Watch a Shape-Shifting Robot Prowl the Big, Bad World

Courtesy of University of Oslo

That morphology is quadrupedal, so DyRET moves like a dog or cat. Really, the robot is more or less just four legs with a handle on top for the researchers to grab. The robot’s legs can extend up to 6 inches total, but in two places: at the “femur” above the knee and the “tibia” below it. This gives the machine the capability of setting sections of its legs at different lengths. For example, it can telescope its limbs to have longer femurs and shorter tibias, or vice versa. The researchers could tweak these configurations, set DyRET loose on each terrain, and calculate how efficient each one was.

More specifically, they were looking at “cost of transport” as an efficiency measurement, the same metric that biologists use when looking at animal movement. Basically, it’s how much energy a creature or robot expends to convey itself, and how fast it moves. Stability while walking is inherently coded into that, which is of course important for an expensive robot like DyRET. “The more energy you expend not moving forward is energy typically spent being unstable,” says Nygaard. “So the less energy you spend moving forward, the more stable you inherently are.”

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The researchers measured this energy expenditure in the motors in the robot’s joints and also used cameras to monitor its movement. The robot also had its own depth-sensing camera, which it used to characterize the roughness of a surface; for example, to observe that concrete is much smoother than gravel. The machine could even dip its toes in the water, so to speak: Force sensors on its feet gave it information about how much softer the sand was than concrete. Together, the camera and force sensors gave DyRET a complex picture of what it was walking on and how efficiently it was doing so.

Courtesy of University of Oslo

The researchers found that when walking across concrete, the shape-shifting robot was most efficient when it had longer legs. In sand, it moved efficiently with any femur length, as long as the tibia were short. On gravel, DyRET also excelled with shorter limbs overall, which makes sense: A lower center of gravity would give the robot better stability as it clambers over tiny rocks. Generally speaking, shorter legs allow the robot to apply more force to get a grip in looser material, while longer legs increase speed for walking across smoother material. (Above, you can see the robot lower itself when it detects that it’s transitioning from concrete to gravel.)

All this training gave the robot prior knowledge of how best to configure its limbs for a particular surface. So when the researchers then took DyRET outside onto novel terrain, the robot could eyeball the ground with its camera and sense the give beneath its feet with the force sensors. Comparing this data with previous information about how concrete looks and feels, the robot then knew how to walk across a road—it made its legs longer overall for longer, more efficient strides. It didn’t need to worry about shortening its legs to lower its center of gravity, as it would when dealing with gravel, because it could see and feel that the surface was smooth and stable.

Courtesy of University of Oslo

DyRET could even tackle grass, a dramatically different surface than anything it had traipsed across in the lab. Its performance was iffy, at first. “It didn’t really know what to do,” says Nygaard. “But then quite quick, it was able to sort of learn which body shapes perform better, and therefore adapt to this new environment as well.”

This isn’t a typical way to get a robot to learn to walk. As machine learning techniques have gotten more sophisticated over the past decade or so, roboticists have instead been training machines in simulation. That is, you train the software that controls the robot in a virtual world, where the simulated robot can make thousands of walking attempts, learning by trial and error. The system penalizes mistakes and rewards successful maneuvers until the virtual robot learns optimal behaviors, a technique known as reinforcement learning. Roboticists can then port that knowledge into the robot in the real world, and voilà, a walking machine.

Except—not so voilà. This technique suffers from the “sim-to-real” problem: There’s just no way to perfectly simulate the complexities of the physical world in a virtual one, so the knowledge gained through simulation isn’t always square with the real world. That means the actual robot can wind up with a fuzzy understanding of its surroundings. Think of how well you’d get along if you woke up tomorrow and suddenly friction doesn’t work like you expect.

What these researchers have done with DyRET, by contrast, is simply to train the robot in the real world. That comes with its own challenges, of course: The shape-shifting machine learns much slower and could potentially get hurt. But the robot is also better equipped to deal with the absolute chaos of real surfaces and forces. “Differences in the terrain, and so on—like the roughness—these things are much harder to simulate than say, the high level of how you should walk, like trajectory,” says University of Oslo computer scientist Kyrre Glette, coauthor on the new paper.