Interview with Professor Aaron Ames

Interviewer: Sherry Wang

Can you give me an overview of your research?

Broadly speaking, what I do is study how robots walk. That’s kind of the core disciple we consider in my lab; that is, specifically achieving walking behaviors on robots that are as dynamic and dexterous and fluid as humans walking. So this actually involves quite a bit of math to try to understand and characterize what walking is, and once you can do that, you can apply it to a lot of application domains, especially domains where humans walk, or humans walking is part of it. What I mean is, robotic assisted devices, prosthetics, exoskeletons, etcetera. So I have a wide spread of robotic applications, but the real focus is dynamic locomotion.

How did you get involved in robotics, and in particular dynamic walking and dynamic motion?

It’s an interesting story that is probably not wholly unique from a Caltech perspective. I was introduced to science fiction as an undergrad, so later in life than most people. I started reading Asimov and all these other authors, and I just got totally hooked. I was just fascinated with this idea of science fiction in general, but a big theme of a lot of Asimov’s books were robotics, and humanoid robots, and all the robots playing a part in our daily lives, and thinking about what that would really mean if we could really realize those behaviors. In undergrad, I did mechanical engineering and math, and I wanted to go to grad school to really understand this better. I think what appealed to me the most was just this deceptively simple thing that we do. Humans walk around all the time, they make dynamic movements all the time, and it seems like something we should be able to understand. Yet under the surface it’s such a complicated problem I find it intriguing. It’s one of those cases where you actually know a solution exists. Feasibility is guaranteed, because humans do it all the time. You just have to be smart enough to try to find that. I was purely theoretical in grad school. I didn’t touch a robot. I didn’t touch an experiment, I didn’t build anything, I just proved theorems and theorems and theorems. I think that made me a better person, but because my passion was robots–it was something I always really loved–I ended up coming back to it as a professor. It was a couple years into being a professor, and I had been doing some walking stuff and some simulation and everything, and I realized no one listens to me, and everyone probably thinks I’m crazy because the math I was doing was really out in left field. The only way I can actually convince people is if I get stuff to work. If I get physical hardware to do awesome stuff, then maybe they’ll listen to the math. I started doing that with simple robots, and making that one walk, and then I started getting addicted and making more and more robots; I worked with NASA on a humanoid. So I started getting really into the robotics side, while at the same time not losing the math that got me excited about this stuff and keeps me going to this day. Intrinsically, it was science fiction that led to studying a whole bunch of math, that led to me actually making robots to do stuff to demonstrate the math.

What are some difficulties and successes that you’ve seen in your research?

Difficulties always stem from the fact that the real world is hard. I always say to my students, and it’s a pretty silly quote but descriptive nonetheless, which is “hardware is hard.” There’s a “hard” in the “hardware” for a reason. The reality is that however smart you think you are–however clever you think you are–you go to put something on a robot and Mother Nature lets you know very quickly that you’re stupid, and you don’t know anything, and the world’s really hard. And you constantly are reminded of this. It’s not so much a difficulty; I view it almost as an opportunity, but it’s humbling. You’re constantly humbled, and what it takes to make anything work is so incredibly complicated. It’s exactly this difficulty that gives you opportunity, and what’s exciting and leads to your success story. Especially when you try to do what’s unique to my lab, (which is not to say that there’s no other labs that do it, but it’s a fairly unique thing) which is trying to do real theory–theorems and proofs and mathematically justifiable things–and then put that on hardware. So the goal is not so much to make the hardware do something, but the goal is to understand something at a deep mathematical level, and then make the hardware demonstrate that understanding. Recently, we worked with a French startup company called WonderCraft on their powered lower-body exoskeleton. And this is specifically aimed at paraplegics. There has never before this time been a powered lower-body exoskeleton that allowed paraplegics to walk dynamically. That is, walk without crutches, move their hands freely, and have a dynamic gait. And they [WonderCraft] took math that was developed by me–my lab, with collaborators and everything–but they took theorems that were in papers that we wrote, and they put it on the exoskeleton, and they actually got paraplegics to walk for the first time ever with a powered assisted device. So that was another one of these moments. You’re writing the papers, you’re proving the theorems–in this case, it was a paper that appeared in 2014–you would never think that in four years that that math would be helping a paraplegic to walk. Those are the moments that everything comes together, and it all looks perfect. And then you realize, “time to go back and try to do something new.” Now we want to have the next behavior, and nature starts smacking you down again. Then nature starts reminding you that you have a lot to learn. The success is great, but there’s always problems out there–which I find exciting. There’s never a lack of problems to solve.

What behaviors are you investigating now?

On the legged robots side, we really want to get robots out in the real world more and more. We want to have them do things that are impressive and dynamic, but in a really unstructured environments. Examples are, we want to take a robot on a hike. We want it to be able to hike one of the trails up in the mountains near Caltech. To actually be able to handle all these different terrain types autonomously, rather than just a pre-prescribed [route]. And not just walking; we’d love to do running and jumping. We’ve made a robot jump in our lab, which was amazing, but we’d like to get dynamic behaviors outside, in the real world. So on the walking robot side, that’s what it’s all about to us. It’s handling more and more complex terrain in a robust and dynamic way.

And in the same light, translating those behaviors that we’ve achieving on walking robots to robotic assisted devices. We actually have one of these exoskeletons by WonderCraft in our lab at Caltech now. We’re putting our new, more advanced stuff on it and testing it out, and we’d love to get that device walking outside, and walking on sand and dirt. Right now it’s walked dynamically, but in clinical settings. Everything we’re doing is pushing outside the lab. Start to handle the complexity of the real world. And that’s where a lot of interesting theoretical problems come from too. How do you quantify that uncertainty? How do you potentially bring in machine learning to make sense of that uncertainty, but use it in the context of the models and the theories that governs the motions of the systems. That’s where we’re pushing: trying to get robots into the real world, and have them try to do fun things.

Do you see the field progressing toward the “singularity” that science fiction authors write about?

I’d love to think that we’re that close, but no, we’re way off from any sort of “singularity”. We can’t even make robots do simple stuff still. If you think about the million years of evolution that it took for humans to do the things that we do, we haven’t recreated a fraction of it. We’ve come an incredibly long way. When I started doing experimental robotics, I had really simple robots and just making them walk dynamically was a big deal. This was before Boston Dynamics days. At the time, Boston Dynamics had quadrupeds that were cool, but no walking robots–no bipeds–and that was only like eight years ago, so it wasn’t that long ago. And now, we have humanoid walking robots. I take our biped from our lab outside and have it walk on grass and dirt, completely untethered, 3D walking dynamically. We’ve made a huge progression towards getting robots out of the lab and into the “real world”, but it’s still very scripted. When we have our walking robots walk on grass or dirt, we get it in various specific scenarios, and we tune the robot right, and we can’t have it go walk some mountain trail, or go walk downtown Pasadena and pick me up a cup of coffee and bring it back to me. These higher level behaviors and reasoning are an enigma to this day. And in some ways, the world of robotics is also bifurcated at this point. There’s a whole wealth of people that are working on getting robots to do dynamic things, and then there’s a whole wealth of people working on machine learning and AI, and those are different people. The things that [machine] learning is good at are things that can’t, right now, be translated to actual robots. And conversely, the things that we can make robots do doesn’t currently doesn’t inform machine learning. I think that’s starting to change, and people are looking for that intersection. [However,] to talk about a singularity is to talk about a robot that can do everything from walk effortlessly, to actually reason about its environment, much less become conscious. We’re just not even close. Right now, if I want to lose my robot, I’ll walk into a volleyball court and it will fall on its face, I guarantee–any robot will. Soft sand, or go in some snow, and it’s over. Until we can do those basic things that animals do so easily, much less humans, we’re a long ways off from achieving a “singularity”.

What are some of your interests outside of science?

I still love science fiction to this day. Books, movies, all of it, I find it really exciting. There’s nothing like a good, corny science fiction movie when you’re burned out mentally, or a good sci-fi book to rekindle that excitement in science. Little things just around LA. We try to take hikes–I have a dog, so I love going on walks with my dog and my wife, and going on hikes around Pasadena.

Do you have any career advice for students interested in robotics?

It’s an exciting area with a lot of potential. It’s interesting because the robotics push has been going on for a while now. It was a “dead” area for a while–very small–and it just bloomed research-wise in the last decade. And it doesn’t look like it’s slowing. We’re seeing more and more robots going into the real world. One of my students is the CTO of a startup that’s doing cooking robots–Miso Robotics–and he’s getting these robots into Dodger Stadium and cooking burgers. So you’re starting to see robots in the real world, so it’s an exciting time to be in the domain. My advice for students that are interested is to, at the undergraduate level, get involved with research labs. There’s no replacement for getting your hands on hardware and starting to play with these things early on in your undergraduate studies. Go to grad school because that’ll give you a depth in robotics that’s hard to emulate. Get involved in research because that’s where the exciting stuff is still happening. That’s not to say that industry doesn’t have exciting things, but the next generation [is] being developed [in research labs]. Get involved early, stay involved, and be part of some of these fun projects. The great thing about robotics is that it doesn’t take years of training to do something. You get in with an undergraduate degree–the standard courses you have–and pretty quickly make something happen that can motivate you to go deeper and deeper into the area.

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