Statistical Learning for Humanoid Robots
Sethu Vijayakumar,Aaron D'Souza, Tomohiro Shibata, Jorg Conradt and Stefan Schaal
Abstract of paper published in Autonomous 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 most advanced of such algorithms, Locally Weighted Projection
Regression (LWPR), to the on-line learning of three problems in
humanoid motor control: the learning of inverse dynamics models for
model-based control, the learning of inverse kinematics of redundant
manipulators, and the learning of oculomotor reflexes. All these
examples demonstrate fast, i.e., within seconds or minutes, learning
convergence with highly accurate final peformance. We conclude that
real-time learning for complex motor system like humanoid robots is
possible with appropriately tailored algorithms, such that
increasingly autonomous robots with massive learning abilities should
be achievable in the near future
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