Statistical Learning for Humanoid Robots

The complexity of the kinematic and dynamic structure of humanoid robots make conventional analytical approaches to control increasingly unsuitable for such systems. Learning techniques offer a possible way to aid controller design if insufficient analytical knowledge is available, and learning approaches seem mandatory when humanoid systems are supposed to become completely autonomous. While recent research in neural networks and statistical learning has focused mostly on learning from finite data sets without stringent constraints on computational efficiency, learning for humanoid robots requires a different setting, characterized by the need for real-time learning performance from an essentially infinite stream of incrementally arriving data. This paper demonstrates how even high-dimensional learning problems of this kind can successfully be dealt with by techniques from nonparametric regression and locally weighted learning. As an example, we describe the application of one of the ...

• Type: Journal
• Year: 2002
• Where: AROBOTS
• Authors: Sethu Vijayakumar, Aaron D'Souza, Tomohiro Shibata, Jörg Conradt, Stefan Schaal
• Keywords: AROBOTS 2002, Humanoid Robots, Real-time Learning, Real-time Learning Performance