There is a psychological phenomenon called pareidolia, where people tend to identify faces in inanimate objects. In order to counter this bias, the artists hypothesized that the sphere is the shape that least resembles humans. They made a prototype to explore the question “how can we use design to balance a robot’s appearance and behavior and the user’s expectations?” Their robot is spherical but can shape-shift in order to give a more or less human-like impression. By controlling its shape they can capture how people’s social behavior changes depending on the robot’s form.

Supported by Japan Shunji Yamanaka Laboratory, University of Tokyo, Japan, and Mitsubishi Electric Corporation

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Supported by Japan Shunji Yamanaka Laboratory, University of Tokyo, Japan, and Mitsubishi Electric Corporation

But if they make too many mistakes Deltu might just get upset and decide to ignore them. Frustrated, Deltu will leave the game and take some selfies to post on Instagram.

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Supported by ECAL, École cantonale d’art de Lausanne

Kitchen Budapest (HU)

Since the first industrial revolution, automation has been one of the primary instruments of increasing productivity, that is replacing human workforce with task-performing machines. The thermo-mechanical models of the early industrial age have now evolved by into more complex electro-computational networks, where scripted interactions are staking out an ever-growing number of domains and specialist fields.

Under algorithmic capitalism, automation (robotics with automated thought—AI) are disrupting a large segment of industry and life as we know it: from the self-driving cars reinventing transport, chatbots reinventing friendship, unmanned drones reinventing war, synthetic bodies/persons reinventing human rights, to e-government reinventing politics.

In contrast to the perceived obfuscation in much of the computational protocols today, Training 2038 stages a scenario where people are given a voice to provide feedback on systems that are just taking shape. In the safe space of a private VR experience, an extensive survey is underway in the form of a dialog played out between an embodied conversational bot and a human user. With no moral conscience, how would an automated decision tree behave?

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Kitchen Budapest is Gábor Pribék (HU), Iván Rohonyi-Demkó (HU), Jonathan Ravasz (HU), Filip Ruisl (SK), Richárd Nagyfi (HU), Szilvi Német (HU), Attila Nemes (HU), Patrik Makrai (HU), Judit Varga (HU), Orsolya Forster (HU), Benjamin A. Balla and Healium Decoration (HU)

Supported by Telekom Group Hungary

NoodleFeet is the functioning robotic manifestation of an illustrated character built from light metal, 3D-printed parts and found objects. Noodle has been developed with mechanical and electronic systems which allow him to exhibit behaviors when stimulated by objects in his environment. His purpose is to exist freely in the world while reacting to situational encounters using self-defining methods of personal expression. Where most technology has a practical or utilitarian application meant to enhance our lives, Noodle is a unique entity who functions without regard to a human’s perception of his purpose or usefulness. The artist’s goal is for this to provoke consideration about the motivation behind humanity’s current innovations. She hopes that those who interact with Noodle will witness a meaningful sense of self from him that will encourage reflection with regard to the value of their own relationship to the technology common in everyday life.

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This project is presented in the framework of the European Digital Art and Science Network and co-funded by the Creative Europe program of the European Union.

Dragan Ilić (RS/AU/US)

A3 K3 is a unique interactive experience. Artworks are created by machine technology and audience participation. Dragan Ilić uses an elaborate brain-computer interface (BCI) system where he controls a hi-tech robot with his brain via state-of-the-art technology.

Members of the audience are invited to try out the BCI technology. The artist and the audience draw and paint on a vertical and a horizontal canvas with the assistance of the robot. The robotic arm is fitted with DI drawing devices that clamp, hold and manipulate various artistic media. They can then create attractive, large-format artworks. Ilić thus provides a context in which people will be able to enhance and augment their abilities in making art. Thanks to the support of g.tec, Dragan Ilić will undertake further research with AI systems/human interaction in the process of making art.

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This program is supported by g.tec and GV Art London.

While Rock Print appeared massive, it was relatively lightweight due to the use of foam-glass gravel. Two years later, the research breakthroughs are responding equally strongly to an ecological and technological agenda: No additives, no substitutes, no mortar, no formwork, just real string and real gravel. It is the purest yet most highly advanced presentation of the robotic fabrication of jammed gravel held in place by computed and robotically placed string patterns to form architectural structures. It is a solid massif built up from loose rock: a Manistone.

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Gramazio Kohler Research, ETH Zürich

Team: Petrus Aejmelaeus-Lindström and Gergana Rusenova (project lead), Ammar Mirjan, Romana Rust, Hannes Mayer, Fabio Gramazio, Matthias Kohler

In cooperation with: Prof. Hans J. Herrmann, Dr. Falk K. Wittel with Pavel Iliev (Institute for Building Materials, ETH Zurich)

Project funding: ETH Zürich Foundation

Selected experts: Self-Assembly Lab, Massachusetts Institute of Technology (MIT)

About the artists

Gramazio Kohler Research
Since its inception in 2005 the research group at ETH Zürich led by Prof. Matthias Kohler and Prof. Fabio Gramazio has been at the forefront of robotics and digital fabrication in architecture. With their robotic laboratories and work ranging from prototypes to building elements, they have inspired architects and researchers alike to explore the capacities of the industrial robot as a universal tool of the digital age.

Read more: starts-prize.aec.at.

This project is presented in the framework of the STARTS Prize 2017. STARTS Prize received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 732019.

Our interactive installation responds to a commonly cited social fear–robots taking over work from humans. The World Economic Forum predicts that robots will take five million jobs over the next five years. However, we believe in a more optimistic future, where robots do not replace humanity, but instead enhance and complement it. Ordinarily, robots like Mimus are completely segregated from humans as they do highly repetitive tasks on a production line. With Mimus, we illustrate how wrapping clever software around industry-standard hardware can completely reconfigure our relationship to these complex, and often dangerous, machines. Rather than viewing robots as human adversaries, we show a future where autonomous machines like Mimus might be companions that peacefully co-exist with us on this planet.

Industrial robots are the foundation of our robotic infrastructure, and have remained relatively unchanged over the past 50 years. With Mimus, we highlight an untapped potential for this old industrial technology to work with people, not against them. Our software illustrates how small, strategic changes to an automation system can take a one-ton beast-of-a-machine from spot welding car chassis in a factory, to curiously following a child around a museum like an excited puppy. We hope to show that despite our collective anxieties about robotics, there is potential for empathy and companionship between humans and machines.

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Madeline Gannon is the founder and principal researcher of ATONATON

Development Team: Madeline Gannon, Julian Sandoval, Kevyn McPhail, Ben Snell

Supported by: Autodesk, ABB Robotics, and The Studio for Creative lnquiry

About the artist

Madeline Gannon (US) is a multidisciplinary designer working at the intersection of art and technology. She leads ATONATON, a research studio inventing better ways to communicate with machines. In her research, Gannon designs and implements cutting-edge tools that explore the future of digital making. Her work blends disciplinary knowledge from design, robotics, and human-computer interaction to innovate at the edges of digital creativity. Gannon is currently completing a PhD in Computational Design at Carnegie Mellon University, where she is developing techniques for digitally designing and fabricating wearables on and around the body.

Lesen Sie mehr auf: starts-prize.aec.at.

This project is presented in the framework of the STARTS Prize 2017. STARTS Prize received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 732019.

Participants can work with the Experience Workshop’s 4Dframe and ReBOT kit, which makes it possible to tinker together robots out of straws or empty boxes and bottles, which can be controlled wirelessly by a mobile phone. For all of this you do not need anything but a pair of scissors, duct tape and some recyclable materials. GeoGebra apps provided by the Johannes Kepler University will be available for perfecting designs and calculating different properties.

During the program, Experience Workshop is documenting and displaying all the stories told by the brainy builders about the robot they need the most.

Then, a so-called Singmaster String is sent to a C / C++ program running on a high-performance computer, which is then able to optimally solve the cube—i.e. in 21 or fewer 90° rotations. The first time the program starts up, approximately 1.5 gigabytes of solutions are generated; they’re arrayed in a tree (brachiating) structure with, of course, the completed cube at the end. These are, needless to say, nowhere near all possible combinations. If none of these combinations matches the cube’s current combination, then it must at least include symmetries since all “non-symmetries” are depicted in the solutions tree. In this case, the cube is rotated until its composition corresponds to one of the anti-symmetrical groups in the solutions tree. Thereafter, it’s simply a matter of following the path to the solved cube. Solution duration: approximately 1 second to 5 minutes.

The result of the program—or, to put it more precisely, the rotation commands—are sent via a Python Script to the robot’s three Arduino Unos, which interpret these in the form of commands for the stepper motors and finally carry them out.

By means of various deep-learning frameworks, thousands of simulations and/or training sets for reinforcement learning are run on the in-house servers at Spengergasse Technical School and the Technical University of Vienna and subsequently tested on the Hexapod. However, to get a clearer picture of its physical surroundings, the Hexapod has recourse to a stereo camera, which puts the information about these surroundings to use. Thanks to object recognition capabilities, the robot can navigate independently in it field of deployment and execute tasks.