Archive for the ‘Podcast’ Category

November 20th, 2009

Robots: Learning

In this episode we speak with two experts in robot learning. Andrea Thomaz from Georgia Tech looks at how humans can teach and humanoids learn with the hope to create good human-robot interactions. We then speak with Sethu Vijayakumar from the University of Edinburgh about machine learning and how it can be used to teach a robot hand to balance a pole.

Andrea Thomaz

Andrea Thomaz is professor at Georgia Tech and the director of the Socially Intelligent Machines Research Laboratory. With a foot in human-robot interactions thanks to her PhD and Post-doc at MIT with Cynthia Breazeal, Thomaz went on to design her own humanoid-creature named Simon augmented with an amazing designer head and flanked with the most expressive ears you’ll be seeing anytime soon. Simon features an articulated torso, dual 7-DOF arms, and anthropomorphic hands from Meka Robotics.


With Simon and other humanoid robots such as Junior, she is looking at how to make social robots that can learn from humans in their everyday environment. With this endeavor in mind, her lab is studying how humans actually teach and draws conclusions that could be useful when designing future machine learning algorithms. She is also taking inspiration from nature to make robots that can learn in an incremental manner by observing and reproducing what people in their environment are doing, similar to what happens when you put two kids together in a playpen.

Andrea Thomaz is also the author of the Blog “So, Where’s My Robot?” where she posts thoughts on social machine learning. Finally, she was awarded the prestigious “MIT Tech Review 2009 Young Innovators Under 35“.

Sethu Vijayakumar


Sethu Vijayakumar is the Director of the Institute of Perception, Action & Behavior in the School of Informatics at the University of Edinburgh and an associate member of the Institute for Adaptive & Neural Computation. With the Statistical Machine Learning and Motor Control Group there he’s been looking at how robots can learn complex tasks such as balancing a pole using an anthropomorphic arm. His pursuit of the holy grail in machine learning has brought him to tackle the intricacies related to highly changing and dynamic environments. Because of this, his research interests span a broad interdisciplinary curriculum involving basic research in the fields of statistical machine learning, robotics, human motor control, Bayesian inference techniques and computational neuroscience. Finally, he’ll be telling us more generally how machine learning is different from human learning and what he sees as the next steps in this area with a short escapade in the world of prosthetics.

Since August 2007, he holds a Senior Research Fellowship of the Royal Academy of Engineering, co-funded by Microsoft Research in Learning Robotics.

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For more information on the autopsy-performing Virtobot, a great video of the Pac Man Robot Game and to revisit some of 2009’s memorable robots, including SCRATCHBOT, Festo’s Robot Penguins, the Wirelessly controlled Beetle and Robot Fashion Model HRP-4C have a look at the Robots forum!

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September 12th, 2008

Robots: An Uncertain Revolution

In this episode we dive into the revolution brought on by the field of probabilistic robotics with Claudio Mattiussi who is Senior Researcher at the Laboratory of Intelligent Systems in Lausanne, Switzerland. We then launch a most “uncertain” competition to see how our listeners are able to cope with uncertainty in estimating the cleaning capabilities of our Roomba robot.

Claudio Mattiussi

As a Senior Researcher at the Laboratory of Intelligent Systems at the EPFL in Lausanne Switzerland, Claudio Mattiussi has been looking into the world of evolutionary computation, neural networks and machine learning applied to tasks such as reverse engineering gene regulatory networks, synthesizing neural networks, and designing electronic circuits. Thanks to his experience with real-world applications and years in industry, Mattiussi has become aware of the need to deal with uncertainty, which is present in most environments and living beings. As a solution, he presents the probabilistic or Bayesian approach to perceiving the world, with a touch of history, philosophy and projection. Rather than being against good old fashion artificial intelligence (GOFAI), or Brooks’ Behavior Based approach, he proposes the “uncertain” revolution using the probabilistic paradigm as being a compromise for the future.

Finally, he discusses how the probabilities can be used to make decisions on robot behavior using neural structures and evolutionary techniques.

Uncertain Contest

For a detailed view on some of the subjects presented in this show, win the new book on “Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies”
written by Dario Floreano and Claudio Mattiussi, out on the 30th of September 2008.

To make you apply your own probabilistic approaches to a concrete problem, we’ll be asking you to guess (or compute) the percentage of dirt collected by a Roomba robot in its own “uncertain” environment. We’re waiting for your vote by Wednesday, September 24th at 9AM GMT.

All the details for the competition can be found on our forum.

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Latest News:

Check out the Robots Forum for pictures, links, videos and some ongoing discussion for this episode’s news, including the most recent iRobot headlines, Rod Brooks’ new Heartland Robotics as well as the gigantic robot spider roaming Liverpool.

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May 9th, 2008

Talking Robots Podcast LogoTalking Robots: Blue Brain Robotics
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In this episode of Talking Robots we speak with Henry Markram who is the director of the Blue Brain Project, director of the Center for Neuroscience and Technology and co-director of EPFL’s Brain Mind Institute in Switzerland. While most roboticists have been working on abstracting the brain, the Blue Brain project has been painting the whole picture of a rat neocortical column (NCC) from the bottom up; starting with the cells, neurons, and finally pulling the connections which generate the jungle of the mind. It seems that modeling our grey matter as a whole might result in emergent features such as consciousness or self representation and provide necessary tools for the study of brain disorders such as Alzheimer’s or Autism. Finally, robots embedded with such in-silico replication of the brain might not only be more efficient in communicating, showing emotions and planning, they will also serve as essential testbeds to better understand what’s happening in our head.

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April 11th, 2008

Talking Robots Podcast LogoTalking Robots: Personal Robots
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In this episode of Talking Robots we talk to Cynthia Breazeal who is an Associate Professor of Media Arts and Sciences at the Massachusetts Institute of Technology in the USA, where she founded and directs the Personal Robots Group at the Media Lab. With her creaturoids, animoids, humanoids and robotized objects, Breazeal has been working to make robots and humans team up in a human-centric way, work together as peers, and learn from one another. Breazeal’s work includes personal robots such as the very expressive Kismet, the Huggable™ robot teddy, Leonardo the social creature and the MDS (Mobile/ Dextourous/Social ) humanoid robot.

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March 14th, 2008

Talking Robots Podcast LogoTalking Robots: Curious Robots
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In this episode we interview Frederic Kaplan. After ten years of research at the Sony Computer Science Laboratory in Paris, he is now researcher at the CRAFT at the EPFL in Lausanne Switzerland where he supervises a new team focusing on interactive furniture and robotic objects. From curious AIBO robots to interactive robot computers and furniture, he has been exploring technologies permitting to endow objects with a personal history so that they become different as we interact with them and to learn from one another, thus creating an ecosystem in perpetual evolution.

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