You may have seen some really impressive demonstrations from bipedal robots in recent months but have seen the leaps and bounds made in the world of robotic bikes? The following videos were published by Robotics and AI Institute (RAI) showing what their two-wheeled Ultra Mobile Vehicle (UMV) is capable of:
“If you want to think about just a jumping bicycle, that’s the best way to understand how Ultra Mobile Vehicle (UMV) works. It works in much the same way a human would jumping on a bicycle — where a human would use the mass of their upper body and their legs to get their center of mass moving upwards, and then bring the bike with them, allowing them to jump up a curb or jump up onto a rock or something.” -Gabe Nelson
While videos of Scottish trials cyclist Danny MacAskill are still more impressive in our book, what the UMV is capable of is nothing short of incredible.
“What an athlete can do when competing in mountain bike trials is astounding. Competitors are able to balance, bounce, and jump across obstacles, affordances, and terrains in ways that make it appear almost easy. This performance takes an impressive combination of biking skills and athleticism but also requires extraordinary human perception and intelligent planning. The courses these athletes are able to maneuver and navigate through are too tough for any standard vehicle to traverse.
While AI has mastered language and vision, directing robots to move swiftly and effortlessly through the world has not been solved. At the RAI Institute, one of our main research projects seeks to capture those athletic and intelligence skills and pair them with remarkable rough-terrain mobility, efficiency, and navigation – creating a robot that essentially thinks and moves like a world-class athlete. The challenge embraces every element of the robot’s construction, including the mechanical design, onboard power, control systems, software, perception, and intelligence.” -Robotics and AI Institute (RAI)
Interestingly the UMV lacks a dedicated stabilization system, rather balance is maintained by controlling its speed and steering paired machine learning. The super bouncy bunnyhops are generated through an articulated arm that can quickly extend upwards. The robot measures about 80 cm tall but it can extend to over 152 cm when jumping. Here’s what the UMV team is working on for the future:
“Going forward, the UMV team will be working on enhancing the design of the robot for even better performance, and will be combining tricks and skills into complex action sequences. Long-term, the team will integrate flexible, high-performance perception that will give the system both situational and terrain awareness, including identifying obstacles and affordances in the world. This research will enable cognitive intelligence that allows UMV to understand its own physical capabilities (i.e. its athletic intelligence), understand the terrain in front of it, and how it can negotiate its way freely in the wild.” -Robotics and AI Institute (RAI)
