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Yesterday, the Toyota Research Institute (TRI) showed off some of the projects that it’s been working on recently, including a ceiling-mounted robot that could one day help us with household chores. That system is just one example of how TRI envisions the future of robotics and artificial intelligence. As TRI CEO Gill Pratt told us, the company is focusing on robotics and AI technology for “amplifying, rather than replacing, human beings.” In other words, Toyota wants to develop robots not for convenience or to do our jobs for us, but rather to allow people to continue to live and work independently even as we age.
To better understand Toyota’s vision of robotics 15 to 20 years from now, it’s worth watching the 20-minute video below, which depicts various scenarios “where the application of robotic capabilities is enabling members of an aging society to live full and independent lives in spite of the challenges that getting older brings.” It’s a long video, but it helps explains TRI’s perspective on how robots will collaborate with humans in our daily lives over the next couple of decades.
Those are some interesting conceptual telepresence-controlled bipeds they’ve got running around in that video, right?
For more details, we sent TRI some questions on how it plans to go from concepts like the ones shown in the video to real products that can be deployed in human environments. Below are answers from TRI CEO Gill Pratt, who is also chief scientist for Toyota Motor Corp.; Steffi Paepcke, senior UX designer at TRI; and Max Bajracharya, VP of robotics at TRI.
IEEE Spectrum: TRI seems to have a more explicit focus on eventual commercialization than most of the robotics research that we cover. At what point TRI starts to think about things like reliability and cost?
Toyota is exploring robots capable of manipulating dishes in a sink and a dishwasher, performing experiments and simulations to make sure that the robots can handle a wide range of conditions.
Gill Pratt: It’s a really interesting question, because the normal way to think about this would be to say, well, both reliability and cost are product development tasks. But actually, we need to think about it at the earliest possible stage with research as well. The hardware that we use in the laboratory for doing experiments, we don’t worry about cost there, or not nearly as much as you’d worry about for a product. However, in terms of what research we do, we very much have to think about, is it possible (if the research is successful) for it to end up in a product that has a reasonable cost. Because if a customer can’t afford what we come up with, maybe it has some academic value but it’s not actually going to make a difference in their quality of life in the real world. So we think about cost very much from the beginning.
The same is true with reliability. Right now, we’re working very hard to make our control techniques robust to wide variations in the environment. For instance, in work that Russ Tedrake is doing with manipulating dishes in a sink and a dishwasher, both in physical testing and in simulation, we’re doing thousands and now millions of different experiments to make sure that we can handle the edge cases and it works over a very wide range of conditions.
A tremendous amount of work that we do is trying to bring robotics out of the age of doing demonstrations. There’s been a history of robotics where for some time, things have not been reliable, so we’d catch the robot succeeding just once and then show that video to the world, and people would get the mis-impression that it worked all of the time. Some researchers have been very good about showing the blooper reel too, to show that some of the time, robots don’t work.
“A tremendous amount of work that we do is trying to bring robotics out of the age of doing demonstrations. There’s been a history of robotics where for some time, things have not been reliable, so we’d catch the robot succeeding just once and then show that video to the world, and people would get the mis-impression that it worked all of the time.”
—Gill Pratt, TRI
In the spirit of sharing things that didn’t work, can you tell us a bit about some of the robots that TRI has had under development that didn’t make it into the demo yesterday because they were abandoned along the way?
Steffi Paepcke: We’re really looking at how we can connect people; it can be hard to stay in touch and see our loved ones as much as we would like to. There have been a few prototypes that we’ve worked on that had to be put on the shelf, at least for the time being. We were exploring how to use light so that people could be ambiently aware of one another across distances. I was very excited about that—the internal name was “glowing orb.” For a variety of reasons, it didn’t work out, but it was really fascinating to investigate different modalities for keeping in touch.
Another prototype we worked on—we found through our research that grocery shopping is obviously an important part of life, and for a lot of older adults, it’s not necessarily the right answer to always have groceries delivered. Getting up and getting out of the house keeps you physically active, and a lot of people prefer to continue doing it themselves. But it can be challenging, especially if you’re purchasing heavy items that you need to transport. We had a prototype that assisted with grocery shopping, but when we pivoted our focus to Japan, we found that the inside of a Japanese home really needs to stay inside, and the outside needs to stay outside, so a robot that traverses both domains is probably not the right fit for a Japanese audience, and those were some really valuable lessons for us.
Toyota recently demonstrated a gantry robot that would hang from the ceiling to perform tasks like wiping surfaces and clearing clutter.
I love that TRI is exploring things like the gantry robot both in terms of near-term research and as part of its long-term vision, but is a robot like this actually worth pursuing? Or more generally, what’s the right way to compromise between making an environment robot friendly, and asking humans to make changes to their homes?
Max Bajracharya: We think a lot about the problems that we’re trying to address in a holistic way. We don’t want to just give people a robot, and assume that they’re not going to change anything about their lifestyle. We have a lot of evidence from people who use automated vacuum cleaners that people will adapt to the tools you give them, and they’ll change their lifestyle. So we want to think about what is that trade between changing the environment, and giving people robotic assistance and tools.
We certainly think that there are ways to make the gantry system plausible. The one you saw today is obviously a prototype and does require significant infrastructure. If we’re going to retrofit a home, that isn’t going to be the way to do it. But we still feel like we’re very much in the prototype phase, where we’re trying to understand whether this is worth it to be able to bypass navigation challenges, and coming up with the pros and cons of the gantry system. We’re evaluating whether we think this is the right approach to solving the problem.
To what extent do you think humans should be either directly or indirectly in the loop with home and service robots?
Bajracharya: Our goal is to amplify people, so achieving this is going to require robots to be in a loop with people in some form. One thing we have learned is that using people in a slow loop with robots, such as teaching them or helping them when they make mistakes, gives a robot an important advantage over one that has to do everything perfectly 100 percent of the time. In unstructured human environments, robots are going to encounter corner cases, and are going to need to learn to adapt. People will likely play an important role in helping the robots learn. Continue reading
Reaching for a nearby object seems like a mindless task, but the action requires a sophisticated neural network that took humans millions of years to evolve. Now, robots are acquiring that same ability using artificial neural networks. In a recent study, a robotic hand “learns” to pick up objects of different shapes and hardness using three different grasping motions.
The key to this development is something called a spiking neuron. Like real neurons in the brain, artificial neurons in a spiking neural network (SNN) fire together to encode and process temporal information. Researchers study SNNs because this approach may yield insights into how biological neural networks function, including our own.
“The programming of humanoid or bio-inspired robots is complex,” says Juan Camilo Vasquez Tieck, a research scientist at FZI Forschungszentrum Informatik in Karlsruhe, Germany. “And classical robotics programming methods are not always suitable to take advantage of their capabilities.”
Conventional robotic systems must perform extensive calculations, Tieck says, to track trajectories and grasp objects. But a robotic system like Tieck’s, which relies on a SNN, first trains its neural net to better model system and object motions. After which it grasps items more autonomously—by adapting to the motion in real-time.
The new robotic system by Tieck and his colleagues uses an existing robotic hand, called a Schunk SVH 5-finger hand, which has the same number of fingers and joints as a human hand.
The researchers incorporated a SNN into their system, which is divided into several sub-networks. One sub-network controls each finger individually, either flexing or extending the finger. Another concerns each type of grasping movement, for example whether the robotic hand will need to do a pinching, spherical or cylindrical movement.
For each finger, a neural circuit detects contact with an object using the currents of the motors and the velocity of the joints. When contact with an object is detected, a controller is activated to regulate how much force the finger exerts.
“This way, the movements of generic grasping motions are adapted to objects with different shapes, stiffness and sizes,” says Tieck. The system can also adapt its grasping motion quickly if the object moves or deforms.
The robotic grasping system is described in a study published October 24 in IEEE Robotics and Automation Letters. The researchers’ robotic hand used its three different grasping motions on objects without knowing their properties. Target objects included a plastic bottle, a soft ball, a tennis ball, a sponge, a rubber duck, different balloons, a pen, and a tissue pack. The researchers found, for one, that pinching motions required more precision than cylindrical or spherical grasping motions.
“For this approach, the next step is to incorporate visual information from event-based cameras and integrate arm motion with SNNs,” says Tieck. “Additionally, we would like to extend the hand with haptic sensors.”
The long-term goal, he says, is to develop “a system that can perform grasping similar to humans, without intensive planning for contact points or intense stability analysis, and [that is] able to adapt to different objects using visual and haptic feedback.” Continue reading