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.
This is just the first taste of what a hacked-up Kinect sensor is capable of… That motion capture and teleoperation system looks pretty sweet, and as Willow Garage says, they’ve basically just started messing with the capabilities of the sensor, and things are already progressing very quickly.
Kinect is $150, and the open source drivers are free. Go crazy.
If you can’t wait for a hacked Neato LIDAR system and you need some cheap localization and mapping hardware, you might want to take a good look at Microsoft’s Kinect system, which has already been hacked open and made available to anyone using ROS.
MIT’s Personal Robotics Group has put together the demo in the vidbelow , which shows an iRobot Create plus a Kinect sensor performing 3D SLAM (simultaneous localization and mapping) and also reacting to gesture inputs from a human, which is pretty cool. Most of the heavy lifting is done by an offboard computer, but there’s no reason that the whole system couldn’t be easily integrated into the robot itself, since I think I remember hearing that Kinect is minimally intensive when it comes to processing requirements.
This kind of thing is really, really fantastic because we’re starting to see high quality sensing systems that provide awesome data being available for what’s basically dirt cheap. Remember those DARPA Grand Challenge cars and their hundreds of thousands of dollars of ranging sensors? It was only a few years ago that 3D sensing hardware was totally, completely out of range for hobby robotics, and now, in the space of like 6 months, we’ve actually got options. Yeah, it’s piggybacking off of other tech, but there’s nothing wrong with that, and it’s only going to get better as the gaming and automotive industry invest more resources in making their machines smarter, not just faster.
Obama pets Paro the robot seal, created by Dr. Takanori Shibata [right] from AIST.
Then it was time for a ride on what appears to be the latest version of Toyota's i-REAL personal mobility vehicle. Well, it wasn't much of a ride. Obama drove an inch forward, but when the machine suddenly tilted back the president almost jumped out of it. "That's what we're going to be driving," he quipped.
Obama sits on Toyota's i-REAL, a futuristic personal mobility vehicle.
Here's a longer video showing various heads of state -- Medvedev of Russia, Hu Jintao of China, Chile's Sebastián Piñera (famous after the trapped miners incident), among others -- interacting with the Japanese technologies:
More news about Geminoid F, the ultrarealistic android unveiled early this year: the robot got a job.
Geminoid F is working as an actress, taking the stage in a play that opened yesterday in a Tokyo theater.
In the 20-minute play, titled "Sayonara" ("good bye" in Japanese), the android shares the stage with another actress (of the human kind) named Bryerly Long. Long plays the role of a young woman who is suffering from a fatal illness and whose parents bring her an android to serve as a companion.
A human operator controls the robot from a soundproof chamber behind the stage. A microphone captures the operator's voice and cameras track head and face movements. When the operator speaks or moves, the android follows suit.
The robot is in a permanent sitting posture, so movements are limited to the head, torso, and arms. The performance is "a bit mechanical," as Reuters puts it, but that doesn't seem to be a problem: the android is playing the role of an android after all.
The "Android-Human Theater" project is a collaboration between Ishiguro and Japanese director Oriza Hirata, who writes and directs.
According to Ishiguro, the play explores the question, "What do life and death mean to humans and robots?," and it will "alter the audience's images of robots and humans, and present a compelling fusion of theater arts and science."
Kevin Warwick is most certainly the preeminent cyborg of our time. More than a decade ago he implanted an RFID chip in himself to control simple functions like turning on the lights, and it's been 8 years since he inserted a more elaborate, 100-electrode array into the nerves in his forearm that allowed him to manipulate a robotic arm on another continent. He's assisted students at the University of Reading, in England, who wished to implant magnets in the tips of their fingers and at least one who wished for an electrode in the tongue (with the help, Warwick says, of a Manchester tattoo artist who goes by the name "Dr. Evil").
More recently, he's been growing rat neurons on a 128-electrode array and using them to control a simple robot consisting of two wheels with a sonar sensor. The rudimentary little toy has no microprocessor of its own -- it depends entirely on a rat embryo's brain cells. The interesting question is just how big one of these neuron-electrode hybrid brains can grow, and those brain cell networks are now getting more complicated, and more legitimately mammalian, Warwick said this week in a keynote speech at the IEEE Biomedical Circuits and Systems conference. Warwick's twist predates the living rat-controlled robot we wrote about recently, and it just goes to show that weird cyborg animal projects have virtually unlimited potential.
To start off a rat brain robot, embryonic neurons are separated out and allowed to grow on an electrode array. Within minutes the neurons start to push out tentacles and link up to each other, becoming interconnected dendrites and axons. A dense mesh of about 100,000 neurons can grow within several days. After about a week, Warwick and his collaborators can start to pulse the electrodes under the neural mesh in search of a pathway -- that is, when neurons near an active electrode fire, another group of neurons on a different side of the array shows an inclination to fire as well.
Once they have a pathway -- the groups fire in tandem at least a third of the time -- the University of Reading researchers can use that connection to get the robot to roam around and learn to avoid crashing into walls. They connect the electrode array to the robot using Bluetooth. When the sonar senses it's nearing a wall, it stimulates the electrode at one end of the neural pathway, and at first the brain sends back a coherent response only every once in awhile. The robot interprets the response as an instruction to turn its wheels. With time and repetition, the neural pathways become stronger, and the robot runs into the walls less frequently. In effect, the robot works out for itself how to not bash into obstacles.
To add complexity to the experiments, Warwick's lab is now collaborating with a Canadian group to culture neurons in three dimensions, meaning they are attempting to grow a network of 30 million neurons -- a big step towards the 100 billion found in a human brain. After that, the next step will be to bring in human neurons. "If we have 100 billion human neurons," Warwick says, "should we give it rights? Does it get to vote?" More to the point, he wonders: "Is it conscious?"