In this episode, our interviewer Audrow Nash speaks to Gil Weinberg, Professor in Georgia Tech’s School of Music and the founding director of the Georgia Tech Center for Music Technology. Weinberg leads a research lab called the Robotic Musicianship group, which focuses on developing artificial creativity and musical expression for robots and on augmented humans. Weinberg discusses several of his improvisational robots and how they work, including Shimon, a multi-armed robot marimba player, as well as his work in prosthetic devices for musicians.

Thanks to all those that sent us their holiday videos. Here’s a selection of 20+ videos to get you into the spirit this season.

Welcome to the 300th episode of the Robohub podcast! You might not know that the podcast has been going in one form or another for 14 years. Originally called “Talking Robots,” the podcast was started in 2006 by Dario Floreano and several of his PhD students at EPFL, in Switzerland, including Sabine Hauert, Peter Dürr, and Markus Waibel, who are all still involved in Robohub today.  Since then, the podcast team has become international, with most of its interviewers in the United States and Europe, and all of its members being volunteers.

To celebrate 300 episodes of our podcast, we thought we would catch up with some of our former, as well as current, volunteers from around the world to find out why and how they got involved in the podcast, how their involvement impacted on their lives and careers, and what they’re doing in their day jobs now.

A group of EPFL researchers have developed a foldable device that can fit in a pocket and can transmit touch stimuli when used in a human-machine interface.

When browsing an e-commerce site on your smartphone, or a music streaming service on your laptop, you can see pictures and hear sound snippets of what you are going to buy. But sometimes it would be great to touch it too – for example to feel the texture of a garment, or the stiffness of a material. The problem is that there are no miniaturized devices that can render touch sensations the way screens and loudspeakers render sight and sound, and that can easily be coupled to a computer or a mobile device.

In its first year of operation, the Intelligent Towing Tank (ITT) conducted about 100,000 total experiments, essentially completing the equivalent of a PhD student’s five years’ worth of experiments in a matter of weeks.

In this episode, we take a closer look at the effect of novelty in human-robot interaction. Novelty is the quality of being new or unusual.

The typical view is that while something is new, or “a novelty”, it will initially make us behave differently than we would normally. But over time, as the novelty wears off, we will likely return to our regular behaviors. For example, a new robot may cause a person to behave differently initially, as its introduced into the person’s life, but after some time, the robot won’t be as exciting, novel and motivating, and the person might return to their previous behavioral patterns, interacting less with the robot.

To find out more about the concept of novelty in human-robot interactions, our interviewer Audrow caught up with Catharina Vesterager Smedegaard, a PhD-student at Aarhus University in Denmark, whose field of study is Philosophy.

Catharina sees novelty differently to how we typically see it. She thinks of it as projecting what we don’t know onto what we already know, which has implications for how human-robot interactions are designed and researched. She also speaks about her experience in philosophy more generally, and gives us advice on philosophical thinking.

That’s right! You better not run, you better not hide, you better watch out for brand new robot holiday videos on Robohub!

By Aviral Kumar

One of the primary factors behind the success of machine learning approaches in open world settings, such as image recognition and natural language processing, has been the ability of high-capacity deep neural network function approximators to learn generalizable models from large amounts of data. Deep reinforcement learning methods, however, require active online data collection, where the model actively interacts with its environment. This makes such methods hard to scale to complex real-world problems, where active data collection means that large datasets of experience must be collected for every experiment – this can be expensive and, for systems such as autonomous vehicles or robots, potentially unsafe. In a number of domains of practical interest, such as autonomous driving, robotics, and games, there exist plentiful amounts of previously collected interaction data which, consists of informative behaviours that are a rich source of prior information. Deep RL algorithms that can utilize such prior datasets will not only scale to real-world problems, but will also lead to solutions that generalize substantially better. A data-driven paradigm for reinforcement learning will enable us to pre-train and deploy agents capable of sample-efficient learning in the real-world.

At Danfoss in Gråsten, the Danish Technological Institute (DTI) is testing, as part of a pilot project in the European robot network ROBOTT-NET, several robot technologies: Manipulation using force sensors, simpler separation of items and a 3D-printed three-in-one gripper for handling capacitors, nuts and a socket handle.