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Modellfreies bestärkendes Lernen bei Robotern

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Eine neue Form des modellfreien bestärkenden Lernens wurde beim Mini-Hunderoboter Cheetah eingesetzt, der von Grund auf per Versuch-und-Irrtum-Verfahren in einer Computer-Simulation in vielen verschiedenen Umgebungen trainiert wurde. Der Roboter-Hund startete das Bewegungslernen in der Simulation ohne menschliches Vorwissen und Regeln und erstellte sich ein eigenes Modell, das er kontinuierlich ausbaut und optimiert. Das in der Simulation gelernte Bewegungswissen kann Cheetah in der Realität wieder abrufen und stellte neben neuen Höchstgeschwindigkeiten von bis zu 14 km/h und einer enormen Agilität auch ein besonderes Bewegungstalent auf schwierigem Terrain unter Beweis. Cheetah bewegt sich viel sicherer und dynamischer auf Kieselsteinen, selbst wenn er bergab rennt und dabei permanent leicht auf den Steinen ausrutscht. Sogar eine vereiste Stelle auf der Straße bringt den Mini-Hunderoboter nicht aus dem Tritt.