Ada - intelligent space: an artificial creature for the SwissExpo.02

Eng, Kynan; Bäbler, Andreas; Bernardet, Ulysses; Blanchard, Mark; Costa, Márcio O.; Delbrück, Tobi; Douglas, Rodney J.; Hepp, K.; Klein, David; Manzolli, Jônatas; Mintz, Matti; Roth, Fabian C.; Rutishauser, Ueli; Waßermann, Klaus; Whatley, Adrian M.; Wittmann, Aaron; Wyss, Reto; Verschure, Paul F. M. J.

doi:10.1109/robot.2003.1242236

Cited by 26 publications

(24 citation statements)

References 7 publications

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“…The interactive tactile floor (Delbrück et al, 2007) was developed for an interactive entertainment environment (Eng et al, 2003). It was considered desirable to devise a topology learning algorithm for the floor to avoid the need for cumbersome manual topology definition each time it was assembled.…”

Section: Resultsmentioning

confidence: 99%

“…It is designed to deal with real-world noisy input and adapt appropriately to changes in the topology of the system due to damage or component redistribution. To test the algorithm, we show real data recorded from a large, interactive tactile sensory floor (Eng et al, 2003;Delbrück et al, 2007) and an event-based temporal contrast silicon retina (Lichtsteiner, Posch, & Delbruck, 2006, 2008.…”

Section: Introductionmentioning

confidence: 99%

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Getting to Know Your Neighbors: Unsupervised Learning of Topography from Real-World, Event-Based Input

2009

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Biological neural systems must grow their own connections and maintain topological relations between elements that are related to the sensory input surface. Artificial systems have traditionally prewired such maps, but the sensor arrangement is not always known and can be expensive to specify before run time.Here we present a method for learning and updating topographic maps in systems comprising modular, event-based elements. Using an unsupervised neural spike-timing-based learning rule combined with Hebbian learning, our algorithm uses the spatiotemporal coherence of the external world to train its network. It improves on existing algorithms by not assuming a known topography of the target map and includes a novel method for automatically detecting edge elements. We show how, for stimuli that are small relative to the sensor resolution, the temporal learning window parameters can be determined without using any user-specified constants. For stimuli that are larger relative to the sensor resolution, we provide a parameter extraction method that generally outperforms the small-stimulus method but requires one user-specified constant. The algorithm was tested on real data from a 64 × 64-pixel section of an event-based temporal contrast silicon retina and a 360-tile tactile luminous floor. It learned 95.8% of the correct neighborhood relations for the silicon retina within about 400 seconds of real-world input from a driving scene and 98.1% correct for the sensory floor after about 160 minutes of human pedestrian traffic. Residual errors occurred in regions receiving little or ambiguous input, and the learned topological representations were able to update automatically in response to simulated damage. Our algorithm has applications in the design of modular autonomous systems in which the interfaces between components are learned during operation rather than at design time. Biological neural systems must grow their own connections and maintain topological relations between elements that are related to the sensory input surface. Artificial systems have traditionally prewired such maps, but the sensor arrangement is not always known and can be expensive to specify before run time. Here we present a method for learning and updating topographic maps in systems comprising modular, event-based elements. Using an unsupervised neural spike-timing-based learning rule combined with Hebbian learning, our algorithm uses the spatiotemporal coherence of the external world to train its network. It improves on existing algorithms by not assuming a known topography of the target map and includes a novel method for automatically detecting edge elements. We show how, for stimuli that are small relative to the sensor resolution, the temporal learning window parameters can be determined without using any user-specified constants. For stimuli that are larger relative to the sensor resolution, we provide a parameter extraction method that generally outperforms the small-stimulus method but requires one user-specified constant. T...

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Getting to Know Your Neighbors: Unsupervised Learning of Topography from Real-World, Event-Based Input

2009

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“…The interactive exhibition Ada: intelligent space (Eng et al 2003;Bullivant 2005) accommodates multiple people. It consists of interactive floor tiles that light up in a variety of colours, for example, in response to visitors stepping on them.…”

Section: Ada: Intelligent Spacementioning

confidence: 99%

Architecture and Interaction

Dalton

Schndelbach²,

Wiberg³

et al. 2016

Human–Computer Interaction Series

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HCI is a multidisciplinary field focused on human aspects of the development of computer technology. As computer-based technology becomes increasingly pervasive -not just in developed countries, but worldwide -the need to take a human-centered approach in the design and development of this technology becomes ever more important. For roughly 30 years now, researchers and practitioners in computational and behavioral sciences have worked to identify theory and practice that influences the direction of these technologies, and this diverse work makes up the field of human-computer interaction. Broadly speaking it includes the study of what technology might be able to do for people and how people might interact with the technology. The HCI series publishes books that advance the science and technology of developing systems which are both effective and satisfying for people in a wide variety of contexts. Titles focus on theoretical perspectives (such as formal approaches drawn from a variety of behavioral sciences), practical approaches (such as the techniques for effectively integrating user needs in system development), and social issues (such as the determinants of utility, usability and acceptability).

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“…The synthetic components of re(PER)curso are realized with computer generated graphics, automated moving light and stage control, video art, a synthetic music composition system called RoBoser [Manzolli and Verschure 2005], and an avatar embedded in a 3D graphic environment. The integration of all elements is realized through the multi-modal mixed reality system the eXperience Induction Machine (XIM) that is based on an earlier large scale public exhibition called Ada [Eng 2003]. XIM is controlled through a neuromorphic system that defines all the rules of interaction and performance dynamics and as a result the complete performance is synthesized in real-time and evolves without human intervention beyond that of the two human actors on the stage.…”

Section: Introductionmentioning

confidence: 99%