So, Where’s My Robot?

Brooks on future of HRI

There was a recent Newsweek interview with Rod Brooks, about the future of HRI. Hammers on many of the same points that Rod tends to make about social robots, their place in society, and the impact they might have on people.

August 25th, 2008 Posted by A.L.T. | In the News, HRI | no comments

Heart Robot

The heart robot is an intriguing project, recently covered on CNET.  Their site has a nice introduction to social-emotional robots, with several examples of existing social robots. And there is an interesting documentation of the design process that they went through to build their semi-autonomous puppet robot.

The motivating question is : “How will people change when the machines around them seem to have emotions?” Their goal is to use their robot puppet, with puppeteers that are skilled at making things “come to life” to take this question out of the lab and into the world.

I think that people often think of emotional machines as “tricking” people, which some are definitely designed to do. In my own work (inspired by great mentors) I like to push the emotional aspect of machines into the functional realm. Asking, what is the function of emotion in human intelligence and human communication, and what functional role can emotions play in a machine that is meant to co-exist and communicate with (emotion-ful) people.

July 30th, 2008 Posted by A.L.T. | HRI | no comments

Junior at AAAI

My lab participated in the Robot Workshop and Exhibition at AAAI this year. We presented a project that Maya Cakmak is working on with this little Bioloid robot we call Junior.

The project is about affordance learning, or learning about the effects of your actions in the world. The system gathers a training dataset, by playing with objects. It collects examples of the form: (perceptual context)(action performed)(observed effects). It learns SVM classifiers and is then able to predict effects for given action-context pairs.

We are interested in what would be different when the robot explores an environment by itself versus when it has a human teacher helping it explore the environment. Our intuition is that a teacher should make the process faster and more efficient, and we just finished an experiment that looks in detail at what exactly changes between self and social learning. In the experiment, people helped Junior by placing objects in the workspace for him to play with, helping learn affordances like “rollable,” “liftable,” “moveable.”

We’re in the process of analyzing and writing up the results, but we can say that both types of data sets result in reasonable classifiers. Social data sets are different in some interesting ways, like having much higher representation of positive examples. Junior was also able to consistently get people to provide help at just the right time, by using a gazing gesture when it couldn’t quite reach an object.

Details to come, but you can read more about the project here.

July 22nd, 2008 Posted by A.L.T. | Machine Learning, Conferences, HRI | no comments

Social Robots: the ultimate AI “open world”

Just returning from a week in Chicago at the AAAI 2008 conference. Eric Horvitz is currently serving as the President of AAAI and kicked off the conference with a talk that looked at the history of the field and shared his thoughts about what will move us forward.

He challenged the community to work towards AI in the “open world.” Meaning, get the algorithms and systems out of simulated lab settings and toy problems, and in to real world uncertainty, dynamics, and overall messiness.

The last half of his talk posed a number of challenge scenarios for AI that represent “open world” problems that will be good for advancing the field:

• Robot manipulation, opening door handles, being able to transfer knowledge and experience to a novel door handle.
• Semantic robot vision challenge
• DARPA’s grand challenge and urban challenge
• Predicting highway traffic, learning patterns of human activity
• Human-computer collaboration, building shared representations to ground collaborative activity
• Automated receptionist systems

I was pleasantly surprised with the challenge scenarios that Eric chose to highlight, because without saying so directly, his talk made the argument for social robots being an ultimate AI challenge domain. Essentially the two main open world problems he suggested for AI research were (1) robots and (2) human interaction.

I, of course, whole heartedly agree, and hope to see more social robotics research at AAAI!

July 18th, 2008 Posted by A.L.T. | Conferences | no comments

Interactive Robot Learning @ RSS 2008

Last weekend I ran an RSS workshop with Henrik Jacobsson, Danijel Skocaj, and Geert-Jan Kruijff. Today, I was writing up a breifing for the euCognition project and thought I’d also post that here. Soon, you’ll find all of the papers linked on the workshop website: http://www.dfki.de/cosy/www/events/InteractiveRobotLearning2008/

Interactive Robot Learning, RSS 2008

Many future applications for autonomous robots bring them into human environments as helpful assistants to untrained users in homes, offices, hospitals, and more. These applications will require robots to be able to quickly learn how to perform new tasks and skills from natural human instruction. The key here is to make it possible for the human to interact with the robot without having to read a manual.

The workshop on Interactive Robot Learning (IRL) was held at the Robotic Science and Systems 2008 conference. The discussion spanned the breadth of research questions at the intersection of Machine Learning and Human-Robot Interaction.

The workshop began with a keynote speaker, Jeff Orkin from MIT, with experience from the video game industry. Orkin gave an overview of a project in which he and his colleagues are collecting data from thousands of people playing a game called The Restaurant Game. In the game people act out a normal scene of being a waiter or a customer in a restaurant, thereby “teaching” the computer about social behavior and dialog that are common in this situation. One important thing that we learned from Orkin’s project related to IRL is that people will not always give perfect input or examples to your learning system. Therefore it is important to collect enough data and to use algorithms that let the anomalies wash out of the model.

In addition to invited keynote speakers we had 7 papers that were submitted, reviewed and selected to present at the workshop. In the morning session we had three of these speakers present. The first was Sylvain Calinon from EPFL, who spoke about a programming by demonstration framework that incorporates natural teaching mechanisms like paying attention to pointing and gaze direction of the teacher, and allowing the teacher to physically move the robot during training. Maren Bennewitz then presented a paper about recognizing gestures, like head nods, and hand gestures. Many of the gestures were quite generic and would have broad use in communicating to a robot learning system. Olaf Booij presented work on interactive mapping. In this project a robot learns a semantic map of places in a home, by clustering sensor data. It uses interaction with a human partner to simplify the task. In ambiguous situation it engages the human partner in a simple dialog, asking the name of the current location.

The afternoon session began with a keynote speaker, Jan Peters from the Max-Planck Institute of Biological Cybernetics. Peters works in robotics, nonlinear control, and machine learning. In his talk he covered a framework of motor skill learning for robots. This starts with parameterized motor primitives, used for movement generation, and then higher level tasks involve transforming these movements into motor commands. To achieve this Peters introduces an EM-based reinforcement learning algorithm, which learns smoothly without dangerous jumps in solution space. Additionally, learning smoothly in the solution space is likely to be the most understandable to a human teacher.

In the afternoon we had three more paper presentations. Two papers were in the realm of assistive robotics. Adriana Tapus presented a robot therapy system that learns and adapts on-line to personalize the therapy and maximize health benefits/outcomes. Their approach is a novel incremental learning method, positive results were found with both stroke rehabilitation patients and dementia patients. Ayanna Howard also presented an therapy application for interactive learning. Their goal is for the robot to be able to observe and evaluate a therapy patient’s exercises, assisting the job of the therapist. They presented two methods for learning to recognize therapy exercises from visual input. Jure Zabkar presented the final paper of this session about using qualitative representations in robot learning. Zabkar argued for this approach, as it results in models that are intuitive for non-expert human’s to inspect and understand, which is a key component of interactive learning.

The workshop ended with a final keynote speaker, Aude Billard from EPFL. Billard has made several contributions over the years, in the realm of robot programming by demonstration, and her talk covered many aspects of the work that her lab has tackled on the problem of imitation learning for robots. In particular their focus on the complementary problems of “what to imitate”, and “how to imitate.” The first is about determining the key components or features that really represent the goal of a task). Having a framework for determining “what to imitate” give the system means to generalize appropriately. Their approach is based on Gaussian Mixture Models. The “how to imitate” problem involves translating motions seen by a human to motions the robot itself can do, and also achieving the goal of the task. Their approach is a stable dynamical system, active in a hybrid cartesian-joint angle frame of reference. This ends up being able to handle perturbations in the environment, joint angle limits, and adapts to changes in target position.

July 3rd, 2008 Posted by A.L.T. | Machine Learning, Conferences, HRI | no comments

Anthropomorphism and the Illusion of Life

What is anthropomorphism?

I think a lot of times when roboticists talk about anthropomorphism, it has an air of cynicism. Like “oh those silly people, they think the robot has thoughts and feelings.” Which is obviously not true for most people, but it’s interesting to think about why people have the propensity to anthropomorphize, and to understand what, if anything, we should do to design for this.

Dan Dennett’s Intenional Stance offers some insight. It explains that while people realize that some thing may not actually have desires and intentions, it ends up being an efficient and successful way to reason about a lot of phenomena that we come across. Put another way, as social animals, humans became very good at reasoning about mental states (beliefs, desires, intentions) to predict the actions of other animals. Human’s applied this reasoning strategy to anything that produced some self-motivated action that could not be described by physics. These days it’s not just animals, there are a lot more things in the world that produce seemingly self-motivated actions that are not (on the surface) describable by physics (cars, computers, robots, …); therefore, the mental state reasoning strategy kicks in.

Don Norman has a similar stance and has long studied how an object’s design and appearance communicate and inform people of the object’s possible functions. In a recent book he takes this into the realm of robots, arguing that anthropomorphism is an abstraction that will help people understand how to interact with a robot, and that robots should use familiar mechanisms like emotive expressions to communicate internal state to a human user.

Maximizing anthropomorphism

The field of animation has a long history of maximizing anthropomorphism. Frank Thomas and Ollie Johnston wrote a beautiful book in 1981, The Illusion of Life, about the principles and process employed in some of the Disney classic animations. In a SIGGRAPH paper in the late 80s, Pixar’s John Lasseter describes how the Disney principles of creating the “Illusion of Life” in 2D animation should translate to 3D animation. In the paper he steps through the 11 fundamental principles of traditional animation and discusses their 3D corralate, arguing that many of the principles transcend the particular medium. Which to me begs the question….How can these principles of animation inform how we create of an “Illusion of Life” in a robot?

Animation Principles for Robots

I’ll mention six of the principles (interpreted pretty loosely) that I find most directly applicable and interesting for thinking about robot behavior design. Most of these can be summed up as: It’s not just what you do, but how you do it. In robotics, I think we tend to work on how to select a particular motor control program, action, or behavior (selecting what to do). These principles of animation argue for having additional mechanisms to dynamically control parameters of how you do it.

Appeal — Creating a design or an action that the audience enjoys watching. This is what usually comes to mind when people talk about robots and anthropomorphism. It is the principle that would argue for creating robots with baby or pet-like proportions, so they are inherently appealing and nice to watch.

Anticipation — The preparation for an action. This is the technique of doing something to prepare the audience for the action that is about to come. Directing their attention to the right part of the screen. I think the best example of this is the “wheely feet” that cartoon characters get before taking off running. Or making sure that an arm movement winds up before exerting some energy. These are definitely cues that robots of all morphologies could use to help a human partner understand more about what they are “about” to do. It would make people feel more comfortable if they felt they could accurately predict the robots behavior. Thus, I think cues to help with audience/user anticipation are important. I’m not currently working with any mobile robots, but if I was…I would be making them some “wheely-feet”!

Arcs — The visual path of action for natural movement. Many of these principles argue against motion control that is simply based on an efficient path, or some inverse kinematics. Arcs, refers to the fact that even in a reaching action or something where the end point is the goal, there is something about the path that is taken to the end point that can make the motion more natural (and, I would hypothesize more predictable. This could ease interaction because a person would find the robot’s actions more intuitive to follow).

Secondary Action — The action of an object resulting from another action. So, the part about objects moving in relation to a character’s actions, robots get for free with physics. But a more subtle aspect of secondary action is within the character’s body. As one example, when you nod your head, it is more than just your neck bone moving. A natural head nod will have secondary actions in some of the robot’s facial features (e.g., if it has eyebrows, or ears). These secondary actions, while not instrumental to the getting from A to B goal, create a more natural looking behavior.

Timing — Spacing actions to define the weight and size of objects and the personality of characters. Robots can certainly use this one. Slow motions communicate something different than fast motions. Thus, not just getting the hand from A to B, but again, it’s about how you get there with a particular speed.

Follow Through and Overlapping Action — The termination of an action and establishing its relationship to the next action. I think this relates most to the prior point about anticipation. Not only does a single action need to have “set up,” but all of the robot’s actions have to make sense together. One is the anticipation of the other. And importantly, a particular action or behavior might terminate differently depending on what action or behavior is coming next.

June 17th, 2008 Posted by A.L.T. | HRI | no comments

NEWHRI summary: Let’s all be friends…

I attended the NEW HRI workshop at ICRA. Though it wasn’t ever clear what they meant by “NEW,” it was a nicely put together workshop that generated a good discussion about “What is HRI anyway?”

All of the speakers were asked to focus on the same three questions:
1) What characteristics unify and should be common to all areas of HRI research?
2) What properties typify novel and significant research in HRI?
3) How can we further synergistic research between HRI and the broader robotics community?

I think it was good to have this workshop at ICRA because there were a broad range of people there. There were plenty of “big-H little-R I” folks, and a fair number of “little-H big-R I” folks as well. There was a *very* wide range of answers to the three questions. I think there were really only one or two people that had any overlap in what they thought were unifying characteristics of HRI research, or what should be common to all HRI research.

A lot of the discussion boils down to what should count as a contribution in the HRI community. Do you have to do a user study for it to be HRI? How interesting/complicated does your robot need to be for it to be HRI? etc.

In the end everyone agreed that it is far too young of a field to start drawing boundaries. At this point, to make sure that the field doesn’t end up being some very narrow aspect of HRI, we need to be inclusive and appreciate a variety of different kinds of research contributions.

Thus, the day ended on a very Kumbiya note. But several people pointed out that what really matters is when the rubber hits the road. For this multidisciplinary field to succeed, we need people to appreciate and advocate for a broad view of HRI contributions in conference, journal, and tenure reviews.

June 10th, 2008 Posted by A.L.T. | Conferences, HRI | no comments

Hello Again!

Well, I fell off the wagon last semester, but it’s time to dust off the blog and get things going again.

It’s been a fun, exciting, and hectic first year at Georgia Tech. I taught a new grad course in the Spring Designing the course was interesting in and of itself, a seminar on Human-Robot Interaction. Given that the field of HRI is still defining itself, the topic list for a course on HRI is pretty up in the air. So, the course reflects my particular interests in HRI and has an AI and Cognitive Science bent. I chose a few broad categories of social intelligence and for each category we did a survey of readings related to what we know about how this capabilities arises in humans, and then coupling that with readings on state-of-the-art approaches to implementing these kinds of capabilities on robots. For example: understanding intentions, social learning, teamwork, empathy and emotions.

My other main focus this year has been getting together a social robot platform for my research group. I’m working with a company, Meka Robotics, who are building us an upper torso robot with two arms and three finger hands. And a group at the Georgia Tech Research Institute is building us a socially expressive head. It’s been a really fun and interesting process, stay tuned for more details as we start to get the hardware up and running.

June 6th, 2008 Posted by A.L.T. | Announcements | no comments

Robot Learning at NIPS 2007

Jan Peters and Marc Toussaint are running a workshop at NIPS this year called “Robotics Challenges for Machine Learning.” Abstracts due Oct 21.

Creating autonomous robots that can assist humans in situations of daily life is a great challenge for machine learning. While this aim has been a long standing vision of robotics, artificial intelligence, and the cognitive sciences, we have yet to achieve the first step of creating robots that can accomplish a multitude of different tasks, triggered by environmental context or higher level instruction.

It would be great to get the NIPS community thinking about robotics, and in particular the kind of adaptation and learning that needs to happen in order for robots to be useful assistants to humans in everyday life. So, I encourage people to participate. The stated goal of the workshop is to bring together people that are interested in robotics as a source and inspiration for new Machine Learning challenges. My take is that social interaction is a new challange, and that it’s a necessary and useful constraint for Machine Learning algorithms in these scenarios. Should be a great venue for that conversation.

October 11th, 2007 Posted by A.L.T. | Conferences, Announcements | no comments

Everybody loves their Roomba?

Beki Grinter and other colleagues at Georgia Tech recently had a paper at Ubicom on their ethnographic study of people’s Roomba usage. It colllected quite a bit of press, an AP article, robots.net, and even inspired Comedy Central’s Colbert Report to feature robots as the #1 Threat Down…nice!

These Roomba studies always leave me wondering about the people that we are not hearing from. I personally know a lot of people that stopped using a Roomba, they either didn’t find that it cleaned very well, or got tired of “roomba-izing” their houses. It’s facinating to learn about the Roomba fans, but as a counterpoint it would be great to see some interviews and analysis of the non-fans too.

October 11th, 2007 Posted by A.L.T. | In the News, Fun, HRI | 4 comments