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Institut für Anthropomatik und Robotik
Lehrstuhl für Hochperformante Humanoide Technologien
Adenauerring 2, Geb.50.20
76131 Karlsruhe

Sekretariat

Sprechstunde:

Mo. - Do.: 10:00h - 12:00h

Telefon: +49 721 608-43547
            +49 721 608-48277

Email: sekretariat asfourIas4∂anthropomatik kit edu

TIMESTORM: Mind and Time - Investigation of the temporal attributes of human-machine synergetic interaction

TIMESTORM: Mind and Time - Investigation of the temporal attributes of human-machine synergetic interaction
Ansprechpartner:

Tamim Asfour 

Links:
Förderung:

EU FET-ProActive Project

Starttermin:

2015

Endtermin:

2018

Description

TimeStorm

TimeStorm aims at equipping artificial systems with humanlike cognitive skills that benefit from the flow of time by shifting the focus of human-machine confluence to the temporal, short- and long-term aspects of symbiotic interaction. The integrative pursuit of research and technological developments in time perception will contribute significantly to ongoing efforts in deciphering the relevant brain circuitry and will also give rise to innovative implementations of artifacts with profoundly enhanced cognitive capacities. TimeStorm promotes time perception as a fundamental capacity of autonomous living biological and computational systems that plays a key role in the development of intelligence. In particular, time is important for encoding, revisiting and exploiting experiences (knowing), for making plans to accomplish timely goals at certain moments (doing), for maintaining the identity of self over time despite changing contexts (being).

The main role of KIT in TimeStorm is to investigate the temporal information in the perception and execution of manipulation actions and to integrate time processing mechanisms in humanoid robots. In particular, we investigate how semantic representation (top-down) and hierarchical segmentation (bottom-up) of human demonstrations based on spatio-temporal object interactions can be combined to facilitate generalization of action durations. This would allow a robot to scale perceived and learnt temporal information of an action in order to perform the same and other actions with various temporal lengths.