Tour of the Web of February

Each month, I collect interesting stories related to the scientific interest of the team, in science, robotics, and education. Here are the highlights of February.

Hard Science

Scientists have observed a curious trend that seems to affect their research. They first publish a result about a brand new observed effect, that they conclusively prove. Then other studies of the phenomenon come from other scientists, largely corroborating the new idea. But then, the effect observed looses in strength as the years and studies pile on. The authors of the original research themselves find themselves unable to replicate their original findings, despite the apparent absence of errors in methodology in the original article. Jonathan Schooler, victim of the phenomenon, termed the effect “cosmic habituation”, as if nature was habituating to scientist ideas over time. This draw a somber and sobering picture of the effect the scientist has on its data.

New Yorker article:

The problem of bad published results is beginning to dawn enough on scientists that some of them are organising. John Ioannidis in particular, author of the 2005 article “Why most published research findings are false”, worked to develop meta-research, or research about research, and is launching the Meta-Research Innovation Centre at Stanford next month.

The Economist article:
Journal article “Why most published research findings are false”:

No Science Left Behind

The New York Times and Nature are running a piece on how American science is increasingly financed by wealthy individuals, in a background of budget cuts. The money is usually aimed at trendy subjects, which could skew scientific research to the detriment of less sexy fields and basic research, which prompted Nature to publish a warning in that sense.

New York Time article:
Nature article:

Concurrently, the Brain initiative got a big boost in public financing from (announced by) Obama, from $100 millions to $200 millions. The project scientific committee also published a report with a more detailed list of the goals of the initiative.

White House press release:
Executive summary (6 pages):


Math “is fundamentally about patterns and structures, rather than “little manipulations of numbers,”” says math educator Maria Droujkov. But with the way the mathematics are taught, she claims, it is hard to get that, and the mechanical drills that are imposed on us from an early age often prevents children from enjoying mathematics for life. Droujkov defends the idea of going from “simple but hard” activities (rote learning of multiplication tables) to “complex but easy” ones (legos or snowflakes cut-outs to learn symmetry). Well worth the read.

The Atlantic article:

The Computing Research Association has published a sneak preview of its 2013 Taulbee Report, to be published in May in CRN, which looks at enrolment figure in CS for the US. The most notable part is the 22% increase in BS enrolment year-over-year.

Press release:

The robot is coming from the US, but the – open source – software suite that powers it is French. And it is in a the lycée la Martinière Monplaisir, in Lyon, that it is being deployed. This telepresence robot is aimed at students unable to be physically present for temporary periods. It makes interacting in class possible, with the teacher and other students, through what they call, tongue-in-cheek, the “robodylanguage”. (shared by Nicolas Jahier)

LyonMag article (in french):

Video from FranceTVInfo (in french):

Musician Cyborg

Science meets art, and creates new things. Gil Weinberg of the Georgia Tech Center for Music Technology (which he founded), created (more probably, supervised the creation of) a robotic prothesis that uses electromyography to control a first drumstick. A second drumstick is also present on the prothesis, but is controlled algorithmically. For the moment, the second drumstick is a on an open loop, with the drummer, Jason Barnes, being the close loop by responding to the rhythms of the second sticks. In the perspective, synchronisation routines and machine learning are envisioned to create a more reactive, yet autonomous second stick.

Press release:

Robots are less impressed by a three-drumsticks drummer. After all, coming from Japan with a 22-arms drummer and 78-fingers guitar player, they are showing off their ability to handle arbitrarily many way of independence. Squarepusher, the composer, is pushing the notion that for music to be emotionally powerful, it doesn’t necessarily have to be performed by humans. (shared by Clément Moulin-Frier)

Pitchfork short story (with video):


ICDL-Epirob 2013: Learning how to learn

In a few week in Osaka, Japan, we will present our latest work on ways to enable robots to autonomously choose how they learn, in an article under the title of Autonomous Reuse of Motor Exploration Trajectories.

We decided to explore the idea of enabling a robot to modify its own learning method based on its previous experience. Human do the same; when studying, for instance when learning by heart a piece of knowledge, they explore different strategies: reading multiple times, rewriting, enunciating, visualizing, and repeating the learning sessions, or learning only once, the night before the test. Each individual eventually choose its preferred strategy, and tweak the specifics of it has it is reused, often based on its perceived effectiveness. Humans learn how to learn.

In our work, we focused on how a robot could improve the way it explore a new, unknown task. The exploration strategy is a important factor the learning effectiveness; work on intrinsic motivation by our team demonstrated that. And autonomous robots, while potentially subjected to very diverse situations, retain a constant morphology: their kinematics and dynamics remain stable, and their motor space, the set of possible motor commands, stays the same. The hypothesis we made was that some exploration strategies are a priori more effective for a given robot, and that those strategies can be uncovered by analyzing past learning experience, that is, past exploration trajectories. Using those exploration strategies would lead to increase in learning performance, compared to random ones.

Our experiment confirmed that. We identified, through autonomous empirical measurement, the motor commands of a first task that belonged to areas where learning had been the most effective, and then reused them on a similar, different tasks, where the robot didn’t have access the the learning experience of the first task. The early learning performance increased significantly. Our methods only requires that the motor space stays the same, the sensory space can be arbitrarily different (hence making possible to reuse an exploration strategy to learn in another modality), and doesn’t make assumptions about the learning algorithms used, which can even differ between tasks.

The article is available here here.
We released the code used to run the experiments, so that anyone can reproduce and analyze them. You can access it here

Reference: Fabien Benureau, Pierre-Yves Oudeyer, “Autonomous Reuse of Motor Exploration Trajectories“, in the proceedings of ICDL-Epirob 2013, Osaka, Japan.