Model-based control of a robot manipulator
Model-based control of a robot manipulator
Artificial Intelligence Review - Special issue on lazy learning
Bayesian learning for neural networks
Bayesian learning for neural networks
The Bayesian backfitting relevance vector machine
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Incremental Online Learning in High Dimensions
Neural Computation
Constructive Incremental Learning from Only Local Information
Neural Computation
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For complex robots such as humanoids, model-based control is highly beneficial for accurate tracking while keeping negative feedback gains low for compliance. However, in such multi degree-of-freedom lightweight systems, conventional identification of rigid body dynamics models using CAD data and actuator models is inaccurate due to unknown nonlinear robot dynamic effects. An alternative method is data-driven parameter estimation, but significant noise in measured and inferred variables affects it adversely. Moreover, standard estimation procedures may give physically inconsistent results due to unmodeled nonlinearities or insufficiently rich data. This paper addresses these problems, proposing a Bayesian system identification technique for linear or piecewise linear systems. Inspired by Factor Analysis regression, we develop a computationally efficient variational Bayesian regression algorithm that is robust to ill-conditioned data, automatically detects relevant features, and identifies input and output noise. We evaluate our approach on rigid body parameter estimation for various robotic systems, achieving an error of up to three times lower than other state-of-the-art machine learning methods.