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• W2893357027 abstract "The autonomous acquisition of sensory-motor skills along multiple developmental stages is one of the current challenges in robotics. To this end, we propose a new developmental cognitive architecture that combines multipurpose predictors and principles of self-verification for the robust bootstrapping of sensory-motor skills. Our architecture operates with loops formed by both mental simulation of sensory-motor sequences and their subsequent physical trial on a robot. During these loops, verification algorithms monitor the predicted and the physically observed sensory-motor data. Multiple types of predictors are acquired through several developmental stages. As a result, the architecture can select and plan actions, adapt to various robot platforms by adjusting proprioceptive feedback, predict the risk of self-collision, learn from a previous interaction stage by validating and extracting sensory-motor data for training the predictor of a subsequent stage, and finally acquire an internal representation for evaluating the performance of its predictors. These cognitive capabilities in turn realize the bootstrapping of early hand-eye coordination and its improvement. We validate the cognitive capabilities experimentally and, in particular, show an improvement of reaching as an example skill." @default.
• W2893357027 created "2018-10-05" @default.
• W2893357027 creator A5017740442 @default.
• W2893357027 creator A5048975019 @default.
• W2893357027 date "2018-12-01" @default.
• W2893357027 modified "2023-09-27" @default.
• W2893357027 title "A Self-Verifying Cognitive Architecture for Robust Bootstrapping of Sensory-Motor Skills via Multipurpose Predictors" @default.
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• W2893357027 doi "https://doi.org/10.1109/tcds.2018.2871857" @default.
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