An issue that has so far received only limited attention in probabilistic logic programming (PLP) is the modelling of so-called epistemic uncertainty, uncertainty about model itself. Accurately quantifying this paramount to robust inference, learning and ultimately decision making. We introduce BetaProbLog, a PLP language can uncertainty. BetaProbLog sound semantics, an effective inference algo...

A streaming probabilistic program receives a stream of observations and produces distributions that are conditioned on these observations. Efficient inference is often possible in context using Rao-Blackwellized particle filters (RBPFs), which exactly solve problems when fall back sampling approximations necessary. While RBPFs can be implemented by hand to provide efficient inference, the goal ...

In the context of learning from demonstration (LfD), trajectory policy representations such as probabilistic movement primitives (ProMPs) allow for rich modeling demonstrated skills. To reproduce a learned skill with real robot, feedback controller is required to cope perturbations and react dynamic changes in environment. this letter, we propose generalized control approach that merges movemen...