Tag Archives: lifting

#439089 Ingenuity’s Chief Pilot Explains How ...

On April 11, the Mars helicopter Ingenuity will take to the skies of Mars for the first time. It will do so fully autonomously, out of necessity—the time delay between Ingenuity’s pilots at the Jet Propulsion Laboratory and Jezero Crater on Mars makes manual or even supervisory control impossible. So the best that the folks at JPL can do is practice as much as they can in simulation, and then hope that the helicopter can handle everything on its own.

Here on Earth, simulation is a critical tool for many robotics applications, because it doesn’t rely on access to expensive hardware, is non-destructive, and can be run in parallel and at faster-than-real-time speeds to focus on solving specific problems. Once you think you’ve gotten everything figured out in simulation, you can always give it a try on the real robot and see how close you came. If it works in real life, great! And if not, well, you can tweak some stuff in the simulation and try again.

For the Mars helicopter, simulation is much more important, and much higher stakes. Testing the Mars helicopter under conditions matching what it’ll find on Mars is not physically possible on Earth. JPL has flown engineering models in Martian atmospheric conditions, and they’ve used an actuated tether to mimic Mars gravity, but there’s just no way to know what it’ll be like flying on Mars until they’ve actually flown on Mars. With that in mind, the Ingenuity team has been relying heavily on simulation, since that’s one of the best tools they have to prepare for their Martian flights. We talk with Ingenuity’s Chief Pilot, Håvard Grip, to learn how it all works.

Ingenuity Facts:
Body Size: a box of tissues

Brains: Qualcomm Snapdragon 801

Weight: 1.8 kilograms

Propulsion: Two 1.2m carbon fiber rotors

Navigation sensors: VGA camera, laser altimeter, inclinometer

Ingenuity is scheduled to make its first flight no earlier than April 11. Before liftoff, the Ingenuity team will conduct a variety of pre-flight checks, including verifying the responsiveness of the control system and spinning the blades up to full speed (2,537 rpm) without lifting off. If everything looks good, the first flight will consist of a 1 meter per second climb to 3 meters, 30 seconds of hover at 3 meters while rotating in place a bit, and then a descent to landing. If Ingenuity pulls this off, that will have made its entire mission a success. There will be more flights over the next few weeks, but all it takes is one to prove that autonomous helicopter flight on Mars is possible.

Last month, we spoke with Mars Helicopter Operations Lead Tim Canham about Ingenuity’s hardware, software, and autonomy, but we wanted to know more about how the Ingenuity team has been using simulation for everything from vehicle design to flight planning. To answer our questions, we talked with JPL’s Håvard Grip, who led the development of Ingenuity’s navigation and flight control systems. Grip also has the title of Ingenuity Chief Pilot, which is pretty awesome. He summarizes this role as “operating the flight control system to make the helicopter do what we want it to do.”

IEEE Spectrum: Can you tell me about the simulation environment that JPL uses for Ingenuity’s flight planning?

Håvard Grip: We developed a Mars helicopter simulation ourselves at JPL, based on a multi-body simulation framework that’s also developed at JPL, called DARTS/DSHELL. That's a system that has been in development at JPL for about 30 years now, and it's been used in a number of missions. And so we took that multibody simulation framework, and based on it we built our own Mars helicopter simulation, put together our own rotor model, our own aerodynamics models, and everything else that's needed in order to simulate a helicopter. We also had a lot of help from the rotorcraft experts at NASA Ames and NASA Langley.

Image: NASA/JPL

Ingenuity in JPL’s flight simulator.

Without being able to test on Mars, how much validation are you able to do of what you’re seeing in simulation?

We can do a fair amount, but it requires a lot of planning. When we made our first real prototype (with a full-size rotor that looked like what we were thinking of putting on Mars) we first spent a lot of time designing it and using simulation tools to guide that design, and when we were sufficiently confident that we were close enough, and that we understood enough about it, then we actually built the thing and designed a whole suite of tests in a vacuum chamber where where we could replicate Mars atmospheric conditions. And those tests were before we tried to fly the helicopter—they were specifically targeted at what we call system identification, which has to do with figuring out what the true properties, the true dynamics of a system are, compared to what we assumed in our models. So then we got to see how well our models did, and in the places where they needed adjustment, we could go back and do that.

The simulation work that we really started after that very first initial lift test, that’s what allowed us to unlock all of the secrets to building a helicopter that can fly on Mars.
—Håvard Grip, Ingenuity Chief Pilot

We did a lot of this kind of testing. It was a big campaign, in several stages. But there are of course things that you can't fully replicate, and you do depend on simulation to tie things together. For example, we can't truly replicate Martian gravity on Earth. We can replicate the atmosphere, but not the gravity, and so we have to do various things when we fly—either make the helicopter very light, or we have to help it a little bit by pulling up on it with a string to offload some of the weight. These things don't fully replicate what it will be like on Mars. We also can't simultaneously replicate the Mars aerodynamic environment and the physical and visual surroundings that the helicopter will be flying in. These are places where simulation tools definitely come in handy, with the ability to do full flight tests from A to B, with the helicopter taking off from the ground, running the flight software that it will be running on board, simulating the images that the navigation camera takes of the ground below as it flies, feeding that back into the flight software, and then controlling it.

To what extent can simulation really compensate for the kinds of physical testing that you can’t do on Earth?

It gives you a few different possibilities. We can take certain tests on Earth where we replicate key elements of the environment, like the atmosphere or the visual surroundings for example, and you can validate your simulation on those parameters that you can test on Earth. Then, you can combine those things in simulation, which gives you the ability to set up arbitrary scenarios and do lots and lots of tests. We can Monte Carlo things, we can do a flight a thousand times in a row, with small perturbations of various parameters and tease out what our sensitivities are to those things. And those are the kinds of things that you can't do with physical tests, both because you can't fully replicate the environment and also because of the resources that would be required to do the same thing a thousand times in a row.

Because there are limits to the physical testing we can do on Earth, there are elements where we know there's more uncertainty. On those aspects where the uncertainty is high, we tried to build in enough margin that we can handle a range of things. And simulation gives you the ability to then maybe play with those parameters, and put them at their outer limits, and test them beyond where the real parameters are going to be to make sure that you have robustness even in those extreme cases.

How do you make sure you’re not relying on simulation too much, especially since in some ways it’s your only option?

It’s about anchoring it in real data, and we’ve done a lot of that with our physical testing. I think what you’re referring to is making your simulation too perfect, and we’re careful to model the things that matter. For example, the simulated sensors that we use have realistic levels of simulated noise and bias in them, the navigation camera images have realistic levels of degradation, we have realistic disturbances from wind gusts. If you don’t properly account for those things, then you’re missing important details. So, we try to be as accurate as we can, and to capture that by overbounding in areas where we have a high degree of uncertainty.

What kinds of simulated challenges have you put the Mars helicopter through, and how do you decide how far to push those challenges?

One example is that we can simulate going over rougher terrain. We can push that, and see how far we can go and still have the helicopter behave the way that we want it to. Or we can inject levels of noise that maybe the real sensors don't see, but you want to just see how far you can push things and make sure that it's still robust.

Where we put the limits on this and what we consider to be realistic is often a challenge. We consider this on a case by case basis—if you have a sensor that you're dealing with, you try to do testing with it to characterize it and understand its performance as much as possible, and you build a level of confidence in it that allows you to find the proper balance.

When it comes to things like terrain roughness, it's a little bit of a different thing, because we're actually picking where we're flying the helicopter. We have made that choice, and we know what the terrain looks like around us, so we don’t have to wonder about that anymore.

Image: NASA/JPL-Caltech/University of Arizona

Satellite image of the Ingenuity flight area.

The way that we’re trying to approach this operationally is that we should be done with the engineering at this point. We’re not depending on going back and resimulating things, other than a few checks here and there.

Are there any examples of things you learned as part of the simulation process that resulted in changes to the hardware or mission?

You know, it’s been a journey. One of the early things that we discovered as part of modeling the helicopter was that the rotor dynamics were quite different for a helicopter on Mars, in particular with respect to how the rotor responds to the up and down bending of the blades because they’re not perfectly rigid. That motion is a very important influence on the overall flight dynamics of the helicopter, and what we discovered as we started modeling was that this motion is damped much less on Mars. Under-damped oscillatory things like that, you kind of figure might pose a control issue, and that is the case here: if you just naively design it as you might a helicopter on Earth, without taking this into account, you could have a system where the response to control inputs becomes very sluggish. So that required changes to the vehicle design from some of the very early concepts, and it led us to make a rotor that’s extremely light and rigid.

The design cycle for the Mars helicopter—it’s not like we could just build something and take it out to the back yard and try it and then come back and tweak it if it doesn’t work. It’s a much bigger effort to build something and develop a test program where you have to use a vacuum chamber to test it. So you really want to get as close as possible up front, on your first iteration, and not have to go back to the drawing board on the basic things.

So how close were you able to get on your first iteration of the helicopter design?

[This video shows] a very early demo which was done more or less just assuming that things were going to behave as they would on Earth, and that we’d be able to fly in a Martian atmosphere just spinning the rotor faster and having a very light helicopter. We were basically just trying to demonstrate that we could produce enough lift. You can see the helicopter hopping around, with someone trying to joystick it, but it turned out to be very hard to control. This was prior to doing any of the modeling that I talked about earlier. But once we started seriously focusing on the modeling and simulation, we then went on to build a prototype vehicle which had a full-size rotor that’s very close to the rotor that will be flying on Mars. One difference is that prototype had cyclic control only on the lower rotor, and later we added cyclic control on the upper rotor as well, and that decision was informed in large part by the work we did in simulation—we’d put in the kinds of disturbances that we thought we might see on Mars, and decided that we needed to have the extra control authority.

How much room do you think there is for improvement in simulation, and how could that help you in the future?

The tools that we have were definitely sufficient for doing the job that we needed to do in terms of building a helicopter that can fly on Mars. But simulation is a compute-intensive thing, and so I think there’s definitely room for higher fidelity simulation if you have the compute power to do so. For a future Mars helicopter, you could get some benefits by more closely coupling together high-fidelity aerodynamic models with larger multi-body models, and doing that in a fast way, where you can iterate quickly. There’s certainly more potential for optimizing things.

Photo: NASA/JPL-Caltech

Ingenuity preparing for flight.

Watching Ingenuity’s first flight take place will likely be much like watching the Perseverance landing—we’ll be able to follow along with the Ingenuity team while they send commands to the helicopter and receive data back, although the time delay will mean that any kind of direct control won’t be possible. If everything goes the way it’s supposed to, there will hopefully be some preliminary telemetry from Ingenuity saying so, but it sounds like we’ll likely have to wait until April 12 before we get pictures or video of the flight itself.

Because Mars doesn’t care what time it is on Earth, the flight will actually be taking place very early on April 12, with the JPL Mission Control livestream starting at 3:30 a.m. EDT (12:30 a.m. PDT). Details are here. Continue reading

Posted in Human Robots

#437940 How Boston Dynamics Taught Its Robots to ...

A week ago, Boston Dynamics posted a video of Atlas, Spot, and Handle dancing to “Do You Love Me.” It was, according to the video description, a way “to celebrate the start of what we hope will be a happier year.” As of today the video has been viewed nearly 24 million times, and the popularity is no surprise, considering the compelling mix of technical prowess and creativity on display.

Strictly speaking, the stuff going on in the video isn’t groundbreaking, in the sense that we’re not seeing any of the robots demonstrate fundamentally new capabilities, but that shouldn’t take away from how impressive it is—you’re seeing state-of-the-art in humanoid robotics, quadrupedal robotics, and whatever-the-heck-Handle-is robotics.

What is unique about this video from Boston Dynamics is the artistic component. We know that Atlas can do some practical tasks, and we know it can do some gymnastics and some parkour, but dancing is certainly something new. To learn more about what it took to make these dancing robots happen (and it’s much more complicated than it might seem), we spoke with Aaron Saunders, Boston Dynamics’ VP of Engineering.

Saunders started at Boston Dynamics in 2003, meaning that he’s been a fundamental part of a huge number of Boston Dynamics’ robots, even the ones you may have forgotten about. Remember LittleDog, for example? A team of two designed and built that adorable little quadruped, and Saunders was one of them.

While he’s been part of the Atlas project since the beginning (and had a hand in just about everything else that Boston Dynamics works on), Saunders has spent the last few years leading the Atlas team specifically, and he was kind enough to answer our questions about their dancing robots.

IEEE Spectrum: What’s your sense of how the Internet has been reacting to the video?

Aaron Saunders: We have different expectations for the videos that we make; this one was definitely anchored in fun for us. The response on YouTube was record-setting for us: We received hundreds of emails and calls with people expressing their enthusiasm, and also sharing their ideas for what we should do next, what about this song, what about this dance move, so that was really fun. My favorite reaction was one that I got from my 94-year-old grandma, who watched the video on YouTube and then sent a message through the family asking if I’d taught the robot those sweet moves. I think this video connected with a broader audience, because it mixed the old-school music with new technology.

We haven’t seen Atlas move like this before—can you talk about how you made it happen?

We started by working with dancers and a choreographer to create an initial concept for the dance by composing and assembling a routine. One of the challenges, and probably the core challenge for Atlas in particular, was adjusting human dance moves so that they could be performed on the robot. To do that, we used simulation to rapidly iterate through movement concepts while soliciting feedback from the choreographer to reach behaviors that Atlas had the strength and speed to execute. It was very iterative—they would literally dance out what they wanted us to do, and the engineers would look at the screen and go “that would be easy” or “that would be hard” or “that scares me.” And then we’d have a discussion, try different things in simulation, and make adjustments to find a compatible set of moves that we could execute on Atlas.

Throughout the project, the time frame for creating those new dance moves got shorter and shorter as we built tools, and as an example, eventually we were able to use that toolchain to create one of Atlas’ ballet moves in just one day, the day before we filmed, and it worked. So it’s not hand-scripted or hand-coded, it’s about having a pipeline that lets you take a diverse set of motions, that you can describe through a variety of different inputs, and push them through and onto the robot.

Image: Boston Dynamics

Were there some things that were particularly difficult to translate from human dancers to Atlas? Or, things that Atlas could do better than humans?

Some of the spinning turns in the ballet parts took more iterations to get to work, because they were the furthest from leaping and running and some of the other things that we have more experience with, so they challenged both the machine and the software in new ways. We definitely learned not to underestimate how flexible and strong dancers are—when you take elite athletes and you try to do what they do but with a robot, it’s a hard problem. It’s humbling. Fundamentally, I don’t think that Atlas has the range of motion or power that these athletes do, although we continue developing our robots towards that, because we believe that in order to broadly deploy these kinds of robots commercially, and eventually in a home, we think they need to have this level of performance.

One thing that robots are really good at is doing something over and over again the exact same way. So once we dialed in what we wanted to do, the robots could just do it again and again as we played with different camera angles.

I can understand how you could use human dancers to help you put together a routine with Atlas, but how did that work with Spot, and particularly with Handle?

I think the people we worked with actually had a lot of talent for thinking about motion, and thinking about how to express themselves through motion. And our robots do motion really well—they’re dynamic, they’re exciting, they balance. So I think what we found was that the dancers connected with the way the robots moved, and then shaped that into a story, and it didn’t matter whether there were two legs or four legs. When you don’t necessarily have a template of animal motion or human behavior, you just have to think a little harder about how to go about doing something, and that’s true for more pragmatic commercial behaviors as well.

“We used simulation to rapidly iterate through movement concepts while soliciting feedback from the choreographer to reach behaviors that Atlas had the strength and speed to execute. It was very iterative—they would literally dance out what they wanted us to do, and the engineers would look at the screen and go ‘that would be easy’ or ‘that would be hard’ or ‘that scares me.’”
—Aaron Saunders, Boston Dynamics

How does the experience that you get teaching robots to dance, or to do gymnastics or parkour, inform your approach to robotics for commercial applications?

We think that the skills inherent in dance and parkour, like agility, balance, and perception, are fundamental to a wide variety of robot applications. Maybe more importantly, finding that intersection between building a new robot capability and having fun has been Boston Dynamics’ recipe for robotics—it’s a great way to advance.

One good example is how when you push limits by asking your robots to do these dynamic motions over a period of several days, you learn a lot about the robustness of your hardware. Spot, through its productization, has become incredibly robust, and required almost no maintenance—it could just dance all day long once you taught it to. And the reason it’s so robust today is because of all those lessons we learned from previous things that may have just seemed weird and fun. You’ve got to go into uncharted territory to even know what you don’t know.

Image: Boston Dynamics

It’s often hard to tell from watching videos like these how much time it took to make things work the way you wanted them to, and how representative they are of the actual capabilities of the robots. Can you talk about that?

Let me try to answer in the context of this video, but I think the same is true for all of the videos that we post. We work hard to make something, and once it works, it works. For Atlas, most of the robot control existed from our previous work, like the work that we’ve done on parkour, which sent us down a path of using model predictive controllers that account for dynamics and balance. We used those to run on the robot a set of dance steps that we’d designed offline with the dancers and choreographer. So, a lot of time, months, we spent thinking about the dance and composing the motions and iterating in simulation.

Dancing required a lot of strength and speed, so we even upgraded some of Atlas’ hardware to give it more power. Dance might be the highest power thing we’ve done to date—even though you might think parkour looks way more explosive, the amount of motion and speed that you have in dance is incredible. That also took a lot of time over the course of months; creating the capability in the machine to go along with the capability in the algorithms.

Once we had the final sequence that you see in the video, we only filmed for two days. Much of that time was spent figuring out how to move the camera through a scene with a bunch of robots in it to capture one continuous two-minute shot, and while we ran and filmed the dance routine multiple times, we could repeat it quite reliably. There was no cutting or splicing in that opening two-minute shot.

There were definitely some failures in the hardware that required maintenance, and our robots stumbled and fell down sometimes. These behaviors are not meant to be productized and to be a 100 percent reliable, but they’re definitely repeatable. We try to be honest with showing things that we can do, not a snippet of something that we did once. I think there’s an honesty required in saying that you’ve achieved something, and that’s definitely important for us.

You mentioned that Spot is now robust enough to dance all day. How about Atlas? If you kept on replacing its batteries, could it dance all day, too?

Atlas, as a machine, is still, you know… there are only a handful of them in the world, they’re complicated, and reliability was not a main focus. We would definitely break the robot from time to time. But the robustness of the hardware, in the context of what we were trying to do, was really great. And without that robustness, we wouldn’t have been able to make the video at all. I think Atlas is a little more like a helicopter, where there’s a higher ratio between the time you spend doing maintenance and the time you spend operating. Whereas with Spot, the expectation is that it’s more like a car, where you can run it for a long time before you have to touch it.

When you’re teaching Atlas to do new things, is it using any kind of machine learning? And if not, why not?

As a company, we’ve explored a lot of things, but Atlas is not using a learning controller right now. I expect that a day will come when we will. Atlas’ current dance performance uses a mixture of what we like to call reflexive control, which is a combination of reacting to forces, online and offline trajectory optimization, and model predictive control. We leverage these techniques because they’re a reliable way of unlocking really high performance stuff, and we understand how to wield these tools really well. We haven’t found the end of the road in terms of what we can do with them.

We plan on using learning to extend and build on the foundation of software and hardware that we’ve developed, but I think that we, along with the community, are still trying to figure out where the right places to apply these tools are. I think you’ll see that as part of our natural progression.

Image: Boston Dynamics

Much of Atlas’ dynamic motion comes from its lower body at the moment, but parkour makes use of upper body strength and agility as well, and we’ve seen some recent concept images showing Atlas doing vaults and pullups. Can you tell us more?

Humans and animals do amazing things using their legs, but they do even more amazing things when they use their whole bodies. I think parkour provides a fantastic framework that allows us to progress towards whole body mobility. Walking and running was just the start of that journey. We’re progressing through more complex dynamic behaviors like jumping and spinning, that’s what we’ve been working on for the last couple of years. And the next step is to explore how using arms to push and pull on the world could extend that agility.

One of the missions that I’ve given to the Atlas team is to start working on leveraging the arms as much as we leverage the legs to enhance and extend our mobility, and I’m really excited about what we’re going to be working on over the next couple of years, because it’s going to open up a lot more opportunities for us to do exciting stuff with Atlas.

What’s your perspective on hydraulic versus electric actuators for highly dynamic robots?

Across my career at Boston Dynamics, I’ve felt passionately connected to so many different types of technology, but I’ve settled into a place where I really don’t think this is an either-or conversation anymore. I think the selection of actuator technology really depends on the size of the robot that you’re building, what you want that robot to do, where you want it to go, and many other factors. Ultimately, it’s good to have both kinds of actuators in your toolbox, and I love having access to both—and we’ve used both with great success to make really impressive dynamic machines.

I think the only delineation between hydraulic and electric actuators that appears to be distinct for me is probably in scale. It’s really challenging to make tiny hydraulic things because the industry just doesn’t do a lot of that, and the reciprocal is that the industry also doesn’t tend to make massive electrical things. So, you may find that to be a natural division between these two technologies.

Besides what you’re working on at Boston Dynamics, what recent robotics research are you most excited about?

For us as a company, we really love to follow advances in sensing, computer vision, terrain perception, these are all things where the better they get, the more we can do. For me personally, one of the things I like to follow is manipulation research, and in particular manipulation research that advances our understanding of complex, friction-based interactions like sliding and pushing, or moving compliant things like ropes.

We’re seeing a shift from just pinching things, lifting them, moving them, and dropping them, to much more meaningful interactions with the environment. Research in that type of manipulation I think is going to unlock the potential for mobile manipulators, and I think it’s really going to open up the ability for robots to interact with the world in a rich way.

Is there anything else you’d like people to take away from this video?

For me personally, and I think it’s because I spend so much of my time immersed in robotics and have a deep appreciation for what a robot is and what its capabilities and limitations are, one of my strong desires is for more people to spend more time with robots. We see a lot of opinions and ideas from people looking at our videos on YouTube, and it seems to me that if more people had opportunities to think about and learn about and spend time with robots, that new level of understanding could help them imagine new ways in which robots could be useful in our daily lives. I think the possibilities are really exciting, and I just want more people to be able to take that journey. Continue reading

Posted in Human Robots