A recent article in Technology Review introduces some cool technology from Lyric Semiconductor, a probability-based processor.
The electrical signals inside Lyric’s chips represent probabilities, instead of 1s and 0s. While the transistors of conventional chips are arranged into components called digital NAND gates, which can be used to implement all possible digital logic functions, those in a probability processor make building blocks known as Bayesian NAND gates. … Whereas a conventional NAND gate outputs a “1″ if neither of its inputs match, the output of a Bayesian NAND gate represents the odds that the two input probabilities match. This makes it possible to perform calculations that use probabilities as their input and output.
Sounds like their initial impact will be in the flash memory market, making error-checking faster and more efficient. But I can definitely see how this kind of hardware could have a major impact in Machine Learning and Robotics. Most of statistical machine learning has its roots in the kind of math that this processor is designed for. This could make reasoning about larger (more real-world) problems increasingly feasible.
September 1st, 2010
Posted by
A.L.T. |
In the News, Industry |
no comments
Last week I attended the IEEE International Conference on Development and Learning, held at the University of Michigan. This is an interesting conference that I’ve been going to for the past few years. It’s goal is to very explicitly mingle researchers working on Machine Learning and Robotics with researchers working on understanding human learning and development.
My lab had two presentations
- “Optimality of Human Teachers for Robot Learners” (M. Cakmak, A. L. Thomaz): Here we take the notion of teaching in Machine Learning Theory, and analyze the extent to which people teaching our robot are adhering to theoretically optimal strategies. Turns out they teach about positive examples optimally, but not negative. And we can use active learning in the negative space to make up for people’s non-optimality.
- “Batch vs. Interactive Learning by Demonstration” (P. Zang, R. Tian, A.L. Thomaz, C. Isbell): We show the computational benefits of collecting LbD examples online rather than in a batch fashion. In an interactive setting people automatically improve their teaching strategy when it is sub-optimal.
And here are some cool things I learned at ICDL.
Keynote speaker, Felix Warneken, gave a really interesting talk about the origins of cooperative behavior in humans. Are people helpful and good at teamwork because you learn it, or do we have some predisposition? His work takes you through a series of great experiments with young children, showing that helping and cooperation are things we are at least partly hardwired to do.
Chen Yu, from Indiana, does some really nice research looking into how babies look around a scene, and how this is different than adults or even older children. They do this by having them wear headbands with cameras, then they can do some nice correlations across multiple video streams and audio streams to analyze the data. For younger children, visual selection is very tied to manual selection. And the success of word learning is determined by the visual dominance of the named target.
Vollmer et al, from Bielefeld, did an analysis of their motionese video corpus, and showed the different ways that a child learner gives feedback to an adult teacher. Particularly that this changes from being dominated by gaze behaviors, to more complex anticipatory gestures between the ages of 8mo to 30 mo.
Several papers touched on the topic of Intrinsic motivation for robots, as inspired by babies and other natural learners. Over the past few years there has been growing interest in this idea. People have gone from focusing on curiosity and novelty, to competence and mastery. There were papers on this topic from Barto’s lab, and from Oudeyer’s. The IM CLeVeR project was also presented, this is a large EU funded collaboration that aims to address intrinsic motivation for robots.
August 27th, 2010
Posted by
A.L.T. |
Conferences, GT Lab Updates |
no comments
Exciting news recently in the realm of robotics and public policy. Robotics is recommended as a Science and Technology Priority for the 2012 budget. The recent OSTP/OMB memo lists six challenge areas, the first of which is “Promoting sustainable economic growth and job creation,” and one of the three recommendations in this section is:
“Support R&D in advanced manufacturing to strengthen U.S. robotics, cyber-physical systems, and flexible manufacturing.”
Congratulations to Henrik Christensen and all of those in the robotics community that have worked hard over the past couple of years to educate the science and technology policy makers about the ways in which robotics research and development can have a positive impact on the U.S. economy and society.
August 9th, 2010
Posted by
A.L.T. |
Industry |
no comments
Autom the weight loss coach has been on a media tour of late, running up to the first release later this year. She was looking good on a “Fox and Friends” segment this past weekend. And the great pic on the left is from some recent coverage in the Tech Review. You can also check the Robots podcast episode with Autom inventor, Cory Kidd, to hear more about the science behind their design of a social robot for healthcare.
Looks like you’ll be able to get your own Autom from Intuitive Automata for $500 in just a few months. Looking forward to seeing how people do with them!
August 2nd, 2010
Posted by
A.L.T. |
In the News, Industry |
no comments
The Nineteenth Edition of the Robotics Program at AAAI is happening right now in Atlanta GA, July 12-15th, at the Westin Peachtree Plaza. This year’s event is co-chaired by Monica Anderson and Andrea Thomaz, and is sponsored by the NSF, Microsoft Research, and iRobot.
The AAAI Robotics Program has a long tradition of demonstrating innovative research at the intersection of robotics and artificial intelligence. This year, the AAAI-10 Robotics Program will feature a workshop on “Enabling Intelligence through Middleware” (July 12th) and a robotics exhibition (July 13-15th) with the following demonstrations of intelligent robotics on display Tues-Thursday, in the Vinings rooms on the 6th floor (near registration).
- Robotic Chess: Small Scale Manipulation Challenge: The AAAI-2010 Small-Scale Manipulation Challenge is designed to highlight advances in embodied intelligence using smaller than human size robots. Robotic chess requires the integration of sensing, planning and actuation and provides an opportunity for performance on a common, well-defined task. The chess challenge will run on Tuesday July 13th, with matches at 10am, 1pm, and 4pm. Additionally the chess robots will be on display throughout the exhibit.
- Learning by Demonstration Challenge: This will be the second annual exhibit and challenge on robot Learning by Demonstration (LbD). The purpose of this event is to bring together research and commercial groups to demonstrate complete platforms performing LbD tasks. Our long-term aim is to define increasingly challenging experiments for future LbD events and greater scientific understanding of the area. This year 5 teams will bring robots that will learn a sorting task from a human teacher. The LbD challenge will run on Wednesday July 14th at 4pm. And the LbD robots will be on display throughout the exhibit.
- Robotics Education Track: This venue offers an accessible and flexible opportunity for undergraduate, early graduate, or pre-college student teams to design, implement, and demonstrate an autonomous robotic system. The tasks involved span physically-embodied AI: exploration, interaction, and learning within an unknown environment. In the long run, we hope to motivate hands-on AI robotics investigation both for its own sake and in service to other academic disciplines and educational goals. This year we have 8 university teams contributing to this part of the exhibit.
July 13th, 2010
Posted by
A.L.T. |
Conferences |
no comments
We made the NYTimes! The most recent article in the same series mentioned previously here, Smarter than you Think, is primarily about people learning from robot teachers. They also touch on the flip side of that equation, robots learning from people, covering Simon and the Socially Intelligent Machines Lab at Georgia Tech.
July 13th, 2010
Posted by
A.L.T. |
In the News |
no comments
Latest in the “Smarter than you think” series. The NYT ran an article yesterday on personal robots.
Paro as emotional companion in the nursing home, and Autom as personal weight loss coach are the most prominently featured examples in this article that explores the touchy subject of robot companions, and what kind of relationships we might let robots hold in our lives.
July 6th, 2010
Posted by
A.L.T. |
HRI, In the News |
one comment
Erico Guizzo of IEEE Spectrum has a fun graphic of “robot babies” that he’s rated on scales of capabilities and human like-ness…the full spectrum from creepy to cute.
iCub is rated as most capable, and Ishiguru’s android child is most human-like.
Simon made the graphic, in the quadrant that is my personal favorite. Maybe after our AAAI demo in a couple weeks we’ll be moving upwards on the graph!
July 2nd, 2010
Posted by
A.L.T. |
Fun, In the News |
no comments
I had a unique opportunity yesterday, I was invited to participate in a PCAST workshop (the President’s Council of Advisors on Science and Technology). The theme of the meeting was Bio/Info/Nano tech, what exciting opportunities are happening in these fields that will create jobs in the US, and what the government can do to spur innovation. I’ll probably have a couple of SWMR posts about the discussion, and thought I’d start off with one about my contribution to the discussion, since I was the only roboticist in the room.
They had a wide range of discussants, several of us were early career researchers, which I think were invited to share our “what’s new and exciting that’s going to create jobs” point of view. Another contingent of the discussion group were more seasoned researchers and entrepreneurs, that had a sort of from the trenches perspective of how the government’s support of basic research has changed over the years.
Each discussant had the opportunity in a three minute introduction to make a statement to the council. Here’s a recap of what I said:
The technology opportunity I decided to highlight is service robotics, because they have the potential to dramatically impact such a diverse set of societal needs. Robots that are capable of working alongside people will revolutionize workplaces, for example in manufacturing.
Robotics represents perhaps our best opportunity to achieve higher levels of domestic manufacturing agility and overall productivity needed to retain high-value manufacturing jobs in the U.S., provided that the current state of the technology can be significantly advanced.
Today’s industrial robots lack the capabilities required to do more than just blindly execute pre-programmed instructions in structured environments. This makes them expensive to deploy and unsafe for people to work alongside.
There is an opportunity to usher in a new era of agile and innovative manufacturing by developing service robots as co-workers in the manufacturing domain. These capable assistants would work safely in collaboration and close proximity to highly skilled workers. For example, providing logistical support, automatically fetching parts, packing/unpacking, loading, stacking boxes, emptying bins, detecting and cleaning spills.
Very similar logistical robotic support could help streamline the operation of hospitals, driving healthcare costs down.
In order to realize this vision, we need to move beyond robots only operating in relatively static structured environments. This presents several research challenges, and I think that the following three are most critical to progress.
- This requires advances in sensing and perception technology, allowing robots to keep track of a dynamically changing workplace.
- Manipulation is a key challenge as well, robots need the flexibility to be able to pickup and use objects in the environment without tedious pre-programming of specialized skills.
- Finally, an important challenge in bringing these robots to fruition is advances in human-robot interaction. We need these robots to work safely and efficiently in collaboration with human workers. People can’t just be seen as an obstacle for the robot to navigate around, the robot needs to reason about and understand people as interaction partners.
Recently, over 140 robotics experts across the country have come together to articulate a national robotics initiative, a robotics research roadmap. This roadmap lays out the target areas where we think robotics research efforts need to be supported in order to bring about robot technology that will have the biggest impact on our economy and our society.
The comment I got from one of the council members was interesting, she said (I’m paraphrasing) “Aren’t you leaving out the challenge of Sentience or AI needed?” I only had time for a short answer, and said something to the effect that, yes, I think that the notion of AI cuts across all of the three areas I mentioned, but particularly human-robot interaction. In order for a robot to work side-by-side with a human partner it will need human compatible intelligence capabilities.
But here on SWMR, I’ll give the longer answer….that, no I don’t think we need AI for service robots. Or I don’t think that’s what we should call it. Yes, perception and manipulation and HRI and autonomy in general all fit under the big umbrella term of AI. But the term AI is so vague, and it makes people think of science fiction, which then makes you feel like robots in society is some pipe dream far in the future. So, particularly in settings like PCAST where people want to hear about concrete objectives and job creation, it does our field no good to just lump everything under the term AI.
If instead we talk about the specific intelligence challenges suddenly it all seems much more achievable, and you can imagine some semi-autonomous form of service robots being deployed in the not so distant future. We see that, hey sensing technology is getting better and better, and look at all the academic and industrial partners working on the manipulation problem, that seems achievable. And in terms of AI for human-robot interaction, yes we need to make some significant advances in computational models of social intelligence before robots can truly interact with people in unstructured environments. But do we need to solve AI? I don’t think so.
June 23rd, 2010
Posted by
A.L.T. |
Conferences, HRI, Industry |
no comments
The Socially Intelligent Machines Lab at Georgia Tech is looking for a postdoc.
The work will focus on HRI for robots that learn interactively from human teachers. This will involve algorithm development, robot control implementation, and system evaluation with human subjects. The experience will include working with undergraduate, MS and PhD students, and with interdisciplinary faculty collaborators.
Applicants should have a Ph.D. in Computer Science or a field clearly related to the research area above.
Qualified applicants should provide the following materials:
- Cover letter briefly describing your background (including information about PhD institution, dissertation, and abstract) and career plans
- Date of availability to start the postdoc
- CV
- Names and contact information for at least three references including the PhD advisor,
- Link to a research web site.
These documents should be submitted as a single PDF to Prof. Andrea L. Thomaz with the email subject line: “Postdoc Candidate”
The position is guaranteed for a year from the start date, with a possible second year extension. The position has been open since June 4. We are currently reviewing applications and hope to fill the position as soon as possible.
June 15th, 2010
Posted by
A.L.T. |
Announcements, GT Lab Updates |
no comments
