A good overview would fill up a post on its own, but some relevant topics are given below. I don't think any of it is behind a paywall, but if it is, let me know and I'll link to another article on the same topic. In cases where I learned about the topic by word of mouth, I haven't necessarily read the provided paper, so I can't guarantee the quality for all of these. I generally tried to pick papers that either gave a survey of progress or solved a specific clearly interesting problem. As a result you might have to do some additional reading to understand some of the articles, but hopefully this is a good start until I get something more organized up.

Learning:

Online concept learning: rational rules for concept learning [a somewhat idealized situation but a good taste of the sorts of techniques being applied]

Learning categories: Bernoulli mixture model for document classification, spatial pyramid matching for images

Learning category hierarchies: nested Chinese restaurant process, hierarchical beta process

Learning HMMs (hidden Markov models): HDP-HMMs this is pretty new so the details haven't been hammered out, but the article should give you a taste of how people are approaching the problem, although I also haven't read this article; I forget where I read about HDP-HMMs, although another paper on HDPs is this one. I think the original article I read was one of Erik Sudderth's, which are here. Another older algorithm is the Baum-Welch algorithm.

Learning image characteristics: deep Boltzmann machines

Handwriting recognition: hierarchical Bayesian approach, basically the same as the previous research

Learning graphical models: a survey paper

Planning:

Planning in MDPs: value iteration, plus LQR trees for many physical systems

Planning in POMDPs: I don't actually know much about this; my impression is that we need to do more work in this area, but approaches include reinforcement learning. A couple interesting papers: Bayes risk approach, plus a survey of hierarchical methods