Nav: Home

'Blind' Cheetah 3 robot can climb stairs littered with obstacles

July 05, 2018

MIT's Cheetah 3 robot 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 mechanical beast -- about the size of a full-grown Labrador -- is intentionally designed to do all this without relying on cameras or any external environmental sensors. Instead, it nimbly "feels" its way through its surroundings in a way that engineers describe as "blind locomotion," much like making one's way across a pitch-black room.

"There are many unexpected behaviors the robot should be able to handle without relying too much on vision," says the robot's designer, Sangbae Kim, associate professor of mechanical engineering at MIT. "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. So we want the robot to rely more on tactile information. That way, it can handle unexpected obstacles while moving fast."

Researchers will present the robot's vision-free capabilities in October at the International Conference on Intelligent Robots, in Madrid. In addition to blind locomotion, the team will demonstrate the robot's improved hardware, including an expanded range of motion compared to its predecessor Cheetah 2, that allows the robot to stretch backwards and forwards, and twist from side to side, much like a cat limbering up to pounce.

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.

"Cheetah 3 is designed to do versatile tasks such as power plant inspection, which involves various terrain conditions including stairs, curbs, and obstacles on the ground," Kim says. "I think there are countless occasions where we [would] want to send robots to do simple tasks instead of humans. Dangerous, dirty, and difficult work can be done much more safely through remotely controlled robots."

Making a commitment

The 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.

"When it comes to switching from the air to the ground, the switching has to be very well-done," Kim says. "This algorithm is really about, 'When is a safe time to commit my footstep?'"

The contact detection algorithm helps the robot determine the best time to transition a leg between swing and step, by constantly calculating for each leg three probabilities: the probability of a leg making contact with the ground, the probability of the force generated once the leg hits the ground, and the probability that the leg will be in midswing. The algorithm calculates these probabilities based on data from gyroscopes, accelerometers, and joint positions of the legs, which record the leg's angle and height with respect to the ground.

If, for example, the robot unexpectedly steps on a wooden block, its body will suddenly tilt, shifting the angle and height of the robot. That data will immediately feed into calculating the three probabilities for each leg, which the algorithm will combine to estimate whether each leg should commit to pushing down on the ground, or lift up and swing away in order to keep its balance -- all while the robot is virtually blind.

"If humans close our eyes and make a step, we have a mental model for where the ground might be, and can prepare for it. But we also rely on the feel of touch of the ground," Kim says. "We are sort of doing the same thing by combining multiple [sources of] information to determine the transition time."

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.

"It doesn't know the height of each step, and doesn't know there are obstacles on the stairs, but it just plows through without losing its balance," Kim says. "Without that algorithm, the robot was very unstable and fell easily."

Future force

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 contact detection algorithm will tell you, 'this is the time to apply forces on the ground,'" Kim says. "But once you're on the ground, now you need to calculate what kind of forces to apply so you can move the body in the right way."

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.

"Say someone kicks the robot sideways," Kim says. "When the foot is already on the ground, the algorithm decides, 'How should I specify the forces on the foot? Because I have an undesirable velocity on the left, so I want to apply a force in the opposite direction to kill that velocity. If I apply 100 newtons in this opposite direction, what will happen a half second later?"

The algorithm is designed to make these calculations for each leg every 50 milliseconds, or 20 times per second. In experiments, researchers introduced unexpected forces by kicking and shoving the robot as it trotted on a treadmill, and yanking it by the leash as it climbed up an obstacle-laden staircase. They found that the model-predictive algorithm enabled the robot to quickly produce counter-forces to regain its balance and keep moving forward, without tipping too far in the opposite direction.

"It's thanks to that predictive control that can apply the right forces on the ground, combined with this contact transition algorithm that makes each contact very quick and secure," Kim says.

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."
-end-
This research was supported, in part, by Naver, Toyota Research Institute, Foxconn, and Air Force Office of Scientific Research.

Additional background

ARCHIVE: Cheetah III robot preps for a role as a first responder https://engineering.mit.edu/

ARCHIVE: Taking a leap in bioinspired robotics http://news.mit.edu/2016/faculty-profile-sangbae-kim-1216

ARCHIVE: MIT cheetah robot lands the running jump http://news.mit.edu/2015/cheetah-robot-lands-running-jump-0529

ARCHIVE: Bound for robotic glory http://news.mit.edu/2014/mit-cheetah-robot-runs-jumps-0915

ARCHIVE: MIT 'cheetah' robot rivals running animals in efficiency http://news.mit.edu/2014/mit-cheetah-robot-runs-jumps-0915

Massachusetts Institute of Technology

Related Algorithm Articles:

Scientists use algorithm to peer through opaque brains
A new algorithm helps scientists record the activity of individual neurons within a volume of brain tissue.
Algorithm generates origami folding patterns for any shape
A new algorithm generates practical paper-folding patterns to produce any 3-D structure.
New algorithm tracks neurons in bendy brain of freely crawling worm
Scientists at Princeton University have developed a new algorithm to track neurons in the brain of the worm Caenorhabditis elegans while it crawls.
Does my algorithm work? There's no shortcut for community detection
Community detection is an important tool for scientists studying networks, but a new paper published in Science Advances calls into question the common practice of using metadata for ground truth validation.
'Cyclops' algorithm spots daily rhythms in cells
Humans, like virtually all other complex organisms on Earth, have adapted to their planet's 24-hour cycle of sunlight and darkness.
An algorithm that knows when you'll get bored with your favorite mobile game
Researchers from the Tokyo-based company Silicon Studio, led by Spanish data scientist África Periáñez, have developed a new algorithm that predicts when a user will leave a mobile game.
Algorithm identified Trump as 'not-married'
Scientists from Russia and Singapore created an algorithm that predicts user marital status with 86% precision using data from three social networks instead of one.
A novel positioning algorithm based on self-adaptive algorithm
Much attention has been paid to the Taylor series expansion (TSE) method these years, which has been extensively used for solving nonlinear equations for its good robustness and accuracy of positioning.
Algorithm can create a bridge between Clinton and Trump supporters
The article that received the best student-paper award in the Tenth International Conference on Web Search and Data Mining (WSDM 2017) builds algorithmic techniques to mitigate the rising polarization by connecting people with opposing views -- and evaluates them on Twitter.
Deep learning algorithm does as well as dermatologists in identifying skin cancer
In hopes of creating better access to medical care, Stanford researchers have trained an algorithm to diagnose skin cancer.

Related Algorithm Reading:

Best Science Podcasts 2019

We have hand picked the best science podcasts for 2019. Sit back and enjoy new science podcasts updated daily from your favorite science news services and scientists.
Now Playing: TED Radio Hour

Changing The World
What does it take to change the world for the better? This hour, TED speakers explore ideas on activism—what motivates it, why it matters, and how each of us can make a difference. Guests include civil rights activist Ruby Sales, labor leader and civil rights activist Dolores Huerta, author Jeremy Heimans, "craftivist" Sarah Corbett, and designer and futurist Angela Oguntala.
Now Playing: Science for the People

#521 The Curious Life of Krill
Krill may be one of the most abundant forms of life on our planet... but it turns out we don't know that much about them. For a create that underpins a massive ocean ecosystem and lives in our oceans in massive numbers, they're surprisingly difficult to study. We sit down and shine some light on these underappreciated crustaceans with Stephen Nicol, Adjunct Professor at the University of Tasmania, Scientific Advisor to the Association of Responsible Krill Harvesting Companies, and author of the book "The Curious Life of Krill: A Conservation Story from the Bottom of the World".