This 5-fingered robot hand learns to get a grip on its own

A University of Washington research team has custom built one of the most highly capable five-fingered robot hands in the world. They also developed an accurate simulation model that enables a computer to analyze movements in real time, and in their latest demonstration (beginning at 1:47), apply the model to the hardware and real-world tasks like rotating an elongated object. With each attempt, the robot hand gets progressively more adept at spinning the tube, thanks to machine learning algorithms that help it model both the basic physics involved and plan which actions it should take to achieve the desired result. Credit: Vikash Kumar, University of Washington

Intricate tasks that require dexterous in-hand manipulation — rolling, pivoting, bending, sensing friction and other things humans do effortlessly with our hands — have proved notoriously difficult for robots.

Now, a University of Washington team of computer science and engineering researchers has built a robot hand that can not only perform dexterous manipulation but also learn from its own experience without needing humans to direct it. Their latest results are detailed in a paper to be presented May 17 at the IEEE International Conference on Robotics and Automation.

“Hand manipulation is one of the hardest problems that roboticists have to solve,” said lead author Vikash Kumar, a UW doctoral student in computer science and engineering. “A lot of robots today have pretty capable arms but the hand is as simple as a suction cup or maybe a claw or a gripper.”

By contrast, the UW research team spent years custom building one of the most highly capable five-fingered robot hands in the world. Then they developed an accurate simulation model that enables a computer to analyze movements in real time. In their latest demonstration, they apply the model to the hardware and real-world tasks like rotating an elongated object.

With each attempt, the robot hand gets progressively more adept at spinning the tube, thanks to machine learning algorithms that help it model both the basic physics involved and plan which actions it should take to achieve the desired result.

This autonomous learning approach developed by the UW Movement Control Laboratory contrasts with robotics demonstrations that require people to program each individual movement of the robot's hand in order to complete a single task.

“Usually people look at a motion and try to determine what exactly needs to happen –the pinky needs to move that way, so we'll put some rules in and try it and if something doesn't work, oh the middle finger moved too much and the pen tilted, so we'll try another rule,” said senior author and lab director Emo Todorov, UW associate professor of computer science and engineering and of applied mathematics.

“It's almost like making an animated film — it looks real but there was an army of animators tweaking it,” Todorov said. “What we are using is a universal approach that enables the robot to learn from its own movements and requires no tweaking from us.”

Building a dexterous, five-fingered robot hand poses challenges, both in design and control. The first involved building a mechanical hand with enough speed, strength responsiveness and flexibility to mimic basic behaviors of a human hand.

The UW's dexterous robot hand — which the team built at a cost of roughly $300,000 — uses a Shadow Hand skeleton actuated with a custom pneumatic system and can move faster than a human hand. It is too expensive for routine commercial or industrial use, but it allows the researchers to push core technologies and test innovative control strategies.

“There are a lot of chaotic things going on and collisions happening when you touch an object with different fingers, which is difficult for control algorithms to deal with,” said co-author Sergey Levine, UW assistant professor of computer science and engineering who worked on the project as a postdoctoral fellow at University of California, Berkeley. “The approach we took was quite different from a traditional controls approach.”

The team first developed algorithms that allowed a computer to model highly complex five-fingered behaviors and plan movements to achieve different outcomes — like typing on a keyboard or dropping and catching a stick — in simulation.

Most recently, the research team has transferred the models to work on the actual five-fingered hand hardware, which never proves to be exactly the same as a simulated scenario. As the robot hand performs different tasks, the system collects data from various sensors and motion capture cameras and employs machine learning algorithms to continually refine and develop more realistic models.

“It's like sitting through a lesson, going home and doing your homework to understand things better and then coming back to school a little more intelligent the next day,” said Kumar.

So far, the team has demonstrated local learning with the hardware system — which means the hand can continue to improve at a discrete task that involves manipulating the same object in roughly the same way. Next steps include beginning to demonstrate global learning — which means the hand could figure out how to manipulate an unfamiliar object or a new scenario it hasn't encountered before.

###

The research was funded by the National Science Foundation and the National Institutes of Health.

For more information, contact Kumar at vikash@cs.washington.edu.

Media Contact

Jennifer Langston
jlangst@uw.edu
206-543-2580

 @UW

http://www.washington.edu/news/ 

Media Contact

Jennifer Langston EurekAlert!

All latest news from the category: Power and Electrical Engineering

This topic covers issues related to energy generation, conversion, transportation and consumption and how the industry is addressing the challenge of energy efficiency in general.

innovations-report provides in-depth and informative reports and articles on subjects ranging from wind energy, fuel cell technology, solar energy, geothermal energy, petroleum, gas, nuclear engineering, alternative energy and energy efficiency to fusion, hydrogen and superconductor technologies.

Back to home

Comments (0)

Write a comment

Newest articles

Lighting up the future

New multidisciplinary research from the University of St Andrews could lead to more efficient televisions, computer screens and lighting. Researchers at the Organic Semiconductor Centre in the School of Physics and…

Researchers crack sugarcane’s complex genetic code

Sweet success: Scientists created a highly accurate reference genome for one of the most important modern crops and found a rare example of how genes confer disease resistance in plants….

Evolution of the most powerful ocean current on Earth

The Antarctic Circumpolar Current plays an important part in global overturning circulation, the exchange of heat and CO2 between the ocean and atmosphere, and the stability of Antarctica’s ice sheets….

Partners & Sponsors