title: Toward Self-Referential Autonomous Learning of Object and Situation Models.
abstract: Most current approaches to scene understanding lack the capability to adapt object and situation models to behavioral needs not anticipated by the human system designer. Here, we give a detailed description of a system architecture for self-referential autonomous learning which enables the refinement of object and situation models during operation in order to optimize behavior. This includes structural learning of hierarchical models for situations and behaviors that is triggered by a mismatch between expected and actual action outcome. Besides proposing architectural concepts, we also describe a first implementation of our system within a simulated traffic scenario to demonstrate the feasibility of our approach.
authors: Control Theory and Robotics, Technical University of Darmstadt, 64283 Darmstadt, Germany.
authors: Florian
authors: F
authors: Damerow
authors: Faculty of Computer Science, Albstadt-Sigmaringen University, 72458 Albstadt, Germany.|Honda Research Institute Europe GmbH, Carl-Legien-Straße 30, 63073 Offenbach, Germany.
authors: Andreas
authors: A
authors: Knoblauch
authors: Honda Research Institute Europe GmbH, Carl-Legien-Straße 30, 63073 Offenbach, Germany.
authors: Ursula
authors: U
authors: Körner
authors: Honda Research Institute Europe GmbH, Carl-Legien-Straße 30, 63073 Offenbach, Germany.
authors: Julian
authors: J
authors: Eggert
authors: Honda Research Institute Europe GmbH, Carl-Legien-Straße 30, 63073 Offenbach, Germany.
authors: Edgar
authors: E
authors: Körner
keywords: Autonomous learning; Hierarchical situation model; Scene understanding; Self-referential control