UC Berkeley’s Salto has been one of our favorite robots since 2016, which makes it antique-ish in robot years. While it’s kept the identical “hyper-competitive pogo-stick” concept, a sequence of enhancements has endowed Salto with the capability to do more dynamic maneuvers.
The authentic Salto may want to make two jumps in a row. Thrusters introduced in 2017 gave the robot the control to collectively chain a couple of jumps. Late closing 12 months at IROS and stepped forward controller gave Salto the intelligence that it had to make pinpoint jumps that allowed it to traverse a chain of vertical limitations (and extra).
The large constraint has constantly been that for Salto to keep itself upright and in one piece, it had to soar inside a motion-capture environment, which restrained its usefulness to (allow’s be honest) now not plenty extra than a fab research mission and particularly effective YouTube video view generator.
Today at ICRA, UC Berkeley roboticists Justin Yim and Eric Wang (from Ron Fearing’s Biomimetic Millisystems Lab) supplied the state-of-the-art model of Salto, which adds the sensing and computing required to dispose of the movement-capture device. Salto can now jump as much as you want out of the lab and, in reality, completely outside.
The new onboard mindset estimation and hopping management device (known as SHOVE for SLIP Hopping Orientation and Velocity Estimator) is powerful enough for Salto to leap continuously, even on compliant surfaces like foam. It uses dead reckoning to estimate how an awful lot its role modifications with every bounce, or even after three hundred jumps over the direction of several minutes, this estimate most effective drifts by a meter or two (representing an error of much less than 1 centimeter in keeping with a jump).
However, the difficult component right now is correctly estimating Salto’s mindset, by which we count on the researchers to imply its orientation (as opposed to how “hyper-competitive” it’s far). Attitude estimate errors of about 1-degree result in foot placement locations varying by about half a meter from soar to leap. Salto can’t plan its jumps with sufficient precision to climb stairs reliably.
This is being labored on, although the researchers believe that “better precision estimation and management can management on more finely various surfaces like stairs, furniture, or other outcroppings” in addition to “soft substrates like upholstery or natural foliage.”
The researchers tell us that Salto’s hardware is capable enough at this point that aside from probably upgrading the motor or battery for more jumping energy or run time, the focal point now can be on new behaviors, even though they’re toying with the idea of adding a few kinds of the gripping foot so that Salto can release from, and land on, tree branches (!). And as for that “pair of legs” and “palms” that Professor Ron Fearing mentions in the video, that might also be pretty an improvement; however, I’ll continually have a gentle spot for Salto as a bouncy little monopod.
