Niiyama wants a robot with the vigor and agility of a human sprinter.
To do that, he's building a legged bot that mimics our musculoskeletal system.
He calls his robot Athlete. Each leg has seven sets of artificial muscles. The sets, each with one to six pneumatic actuators, correspond to muscles in the human body -- gluteus maximus, adductor, hamstring, and so forth [see diagram below].
To simplify things a bit, the robot uses prosthetic blades, of the type that double amputees use to run.
And to add a human touch, Niiyama makes the robot wear a pair of black shorts.
Human runners with prosthetic feet, like South African paralympic runner Oscar Pistorius, nicknamed the "Blade Runner," "give me great inspiration," Niiyama tells me.
The robot has touch sensors on each foot and an inertial measurement unit on the torso for detecting the body's orientation.
They presented their project at the IEEE Humanoids 2010 conference in Nashville, Tenn., last week.
The researchers are now teaching Athlete to run. They programmed the robot to activate its artificial muscles with the same timing and pattern of a person's muscles during running.
Niiyama, who has since become a post-doc at MIT's Robot Locomotion Group in Cambridge, Mass., says they're trying to better understand how we control our muscles during a challenging task like running.
Traditional humanoid robots like Asimo run by changing the angle of their joints. Their legs are rigid, powered by motors coupled to reduction gears. In other words, they run like robots.
People, as well as animals, don't keep track of the position of their joints -- we use our viscoelastic muscles and tendons to bounce against the ground, propelling our bodies forward while maintaining balance.
Athlete can take three, sometimes five steps, moving at about 1.2 meters per second. Then it falls. Watch:
It's a short dash, but the researchers are optimistic. They plan to fine tune the artificial muscles and improve the feedback control system. And then hopefully move their tests to a real running track.
These unmanned aerial vehicles, or UAVs, have varied degrees of autonomy, though typically they depend on GPS and also on supervision from a human operator, who can send commands to the aircraft and receive images from its on-board cameras.
Now researchers at McGill University's Mobile Robotics Lab, in Montreal, Canada, are making these smart aircraft a bit smarter. They've developed a UAV control system that uses aerial images to identify visual cues on the landscape and steer the aircraft autonomously.
Aerial vehicles guided by advanced vision capabilities could help track wildfires, oil spills, and even animal herds. The aircraft would carry out monitoring and mapping missions requiring no human supervision or GPS coordinates.
Anqi Xu, a PhD student, and his advisor, Professor Gregory Dudek, director of the Mobile Robotics Lab, say that their current system is capable of following a coastline or a road surrounded by forests.
They used a fixed-wing UAV called the Unicorn from Procerus Technologies, which they can control via software. The aircraft carries a gimbal-mounted camera that streams video over a radio link. A Linux notebook computer analyzes the video feed and sends heading updates to the UAV in real time.
To track coastlines, their vision algorithm analyzes the color properties in the images to distinguish between water and land. To track a highway in a wooded region, it analyzes textures cues. Once the algorithm has identified the boundaries between different areas, it then determines a heading to follow.
To test their system, the researchers took their UAV to the beach. Watch:
The test area consisted of a 1-kilometer long "S" shaped tropical coastline. After manually aligning their UAV, their control system took over and successfully steered the aircraft along the stretch of the shore. The UAV traveled at an altitude of 150 meters with an average ground speed of 13 meters per second with lateral wind speed of 7 meters per second.
How would that performance compare to a human operator piloting the UAV using the same visual information?
The researchers asked five volunteers to watch the recorded images and specify headings to keep the UAV following the coastline. Though there were discrepancies between the headings produced by the algorithm and by the volunteers, the researchers concluded that their system can perform nearly as well as a human operator.
Dennis Hong is a Virginia Tech roboticist who has been building some really cool robots. He's also a good salesman. Watch him showing off his "new baby," DARwIn-OP, at this week's IEEE Humanoids 2010 conference in Nashville, Tenn. Designed by Hong's RoMeLa team and collaborators at University of Pennsylvania's Grasp Lab, Purdue University, and Korean company Robotis, DARwIn-OP has both its hardware and software open source. That means that in principle you can fabricate the parts, choose your own electronics and actuators, and build your own. Or maybe you'd prefer to buy one already assembled? Robotis is selling it for around U.S. $8,000. (Update: Robotis announced that it will be $12,000 MSRP and $9,600 educational discount price.)
Specs below from Robotis:
DARwIn-OP (Dynamic Anthropomorphic Robot with Intelligence-Open Platform)
* Height: 455 mm (17.9 inches)
* Weight: 2.8 kg (6.3 lbs)
* Head: USB camera (HD); status LEDs on eyes and forehead; USB mic; two microphones on sides of the head (optional)
* Torso: Speaker; 3-axis gyroscope and 3-axis accelerometer; Mini SD; WiFi; two cooling fans; two USB interfaces; HDMI; audio line-in; audio line-out; battery; external power input; power switch; Ethernet port; seven status LEDs; removable handle
* Feet: FSR X4 sensor (optional)
* Default walking speed: 24.0 cm/sec (9.5 in/sec); 0.25 sec/step (user modifiable gait)
* Default standing up time from ground: 2.8 sec (from facing down) and 3.9 sec (from facing up)
* Built-in PC: 1.6 GHz Intel Atom Z530 on-board 4 GB flash SSD
* Management controller (CM-730): ARM CortexM3 STM32F103RE 72 MHz
* 20 actuator modules: Robotis Dynamixel RX-28M (6 DOF leg x2 + 3 DOF arm x2 + 2 DOF neck)
* 1 spare actuator (for maintenance and expansion)
* Self-maintenance kit
* Standby mode for low-power consumption
* 4.5 Mbps high-speed Dynamixel bus for joint control
* Battery (30 minutes of operation), charger, and external power adapter
* Mechanical and electronics information and source code: http://sourceforge.net/projects/darwinop
This video may be NSFW if you don’t like watching raw meat get sliced up by a robot.
The reason that someone thought that giving this robot arm a razor sharp knife to stab meat with was that boning hams is a repetitive task, i.e. something that a robot would be great at. They’re probably right, and it’s an impressive technical achievement, because the robot has to be able to compensate for lots of variability in, uh, “meat form and bone size.” Using these robots, it only takes 10 people to bone 500 hams an hour instead of 20.
On the other hand, I can’t help but thing two things. First of all, this is the sort of semi-skilled labor that until very recently was not at risk for automation because of the knowledge and adaptability required. And second, we’re giving robot arms knives now. PANIC!
Kinect’s 3D sensor is so cheap and effective that it’s getting bolted onto any robot that moves, and quadrotors are just the latest victims. UC Berkeley’s quadrotor is using the Kinect for autonomous flight and dynamic obstacle avoidance, and as long as you don’t come at it from behind, it works great. The nice thing about using Kinect like this is that it translates into a SLAM system, where the robot can fly around and make a 3D map of a space using the same data that it’s relying on to keep from crashing in to stuff.
Japan’s National Agriculture and Food Research Organization has developed this excessively complicated robot that’s able to visually recognize ripe strawberries and then delicately pluck them and drop them in a basket.
The robot operates at a speed of 9 seconds per strawberry, which is probably a minimum of 9 times slower than an experienced human would be able to do it, so I’m really not sure how the designers suggest that using robots would be 60% faster. The only way I can get that type of math to work is by using an impractical number of robots, and by impractical, I mean hugely expensive. Don’t get me wrong, I think there’s a future in agricultural robots like this… But they’re going to have to find some way of overcoming cheap and efficient human labor first. This has already happened with lots of crops, but with some exceptions, fruit is significantly more difficult, because it has a ripeness factor and bruises easily.
The strawberry harvesting robot is currently being tested in the field, with a more practical production version due next year.
UT Austin’s HCR Lab just got this robot head, and its primary goal is to “elicit a sense of trust and sociability to an otherwise pure mechatronic device.” This is a moderately refreshing (and on the whole, quite advisable) approach to creating a robot… It’s very easy to focus on functionality without worrying about whether or not people are going to actually want to interact with your robot. Obviously, a lot of thought was put into Dreamer, because it’s securely in that sweet spot of humanish without trying too hard.
One of the things that I think makes this robot appear so natural is that fact that it has fast eyes that lead its head around, just like an animal or human. There’s only a minimal amount of that sluggish, mechanical servo response, and the video even mentions that the eyes are capable of moving even faster, up to “human speed.” Plus, as we’ve mentioned before, having eyelids is a really big deal.
Katharina Mülling (holding the emergency stop switch), Jan Peters, and Jens Kober monitor their ping pong robot practicing against a ball gun. All photos: Axel Griesch/MPG, München
Despite all the recent advances in robotics, one fundamental task appears to remain as hard as ever: robot programming.
To be sure, robot programming in industrial settings has evolved significantly, from a series of mechanical switches to advanced programming languages and teach-pendant devices for trajectory planning. But getting robots to do their jobs still requires a great deal of human labor -- and human intelligence.
The situation is even worse when it comes to programming robots to do things in non-industrial environments. Homes, offices, and hospitals are unstructured spaces, where robots need to deal with more uncertainty and act more safely.
To overcome this programming bottleneck, engineers need to create robots that are more flexible and adaptable -- robots that, like humans, learn by doing.
That's what a team led by Dr. Jan Peters at the Robot Learning Lab, part of the Max-Planck Institute for Biological Cybernetics, in Tübingen, Germany, is trying to do. Peters wants to transform robot programming into robot learning. In other words, he wants to design robots that can learn tasks effortlessly instead of requiring people to painstakingly determine their every move.
In the video below, you can see his students taking their robot "by the hand" to teach it motor skills needed for three tasks: paddle a ball on a string, play the ball-in-a-cup game, and hit a ping pong ball.
Here's how Dr. Peters explained to Automaton his team's approach: "Take the example of a person learning tennis. The teacher takes the student by the hand and shows basic movements: This is a forehand, this is a backhand, this is a serve. Still, it will take hours and hours of training before the student even feels comfortable at performing these behaviors. Even more practice is needed for the student to be able to play an actual game with these elementary behaviors." But still, he adds, humans succeed at learning the task. Why can't robots do the same? "That's what we're trying to do: Make our robots mimic the way humans learn new behaviors."
In the first part of the video, graduate student Katharina Muelling shows the robot how to paddle a ball on a string by performing the action while holding the robot's "hand." The robot decomposes the movement into primitive motor behaviors -- a discrete motor primitive that modulates the rhythmic paddling with an increasing amplitude until it becomes a stable rhythmic behavior -- and quickly "learns" how to perform the task.
For comparison purposes, the researchers tried to manually program the robot's motors to perform the same task. It took them three months and the result wasn't as good as the imitation learning experiment, which took less than an hour, Dr. Peters says.
In the second part of the video, Muelling teaches the robot the ball-in-a-cup game. [See photo on the right; the robot has to swing the yellow ball, which is attached to a string, and make it land into the blue cup.] This skill is significantly more difficult than paddling the ball on a string, and the robot doesn't have enough data to simply imitate what the human did. In fact, when the robot attempts to reproduce the human action, it can't match the accelerations of the human hand and the ball misses the cup by a large margin. Here, self-improvement becomes key, Dr. Peters says.
"For every new attempt, when the robot reduces the distance by which the ball misses the cup, the robot receives a 'reward,' " he says. "The robot subsequently self-improves on a trial-by-trial basis. It usually gets the ball in the cup for the first time after 40 to 45 trials and it succeeds all the time after about 90 to 95 trials."
How does the robot's learning ability compare to a human being? PhD student Jens Kober, who led this particular experiment, wanted to find out: He went home for a holiday last year and enjoyed the benefit of an extended, large family -- always good subjects for a scientific experiment. He showed his many cousins the ball-in-a-cup game and rewarded them with chocolate. It turned out that the younger ones (around 6 years old) would not learn the behavior at all, the ones in their early teens (10 to 12) would learn it within 30 to 35 trials, and the grownups would be much faster.
"His supervisor may be the only person in his lab who has not managed to learn this task," Dr. Peters quips.
In the last part of the video, the researchers tackle an ever harder task: ping pong. Again, Muelling teaches the robot by holding its "hand," this time to hit a ping pong ball sent by a ball gun [photos above]. The challenge here is to use -- and modify -- previously learned basic motions and combine them with visual stimuli: The robot needs to keep track of the ball, which may come from different directions, and then execute the right set of motions.
Some of their work, part of GeRT consortium, a program that aims at generalizing robot manipulation tasks, is still preliminary, Dr. Peters notes. But he's confident they can teach their robot to become a good ping pong player. How good? Maybe not as good as Forrest Gump, but good enough to beat everyone in the lab.
Samuel Bouchard is a co-founder of Robotiq in Quebec City.
According to the company, this massage robot uses "unique tilt sensor technology" to move slowly across a person's body "without falling off or losing its grip." As the bot roams around, its four sprocket-like rubber wheels press gently on the skin.
Founded by a bunch of Israeli electronics and defense engineers, DreamBots will show off the WheeMe at CES next January. There's no word on price yet. The company admits the robot can't give you a deep tissue massage, because it's very light (240 grams, or 8.5 ounces). But they claim the device can provide "a delightful sense of bodily pleasure."
It's unclear how big the market is for a body-rolling robot. I guess we'll have to wait and see.
We've seen robot geckos climbing walls before. Now researchers are adding a twist -- literally. If this bio-inspired bot falls, rather than crashing into pieces, it can right itself mid-air and land on its feet.
The UC Berkeley researchers, led by graduate student Ardian Jusufi, describe their results in a paper published today in a special edition of the Institute of Physics's Bioinspiration & Biomimetics. The movie shows how a gecko uses its tail to right and turn itself mid-air and fall on its feet. The researchers studied how a real gecko does the trick, modeled the maneuver on a computer, and built a robot gecko that can do the same.
"Because biologists and engineers are typically trained quite differently, there is a gap between the understanding of natural flight of biologists and the engineer's expertise in designing vehicles that function well," the special edition's editor David Lentink from Wageningen University, in the Netherlands, writes in an accompanying editorial. "In the middle however is a few pioneering engineers who are able to bridge both fields."
Other articles describe how scientists are trying to mimic the natural abilities of humming birds, cruising seagulls, flapping insects, and floating maple seeds to improve the design of air vehicles.
But the robot I really want to see? The amazing gliding snake!
See more videos below, including one from Jake Socha and his team at Virginia Tech showing the mystifying skills of flying snakes, which direct their flight mid-air by slithering.