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#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
#439432 Nothing Can Keep This Drone Down
When life knocks you down, you’ve got to get back up. Ladybugs take this advice seriously in the most literal sense. If caught on their backs, the insects are able to use their tough exterior wings, called elytra (of late made famous in the game Minecraft), to self-right themselves in just a fraction of a second.
Inspired by this approach, researchers have created self-righting drones with artificial elytra. Simulations and experiments show that the artificial elytra can not only help salvage fixed-wing drones from compromising positions, but also improve the aerodynamics of the vehicles during flight. The results are described in a study published July 9 in IEEE Robotics and Automation Letters.
Charalampos Vourtsis is a doctoral assistant at the Laboratory of Intelligent Systems, Ecole Polytechnique Federale de Lausanne in Switzerland who co-created the new design. He notes that beetles, including ladybugs, have existed for tens of millions of years. “Over that time, they have developed several survival mechanisms that we found to be a source of inspiration for applications in modern robotics,” he says.
His team was particularly intrigued by beetles’ elytra, which for ladybugs are their famous black-spotted, red exterior wing. Underneath the elytra is the hind wing, the semi-transparent appendage that’s actually used for flight.
When stuck on their backs, ladybugs use their elytra to stabilize themselves, and then thrust their legs or hind wings in order to pitch over and self-right. Vourtsis’ team designed Micro Aerial Vehicles (MAVs) that use a similar technique, but with actuators to provide the self-righting force. “Similar to the insect, the artificial elytra feature degrees of freedom that allow them to reorient the vehicle if it flips over or lands upside down,” explains Vourtsis.
The researchers created and tested artificial elytra of different lengths (11, 14 and 17 centimeters) and torques to determine the most effective combination for self-righting a fixed-wing drone. While torque had little impact on performance, the length of elytra was found to be influential.
On a flat, hard surface, the shorter elytra lengths yielded mixed results. However, the longer length was associated with a perfect success rate. The longer elytra were then tested on different inclines of 10°, 20° and 30°, and at different orientations. The drones used the elytra to self-right themselves in all scenarios, except for one position at the steepest incline.
The design was also tested on seven different terrains: pavement, course sand, fine sand, rocks, shells, wood chips and grass. The drones were able to self-right with a perfect success rate across all terrains, with the exception of grass and fine sand. Vourtsis notes that the current design was made from widely available materials and a simple scale model of the beetle’s elytra—but further optimization may help the drones self-right on these more difficult terrains.
As an added bonus, the elytra were found to add non-negligible lift during flight, which offsets their weight.
Vourtsis says his team hopes to benefit from other design features of the beetles’ elytra. “We are currently investigating elytra for protecting folding wings when the drone moves on the ground among bushes, stones, and other obstacles, just like beetles do,” explains Vourtsis. “That would enable drones to fly long distances with large, unfolded wings, and safely land and locomote in a compact format in narrow spaces.” Continue reading
Posted in Human Robots