MIT’s Cheetah 3 robot — an upgrade to the Cheetah 2, can now leap and gallop across rough terrain, climb a staircase littered with debris, and quickly recover its balance when suddenly yanked or shoved — all while essentially blind.
The 90-pound robot is intentionally designed to do all this without relying on cameras or any external environmental sensors. The idea is to allow it to “feel” its way through its surroundings via “blind locomotion,” (like making your way across a pitch-black room), eliminating visual distractions, which would slow the robot down.
“Vision can be noisy, slightly inaccurate, and sometimes not available, and if you rely too much on vision, your robot has to be very accurate in position and eventually will be slow, said the robot’s designer, Sangbae Kim, associate professor of mechanical engineering at MIT. “So we want the robot to rely more on tactile information. That way, it can handle unexpected obstacles while moving fast.”
Faster, more nimble, more cat-like
Cheetah3 has an expanded range of motion compared to its predecessor Cheetah 2, which allows the robot to stretch backwards and forwards, and twist from side to side, much like a cat limbering up to pounce. Cheetah 3 can blindly make its way up staircases and through unstructured terrain, and can quickly recover its balance in the face of unexpected forces, thanks to two new algorithms developed by Kim’s team: a contact detection algorithm, and a model-predictive control algorithm.
The contact detection algorithm helps the robot determine the best time for a given leg to switch from swinging in the air to stepping on the ground. For example, if the robot steps on a light twig versus a hard, heavy rock, how it reacts — and whether it continues to carry through with a step, or pulls back and swings its leg instead — can make or break its balance.
The researchers tested the algorithm in experiments with the Cheetah 3 trotting on a laboratory treadmill and climbing on a staircase. Both surfaces were littered with random objects such as wooden blocks and rolls of tape.
The robot’s blind locomotion was also partly due to the model-predictive control algorithm, which predicts how much force a given leg should apply once it has committed to a step. The model-predictive control algorithm calculates the multiplicative positions of the robot’s body and legs a half-second into the future, if a certain force is applied by any given leg as it makes contact with the ground.
Cameras to be activated later
The team had already added cameras to the robot to give it visual feedback of its surroundings. This will help in mapping the general environment, and will give the robot a visual heads-up on larger obstacles such as doors and walls. But for now, the team is working to further improve the robot’s blind locomotion
“We want a very good controller without vision first,” Kim says. “And when we do add vision, even if it might give you the wrong information, the leg should be able to handle [obstacles]. Because what if it steps on something that a camera can’t see? What will it do? That’s where blind locomotion can help. We don’t want to trust our vision too much.”
Within the next few years, Kim envisions the robot carrying out tasks that would otherwise be too dangerous or inaccessible for humans to take on.
This research was supported, in part, by Naver, Toyota Research Institute, Foxconn, and Air Force Office of Scientific Research.