Lately I've spent my time reading about neuroscience and biologically-realistic learning algorithms (and doing classwork, of course).
I skimmed through "Biophysics of Computation" by Chrisfof Koch. It describes the low-level electrical and chemical properties of neurons and synapses. Pretty good stuff. I especially like how he shows the similarities between certain neural structures and well-known electrical circuits. I'm also reading a bunch of articles out of "The Handbook of Brain Theory and Neural Networks" by Michael Arbib. (I have the 1st edition from the school library, but I'm trying to get the second edition since it's a lot newer and supposedly updated a lot.) It has a lot of good short articles on neural reinforcement learning and motor control.
I'm also reading Russell Smith's (the guy who wrote Open Dynamics Engine) PhD thesis, "Intelligent Motion Control with an Artificial Cerebellum." This contains a great overview of the human motor control system (especially the cerebellum) and other motion control research. He extends a system called the CMAC controller. I can't explain it yet since I just started reading it, but I think it'll be pretty good.
One more thing I'm doing that will probably pertain to Verve... I'm working with some other students on an abstract simulated physics interface (OPAL: Open Physics Abstraction Layer). This will be a high-level interface to physics engines. The purposes of this are: 1) to have an abstract interface that can be extended to work with any physics engine, and 2) to have a simple API that does tons of cool things automatically with relatively few function calls. Open Dynamics Engine, for instance, gives developers total control over a ton of parameters, but it takes a while to learn to use it well. Hopefully OPAL will alleviate this problem. I hope to use OPAL to make a lot of good Verve demos.