mbocamazo's blog

Blag Weeks 8-11: I Heart Gauss

The team gained a greater appreciation for Johann Carl Friedrich in the implementation of Gaussian Process Regression with basis functions. GPR was chosen to replace LWPR because of LWPR’s failure to generalize outside of the state space already explored. The inclusion of basis functions in GPR will allow global semi-parametric modeling of system dynamics with Gaussian Processes modeling the local residuals. We adopted the code and book from Gaussian Processes for Machine Learning by Carl Edward Rasmussen and Chris Williams.

Blag Weeks 6, 7: A Hill Too Steep to Climb

Sometimes models don’t generalize well. The team’s path met a gradient too great to climb: LWPR could not generalize the local models (built with receptive fields) to broader regions of the state space for use in the controller. This makes sense, in hindsight, because LWPR is an efficient high-dimensional function approximation approach. It performed worse than regularized linear regression on the forward model identification task, with and without additional basis functions.

Blag Weeks 4, 5: The Implementation Battle Progresses

The Lifelong Learning team is closing in on the SysID-Control-Simulation problem.

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