then there was this thing in Africa... Point is, you can do whatever cool things you want. You don't have to do the same thing forever.

Granted, knowing that you can do something else later doesn't necessarily help at all with figuring out what to do now. On to the next section:

The "Figure My Life Out" Toolkit

Your two best resources are

1. yourself
2. other people

By this, I mean that you should (1) try new things as a way of figuring out what kinds of things you like doing, and you should (2) talk to other people about their experiences in doing different kinds of things. Gather information about what makes you happy, what kind of work you find worthwhile, what kind of jobs sound just plain cool, and so on.

Try new things

There are several ways to proceed.

Three of my favorites:

1. Classes.

The reason I took my first computer science class was because one day, I looked at my laptop and thought to myself, I don't know how you work at all. I signed up for CS101, vaguely hoping that I'd learn something about the Magical Innards of Computers. I didn't -- instead, I learned some Magical Incantations and Rituals for making little Java applications. I also learned that programming was fun, and that I'd probably enjoy further classes in that area. Now? The graduate program I'm entering has a heavy CS component, and most of the other programs I'd applied to were CS programs.

The point of this story: Take classes in novel areas. Either in person, at school, or via one of the increasing number of free online courses. It's one of the best ways to explore new subjects. If, after the first couple class sessions, you really hate it? Drop the class. It's worthwhile to remember that you may love a subject but dislike a professor, or love a professor enough to make any subject taught interesting. Regardless, it's a nice, easy, safe way to explore new stuff. You never know what you might find.

2. Independent learning.

My personal favorite here is reading books on all sorts of cool non-fiction topics. Pick up a book at the library on a topic you know nothing about, read it, see if it interests you. Other options include taking free online courses (see point 1), joining clubs to try out new activities, volunteering for new programs, ... lots of potential here. Spend time thinking about what activities you find worthwhile and important -- helping people or animals in need? Engineering solutions to problems in the world? Making a lot of money so you can live the life you want?

3. Internships etc.

The best time for this, if you're in school, is those warm summer months between semesters. Summer internships. Summer research programs. If you're interested in cognitive science or computer science, I have a fantastic list of resources for you. A lot of Research Experience for Undergraduates (REU) programs exist across the sciences; lots of government agencies and national labs have programs as well, not to mention a myriad of companies!

Semesters are good, too: A relative of mine took a semester off for the NASA USRP program; friends have spent semesters interning at or just plain working for software companies. You don't have to leave school, though -- while studying abroad, I nabbed an internship in a psychology research lab as a part of Sydney Uni's Study Abroad Internship Program. Many schools have field work programs or internship programs -- does yours?

Two pieces of Important Advice:

Don't do the same thing every summer

and

It's okay if you don't like your internship/job/field work/etc.

Spend a summer or two doing research on a university campus. See what it's like working in at a government facility. Try out an internship with a company. Test out different environments and see what you like. See what you don't like. Discovering that you don't like some particular kind of work is as helpful -- if not more so! -- than finding that you do like something. You'll be able to rule out jobs that make you do that.

I admit, I didn't strictly follow this advice. I spent two summers on a research project at my home college, then two summers at different NASA facilities -- again, research projects, not with a company. I dabbled in research during semesters as well.

What I did do, however, was vary the kind of research I was exposed to. Working on autonomous learning in robots at Vassar was science; the laser space robots at NASA last summer and the Autonomous Vehicle Lab the summer prior were very much engineering projects. The emotions group I work with now, among others, exposed me to psychology and cognitive science research methods.

... okay, so that's all well and good. How do you actually find a good internship opportunity?

Google is your best friend. So are people you know -- see the following section. I've been invited to apply, but I've also spent weeks or months searching online for intriguing opportunities. Search for lists of internships (e.g., in cognitive science and computer science) or lists of databases of internships, and search all these. If your university has a Career Development Office or the like, go talk to them; they have even more resources.

My advice: Start early. Deadlines for summer internship applications tend to be in January and February; sometimes, they may be as late as March or as early as October. You'll need time to find the opportunities to apply for, and you'll need time to collect the materials (such as an updated resume) for your application.

Talk to people

This point sounds relatively straightfoward. Okay, have conversations with people. But there are several ways to get the most out of those conversations...

1. Listen to advice.

You know all those other people who want to give you advice? Let them. These people may be your grandparents, your professors, other relatives, older students, current professionals ... anyone, really. Let them talk. Listen to what they all have to say. You don't have to take their advice -- not a word of it -- but now and then, they say useful things. And you won't hear those useful things unless you're listening.

2. Use your resources wisely.

You probably know a lot of people. These people probably know a lot of people. Some of those people might be working jobs you're interested in. Some of those people might know people who are looking for people to work for them. Get the gist?

A further couple points:

Tell people what you're looking for. If they don't know, they can't help you or hook you up with opportunities they find.

If you're in school, your school probably has a Career Development Office or the like. Talk to the people there. Tell them what you're hoping to find -- whether it's a specific internship, information about a particular field, or just that you're hopelessly confused and would like their help. They have resources for you. It's their job to have resources for you.

See if you can set up informational interviews with people in fields you might be interested in, to get the scoop on what it's like to work that kind of job.

Attend job fairs -- a lot of schools host them; does yours? -- and even if you're not looking for any particular job yet, it's a great opportunity to talk to recruiters about the kinds of jobs out there.

3. Ask a whole bunch of questions.

The best thing to remember is that, in general, people really like talking about themselves. Use this to your advantage. Even simple questions like "So, what's your job like?" and "Can you tell me more about what it's like to do X?" can lead to worthwhile information.

Then what?

The next step is pretty simple. (Do recall, simple does not necessarily mean easy.)

You've learned about your options. You've learned about what you like doing. You've learned about what you find worthwhile. It's time to stop evaluating possible directions to go in and actually go in a direction.

Maybe now, you know exactly what you want to do with your life. Great -- do that! Or maybe now you've concluded that no job will ever make you content. That one's a bit tougher. Try to find something at least tolerable, or, like some people joke, marry rich? Or maybe you like everything, and the sheer number of options is still overwhelming. Your best option here: find a reasonable job in a reasonable location near people you like. Go in some direction, at least for a while. If you love it, great. If you don't, move on.

Still have questions? Post a comment below! Maybe I, or someone else, will have helpful advice for you specifically.

And no matter what, remember: You don't have to do the same thing forever.

Learning is awesome

My favorite part of just living is how much I learn. Here are some pieces of advice you might find useful, some cool skills I've acquired (maybe you'll be inspired), and a couple other things, too:

Because lists are awesome, too...

• A GPS is only helpful in localizing large vehicles, particularly when you're trying to use the GPS to direct navigation. When your vehicle is smaller than the error margin of plus or minus two meters (e.g., an RC car), it doesn't work so well! (This from last summer, at NASA Langley.)
• Pens with lights attached are a fantastic invention. I got a combo flashlight-pen at GHC last year. It writes. It lights up. This pen lives next to the pad of sticky notes by my bed. Now all my middle-of-the-night ideas are legible!
• If you're working on a big important project, always work on it, every day. Could be a thesis. Could be a novel, or a software project. Even on the days when you really don't want to work on it and you're entirely unmotivated, work on it anyway. Do a tiny little bit, then do a tiny little bit more, and maybe you'll convince yourself that you are in the mood to work on it after all. If not, at least you did a little bit, right?
• Just how cool people think NASA is. Specifically, how cool people think it is when they find out I interned there, twice. I continue to be surprised. Quite seriously. Are my standards for what counts as super awesome too high? Do I just expect everyone else to be similarly awesome, making my accomplishments average on the scale of awesomeness? Maybe I do ... everyone has the capacity for brilliance. Maybe not everyone fulfills that capacity, but I think you're suppose to take this as your cue to go be brilliant.
• I earned my Amateur Radio Technician's license. I am now qualified to talk on the HAM radio bands! I know more than I used to about electronics, antennae, and radio frequencies. I'm still working on learning Morse Code.
• Philosophy of mind. I know a decent amount on the subject from my cognitive science background, but there's always more to learn! A friend and I have delved into some fun readings: Aristotle's conception of matter and form, Aquinas on the immateriality of mind, Lawrence Shapiro on embodiment and reductionism, and many more. I'm re-reading Shapiro's The Mind Incarnate, which I initially read in my second cognitive science class ever, some three and a half years ago.
• How to successfully relocate to a new city in a new state. Yeah, I did that. It involved a lot of talking to people, a lot of driving, and a lot of paperwork and standing in lines.
• Just how flexible my sleep schedule can be. I used to be a stickler for getting my full eight hours every single night of the week. I realized over the summer that I can function just fine on a weird schedule of eight hours, then three hours, then seven hours, then maybe five, followed by nine or ten hours to catch up... I'll write more on this sometime. Carol Worthman wrote a particularly relevant chapter on sleep for Evolutionary Medicine and Health that I plan to outline for you.
• The rudiments of tae kwon do. According to the instructors at the Goddard Tae Kwon Do club, I have a decent roundhouse kick. I'd like to learn more -- I'm still very much the beginner white belt.

And a whole slew of technology-related items:

• Octave, essentially an open-source Matlab.
• R, a statistical computing language and environment.
• The rudiments of time series analysis
• ROS, an open-source platform for robotics work
• Mobile Robotics Programming Toolkit (MRPT) libraries
• PCL, the point cloud library and useful for feature detection in point clouds
• Simultaneous localization and mapping (SLAM) algorithms, as well as other common mapping and path planning algorithms.
• How to use subversion.
• Random little things about Ubuntu, including the "alt-f9" shortcut to minimize the current window
• How to use the Tobii T60 eye tracker.
• And so much more ...

I wonder if I can double this list by this time next year..?