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#430761 How Robots Are Getting Better at Making ...
The multiverse of science fiction is populated by robots that are indistinguishable from humans. They are usually smarter, faster, and stronger than us. They seem capable of doing any job imaginable, from piloting a starship and battling alien invaders to taking out the trash and cooking a gourmet meal.
The reality, of course, is far from fantasy. Aside from industrial settings, robots have yet to meet The Jetsons. The robots the public are exposed to seem little more than over-sized plastic toys, pre-programmed to perform a set of tasks without the ability to interact meaningfully with their environment or their creators.
To paraphrase PayPal co-founder and tech entrepreneur Peter Thiel, we wanted cool robots, instead we got 140 characters and Flippy the burger bot. But scientists are making progress to empower robots with the ability to see and respond to their surroundings just like humans.
Some of the latest developments in that arena were presented this month at the annual Robotics: Science and Systems Conference in Cambridge, Massachusetts. The papers drilled down into topics that ranged from how to make robots more conversational and help them understand language ambiguities to helping them see and navigate through complex spaces.
Improved Vision
Ben Burchfiel, a graduate student at Duke University, and his thesis advisor George Konidaris, an assistant professor of computer science at Brown University, developed an algorithm to enable machines to see the world more like humans.
In the paper, Burchfiel and Konidaris demonstrate how they can teach robots to identify and possibly manipulate three-dimensional objects even when they might be obscured or sitting in unfamiliar positions, such as a teapot that has been tipped over.
The researchers trained their algorithm by feeding it 3D scans of about 4,000 common household items such as beds, chairs, tables, and even toilets. They then tested its ability to identify about 900 new 3D objects just from a bird’s eye view. The algorithm made the right guess 75 percent of the time versus a success rate of about 50 percent for other computer vision techniques.
In an email interview with Singularity Hub, Burchfiel notes his research is not the first to train machines on 3D object classification. How their approach differs is that they confine the space in which the robot learns to classify the objects.
“Imagine the space of all possible objects,” Burchfiel explains. “That is to say, imagine you had tiny Legos, and I told you [that] you could stick them together any way you wanted, just build me an object. You have a huge number of objects you could make!”
The infinite possibilities could result in an object no human or machine might recognize.
To address that problem, the researchers had their algorithm find a more restricted space that would host the objects it wants to classify. “By working in this restricted space—mathematically we call it a subspace—we greatly simplify our task of classification. It is the finding of this space that sets us apart from previous approaches.”
Following Directions
Meanwhile, a pair of undergraduate students at Brown University figured out a way to teach robots to understand directions better, even at varying degrees of abstraction.
The research, led by Dilip Arumugam and Siddharth Karamcheti, addressed how to train a robot to understand nuances of natural language and then follow instructions correctly and efficiently.
“The problem is that commands can have different levels of abstraction, and that can cause a robot to plan its actions inefficiently or fail to complete the task at all,” says Arumugam in a press release.
In this project, the young researchers crowdsourced instructions for moving a virtual robot through an online domain. The space consisted of several rooms and a chair, which the robot was told to manipulate from one place to another. The volunteers gave various commands to the robot, ranging from general (“take the chair to the blue room”) to step-by-step instructions.
The researchers then used the database of spoken instructions to teach their system to understand the kinds of words used in different levels of language. The machine learned to not only follow instructions but to recognize the level of abstraction. That was key to kickstart its problem-solving abilities to tackle the job in the most appropriate way.
The research eventually moved from virtual pixels to a real place, using a Roomba-like robot that was able to respond to instructions within one second 90 percent of the time. Conversely, when unable to identify the specificity of the task, it took the robot 20 or more seconds to plan a task about 50 percent of the time.
One application of this new machine-learning technique referenced in the paper is a robot worker in a warehouse setting, but there are many fields that could benefit from a more versatile machine capable of moving seamlessly between small-scale operations and generalized tasks.
“Other areas that could possibly benefit from such a system include things from autonomous vehicles… to assistive robotics, all the way to medical robotics,” says Karamcheti, responding to a question by email from Singularity Hub.
More to Come
These achievements are yet another step toward creating robots that see, listen, and act more like humans. But don’t expect Disney to build a real-life Westworld next to Toon Town anytime soon.
“I think we’re a long way off from human-level communication,” Karamcheti says. “There are so many problems preventing our learning models from getting to that point, from seemingly simple questions like how to deal with words never seen before, to harder, more complicated questions like how to resolve the ambiguities inherent in language, including idiomatic or metaphorical speech.”
Even relatively verbose chatbots can run out of things to say, Karamcheti notes, as the conversation becomes more complex.
The same goes for human vision, according to Burchfiel.
While deep learning techniques have dramatically improved pattern matching—Google can find just about any picture of a cat—there’s more to human eyesight than, well, meets the eye.
“There are two big areas where I think perception has a long way to go: inductive bias and formal reasoning,” Burchfiel says.
The former is essentially all of the contextual knowledge people use to help them reason, he explains. Burchfiel uses the example of a puddle in the street. People are conditioned or biased to assume it’s a puddle of water rather than a patch of glass, for instance.
“This sort of bias is why we see faces in clouds; we have strong inductive bias helping us identify faces,” he says. “While it sounds simple at first, it powers much of what we do. Humans have a very intuitive understanding of what they expect to see, [and] it makes perception much easier.”
Formal reasoning is equally important. A machine can use deep learning, in Burchfiel’s example, to figure out the direction any river flows once it understands that water runs downhill. But it’s not yet capable of applying the sort of human reasoning that would allow us to transfer that knowledge to an alien setting, such as figuring out how water moves through a plumbing system on Mars.
“Much work was done in decades past on this sort of formal reasoning… but we have yet to figure out how to merge it with standard machine-learning methods to create a seamless system that is useful in the actual physical world.”
Robots still have a lot to learn about being human, which should make us feel good that we’re still by far the most complex machines on the planet.
Image Credit: Alex Knight via Unsplash Continue reading
#430649 Robotherapy for children with autism
New Robotherapy for children with autism could reduce patient supervision by therapists.
05.07.2017
Autism treatments and therapies routinely make headlines. With robot enhanced therapies on the rise, often overlooked though, is the mental stress and physical toll the procedures take on therapists. As autism treatments can be taxing on both patient and therapists, few realize the stress and workload of those working with autistic patients.
It is against this backdrop, that researchers from the Vrije Universiteit Brussel are pioneering a new technology to aid behavioural therapy, and one with a very deliberate aspect: they are using robots to boost the basic social learning skills of children with ASD and while doing so, they hope to make the therapists’ job substantially easier.
A study, just published in PALADYN – Journal of Behavioural Robotics examines the use of social robots as tools in clinical situations by addressing the challenge of increasing robot autonomy.
The growing deployment of robot-assisted therapies in recent decades means children with Autism Spectrum Disorder (ASD) can develop and nurture social behaviour and cognitive skills. Learning skills that hold out in real life is the first and foremost goal of all autism therapies, including the Robot-Assisted Therapy (RAT), with effectiveness always considered a key concern. However, this time round the scientists have set off on the additional mission to take the load off the human therapists by letting parts of the intervention be taken over by the supervised yet autonomous robots.
The researchers developed a complete system of robot-enhanced therapy (RET) for children with ASD. The therapy works by teaching behaviours during repeated sessions of interactive games. Since the individuals with ASD tend to be more responsive to feedback coming from an interaction with technology, robots are often used for this therapy. In this approach, the social robot acts as a mediator and typically remains remote-controlled by a human operator. The technique, called Wizard of Oz, requires the robot to be operated by an additional person and the robot is not recording the performance during the therapy. In order to reduce operator workload, authors introduced a system with a supervised autonomous robot – which is able to understand the psychological disposition of the child and use it to select actions appropriate to the current state of the interaction.
Admittedly, robots with supervised autonomy can substantially benefit behavioural therapy for children with ASD – diminishing the therapist workload on the one hand, and achieving more objective measurements of therapy outcomes on the other. Yet, complex as it is, this therapy requires a multidisciplinary approach, as RET provides mixed effectiveness for primary tasks: the turn-taking, joint attention and imitation task comparing to Standard Human Treatment (SHT).
Results are likely to prompt a further development of the robot assisted therapy with increasing robot’s autonomy. With many outstanding conceptual and technical issues yet to tackle –it is definitely the ethical questions that pose one of the major challenges as far as the potential and maximal degree of robot autonomy is concerned.
The article is fully available in open access to read, download and share on De Gruyter Online.
Research was conducted as a part of DREAM (Development of Robot-Enhanced therapy for children with Autism spectrum disorders) project.
DOI: 10.1515/pjbr-2017-0002
Image credit: P.G. Esteban
About the Journal: PALADYN – Journal of Behavioural Robotics is a fully peer-reviewed, electronic-only journal that publishes original, high-quality research on topics broadly related to neuronally and psychologically inspired robots and other behaving autonomous systems.
About De Gruyter Open: De Gruyter Open is a leading publisher of Open Access academic content. Publishing in all major disciplines, De Gruyter Open is home to more than 500 scholarly journals and over 100 books. The company is part of the De Gruyter Group (www.degruyter.com) and a member of the Association of Learned and Professional Society Publishers (ALPSP). De Gruyter Open’s book and journal programs have been endorsed by the international research community and some of the world’s top scientists, including Nobel laureates. The company’s mission is to make the very best in academic content freely available to scholars and lay readers alike.
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