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#439465 Dextrous Robotics Wants To Move Boxes ...

Hype aside, there aren’t necessarily all that many areas where robots have the potential to step into an existing workflow and immediately provide a substantial amount of value. But one of the areas that we have seen several robotics companies jump into recently is box manipulation—specifically, using robots to unload boxes from the back of a truck, ideally significantly faster than a human. This is a good task for robots because it plays to their strengths: you can work in a semi-structured and usually predictable environment, speed, power, and precision are all valued highly, and it’s not a job that humans are particularly interested in or designed for.

One of the more novel approaches to this task comes from Dextrous Robotics, a Memphis TN-based startup led by Evan Drumwright. Drumwright was a professor at GWU before spending a few years at the Toyota Research Institute and then co-founding Dextrous in 2019 with an ex-student of his, Sam Zapolsky. The approach that they’ve come up with is to do box manipulation without any sort of suction, or really any sort of grippers at all. Instead, they’re using what can best be described as a pair of moving arms, each gripping a robotic chopstick.

We can pick up basically anything using chopsticks. If you're good with chopsticks, you can pick up individual grains of rice, and you can pick up things that are relatively large compared to the scale of the chopsticks. Your imagination is about the limit, so wouldn't it be cool if you had a robot that could manipulate things with chopsticks? —Evan Drumwright

It definitely is cool, but are there practical reasons why using chopsticks for box manipulation is a good idea? Of course there are! The nice thing about chopsticks is that they really can grip almost anything (even if you scale them up), making them especially valuable in constrained spaces where you’ve got large disparities in shapes and sizes and weights. They’re good for manipulation, too, able to nudge and reposition things with precision. And while Dextrous is initially focused on a trailer unloading task, having this extra manipulation capability will allow them to consider more difficult manipulation tasks in the future, like trailer loading, a task that necessarily happens just as often as unloading does but which is significantly more complicated to robot-ize.

Even though there are some clear advantages to Dextrous’ chopstick technique, there are disadvantages as well, and the biggest one is likely that it’s just a lot harder to use a manipulation technique like this. “The downside of the chopsticks approach is, as any human will tell you, you need some sophisticated control software to be able to operate,” Drumwright tells us. “But that’s part of what we bring to the game: not just a clever hardware design, but the software to operate it, too.”

Meanwhile, what we’ve seen so far from other companies in this space is pretty consistent use of suction systems for box handling. If you have a flat, non-permeable surface (as with most boxes), suction can work quickly and reliably and with a minimum of fancy planning. However, suction has limits form of manipulation, because it’s inherently so sticky, meaning that it can be difficult and/or time consuming to do anything with precision. Other issues with suction include its sensitivity to temperature and moisture, its propensity to ingest all the dirt it possibly can, and the fact that you need to design the suction array based on the biggest and heaviest things that you anticipate having to deal with. That last thing is a particular problem because if you also want to manipulate smaller objects, you’re left trying to do so with a suction array that’s way bigger than you’d like it to be. This is not to say that suction is inferior in all cases, and Drumwright readily admits that suction will probably prove to be a good option for some specific tasks. But chopstick manipulation, if they can get it to work, will be a lot more versatile.

Dextrous Robotics co-founders Evan Drumwright and Sam Zapolsky.
Photo: Dextrous Robotics

I think there's a reason that nature has given us hands. Nature knows how to design suction devices—bats have it, octopi have it, frogs have it—and yet we have hands. Why? Hands are a superior instrument. And so, that's why we've gone down this road. I personally believe, based on billions of years of evolution, that there's a reason that manipulation is superior and that that technology is going to win out. —Evan Drumwright

Part of Dextrous’ secret sauce is an emphasis on simulation. Hardware is hard, so ideally, you want to make one thing that just works the first time, rather than having to iterate over and over. Getting it perfect on the first try is probably unrealistic, but the better you can simulate things in advance, the closer you can get. “What we’ve been able to do is set up our entire planning perception and control system so that it looks exactly like it does when that code runs on the real robot,” says Drumwright. “When we run something on the simulated robot, it agrees with reality about 95 percent of the time, which is frankly unprecedented.” Using very high fidelity hardware modeling, a real time simulator, and software that can directly transfer between sim and real, Dextrous is able to confidently model how their system performs even on notoriously tricky things to simulate, like contact and stiction. The idea is that the end result will be a system that can be developed faster while performing more complex tasks better than other solutions.

We were also wondering why this system uses smooth round chopsticks rather than something a little bit grippier, like chopsticks with a square cross section, and maybe with some higher friction something on the inside surface. Drumwright explains that the advantage of the current design is that it’s symmetrical around its rotational axis, meaning that you only need five degrees of freedom to fully control it. “What that means practically is that things can get a whole lot simpler—the control algorithms get simpler, the inverse kinematics algorithms get simpler, and importantly the number of motors that we need to drive in the robot goes down.”

Simulated version of Dextrous Robotics’ hardware.
Screenshot: Dextrous Robotics

Dextrous took seed funding 18 months ago, and since then they’ve been working on both the software and hardware for their system as well as finding the time to score an NSF SBIR phase 1 grant. The above screenshot shows the simulation of the hardware they’re working towards (chopstick manipulators on two towers that can move laterally), while the Franka Panda arms are what they’re using to validate their software in the meantime. New hardware should be done imminently, and over the next year, Dextrous is looking forward to conducting paid pilots with real customers. Continue reading

Posted in Human Robots

#439443 This Robot Taught Itself to Run, Then ...

In the last few months, robots have learned some pretty cool new skills, including performing a sweet coordinated dance routine and making pizzas from start to finish. Now there’s another accomplishment to add to the list: a bipedal robot named Cassie just ran a 5K.

Made by Agility Robotics, which was spun out of Oregon State University, Cassie was developed using a $1 million grant from DARPA. The robot is basically a pair of mechanical legs with a battery pack sitting on top. Thanks to the design of its hip joints, its legs can move forward, backward, or side to side.

Earlier this year, a group of students at Berkeley used machine learning to teach Cassie to walk. But making the leap from walking to running wasn’t as straightforward as you might think. To us, running is just a faster version of walking, and we don’t often consider the various skills and brain regions that go into even a short jog around the neighborhood.

Our core muscles engage to help keep us balanced as we’re in constant motion. Our vision scans the area in front of us for obstacles to avoid, changing course as necessary. Our heart rate kicks up a few notches, and our respiratory system regulates our breathing.

Granted, it’s a little different for a robot, since they don’t have lungs or a heart. But they do have a “brain” (software), “muscles” (hardware), and “fuel” (a battery), and these all had to work together for Cassie to be able to run.

The brunt of the work fell to the brain—in this case, a machine learning algorithm developed by students at Oregon State University’s Dynamic Robotics Laboratory. Specifically, they used deep reinforcement learning, a method that mimics the way humans learn from experience by using a trial-and-error process guided by feedback and rewards. Over many repetitions, the algorithm uses this process to learn how to accomplish a set task. In this case, since it was trying to learn to run, it may have tried moving the robot’s legs varying distances or at distinct angles while keeping it upright.

Once Cassie got a good gait down, completing the 5K was as much a matter of battery life as running prowess. The robot covered the whole distance (a course circling around the university campus) on a single battery charge in just over 53 minutes, but that did include six and a half minutes of troubleshooting; the computer had to be reset after it overheated, as well as after Cassie fell during a high-speed turn. But hey, an overheated computer getting reset isn’t so different from a human runner pausing to douse their head and face with a cup of water to cool off, or chug some water to rehydrate.

Cassie isn’t the first two-legged robot to run. Honda’s Asimo robot had a slow jog down in 2004, and Boston Dynamics’ Atlas bot looks (sort of frighteningly) like a person when it runs, moving its arms in coordination with its legs. But it is notable that Cassie taught itself to run, as it shows off machine learning’s potential in robotic systems.

And this feat is just the beginning. “The students combined expertise from biomechanics and existing robot control approaches with new machine learning tools,” said Jonathan Hurst, a robotics professor who co-founded Agility in 2017. “This type of holistic approach will enable animal-like levels of performance. It’s incredibly exciting.”

Image Credit: Agility Robotics/Oregon State University Dynamic Robotics Laboratory Continue reading

Posted in Human Robots

#439441 Bipedal robot makes history by learning ...

Cassie the robot, invented at Oregon State University and produced by OSU spinout company Agility Robotics, has made history by traversing 5 kilometers, completing the route in just over 53 minutes. Continue reading

Posted in Human Robots

#439439 Swarms of tiny dumb robots found to ...

A team of researchers affiliated with several institutions in Europe has found that swarms of tiny dumb vibrating robots are capable of carrying out sophisticated actions such as transporting objects or squeezing through tunnels. In their paper published in the journal Science Robotics, the group describes experiments they conducted with tiny dumb robots they called “bugs.” Continue reading

Posted in Human Robots

#439406 Dextrous Robotics Wants To Move Boxes ...

Hype aside, there aren’t necessarily all that many areas where robots have the potential to step into an existing workflow and immediately provide a substantial amount of value. But one of the areas that we have seen several robotics companies jump into recently is box manipulation—specifically, using robots to unload boxes from the back of a truck, ideally significantly faster than a human. This is a good task for robots because it plays to their strengths: you can work in a semi-structured and usually predictable environment, speed, power, and precision are all valued highly, and it’s not a job that humans are particularly interested in or designed for.

One of the more novel approaches to this task comes from Dextrous Robotics, a Memphis TN-based startup led by Evan Drumwright. Drumwright was a professor at GWU before spending a few years at the Toyota Research Institute and then co-founding Dextrous in 2019 with an ex-student of his, Sam Zapolsky. The approach that they’ve come up with is to do box manipulation without any sort of suction, or really any sort of grippers at all. Instead, they’re using what can best be described as a pair of moving arms, each gripping a robotic chopstick.

We can pick up basically anything using chopsticks. If you're good with chopsticks, you can pick up individual grains of rice, and you can pick up things that are relatively large compared to the scale of the chopsticks. Your imagination is about the limit, so wouldn't it be cool if you had a robot that could manipulate things with chopsticks? —Evan Drumwright

It definitely is cool, but are there practical reasons why using chopsticks for box manipulation is a good idea? Of course there are! The nice thing about chopsticks is that they really can grip almost anything (even if you scale them up), making them especially valuable in constrained spaces where you’ve got large disparities in shapes and sizes and weights. They’re good for manipulation, too, able to nudge and reposition things with precision. And while Dextrous is initially focused on a trailer unloading task, having this extra manipulation capability will allow them to consider more difficult manipulation tasks in the future, like trailer loading, a task that necessarily happens just as often as unloading does but which is significantly more complicated to robot-ize.

Even though there are some clear advantages to Dextrous’ chopstick technique, there are disadvantages as well, and the biggest one is likely that it’s just a lot harder to use a manipulation technique like this. “The downside of the chopsticks approach is, as any human will tell you, you need some sophisticated control software to be able to operate,” Drumwright tells us. “But that’s part of what we bring to the game: not just a clever hardware design, but the software to operate it, too.”

Meanwhile, what we’ve seen so far from other companies in this space is pretty consistent use of suction systems for box handling. If you have a flat, non-permeable surface (as with most boxes), suction can work quickly and reliably and with a minimum of fancy planning. However, suction has limits form of manipulation, because it’s inherently so sticky, meaning that it can be difficult and/or time consuming to do anything with precision. Other issues with suction include its sensitivity to temperature and moisture, its propensity to ingest all the dirt it possibly can, and the fact that you need to design the suction array based on the biggest and heaviest things that you anticipate having to deal with. That last thing is a particular problem because if you also want to manipulate smaller objects, you’re left trying to do so with a suction array that’s way bigger than you’d like it to be. This is not to say that suction is inferior in all cases, and Drumwright readily admits that suction will probably prove to be a good option for some specific tasks. But chopstick manipulation, if they can get it to work, will be a lot more versatile.

Photo: Dextrous Robotics

Dextrous Robotics co-founders Evan Drumwright and Sam Zapolsky.

I think there's a reason that nature has given us hands. Nature knows how to design suction devices—bats have it, octopi have it, frogs have it—and yet we have hands. Why? Hands are a superior instrument. And so, that's why we've gone down this road. I personally believe, based on billions of years of evolution, that there's a reason that manipulation is superior and that that technology is going to win out. —Evan Drumwright

Part of Dextrous’ secret sauce is an emphasis on simulation. Hardware is hard, so ideally, you want to make one thing that just works the first time, rather than having to iterate over and over. Getting it perfect on the first try is probably unrealistic, but the better you can simulate things in advance, the closer you can get. “What we’ve been able to do is set up our entire planning perception and control system so that it looks exactly like it does when that code runs on the real robot,” says Drumwright. “When we run something on the simulated robot, it agrees with reality about 95 percent of the time, which is frankly unprecedented.” Using very high fidelity hardware modeling, a real time simulator, and software that can directly transfer between sim and real, Dextrous is able to confidently model how their system performs even on notoriously tricky things to simulate, like contact and stiction. The idea is that the end result will be a system that can be developed faster while performing more complex tasks better than other solutions.

We were also wondering why this system uses smooth round chopsticks rather than something a little bit grippier, like chopsticks with a square cross section, and maybe with some higher friction something on the inside surface. Drumwright explains that the advantage of the current design is that it’s symmetrical around its rotational axis, meaning that you only need five degrees of freedom to fully control it. “What that means practically is that things can get a whole lot simpler—the control algorithms get simpler, the inverse kinematics algorithms get simpler, and importantly the number of motors that we need to drive in the robot goes down.”

Screenshot: Dextrous Robotics

Simulated version of Dextrous Robotics’ hardware.

Dextrous took seed funding 18 months ago, and since then they’ve been working on both the software and hardware for their system as well as finding the time to score an NSF SBIR phase 1 grant. The above screenshot shows the simulation of the hardware they’re working towards (chopstick manipulators on two towers that can move laterally), while the Franka Panda arms are what they’re using to validate their software in the meantime. New hardware should be done imminently, and over the next year, Dextrous is looking forward to conducting paid pilots with real customers. Continue reading

Posted in Human Robots