Actions description languages (ADLs), such as STRIPS, PDDL, and RDDL specify the input format for planning algorithms. Unfortunately, their syntax is familiar to planning experts only, and not to potential users of planning technology. Moreover, this syntax limits the ability to describe complex and large domains. We argue that programming languages (PLs), and more specifically, probabilistic programming languages (PPLs), provide a more suitable alternative. PLs are familiar to all programmers, support complex data types and rich libraries for their manipulation, and have powerful constructs, such as loops, sub-routines, and local variables with which complex, realistic models and complex objectives can be simply and naturally specified. PPLs, specifically, make it easy to specify distributions, which is essential for stochastic models. The natural objection to this proposal is that PLs are opaque and too expressive, making reasoning about them difficult. However, PPLs also come with efficient inference algorithms, which, coupled with a growing body of work on sampling-based and gradient-based planning, imply that planning and execution monitoring can be carried out efficiently in practice. In this paper, we expand on this proposal, illustrating its potential with examples.

To enable robots to achieve high level objectives, engineers typically write scripts that apply existing specialized skills, such as navigation, object detection and manipulation to achieve these goals. Writing good scripts is challenging since they must intelligently balance the inherent stochasticity of a physical robot's actions and sensors, and the limited information it has. In principle, AI planning can be used to address this challenge and generate good behavior policies automatically. But this requires passing three hurdles. First, the AI must understand each skill's impact on the world. Second, we must bridge the gap between the more abstract level at which we understand what a skill does and the low-level state variables used within its code. Third, much integration effort is required to tie together all components. We describe an approach for integrating robot skills into a working autonomous robot controller that schedules its skills to achieve a specified task and carries four key advantages. 1) Our Generative Skill Documentation Language (GSDL) makes code documentation simpler, compact, and more expressive using ideas from probabilistic programming languages. 2) An expressive abstraction mapping (AM) bridges the gap between low-level robot code and the abstract AI planning model. 3) Any properly documented skill can be used by the controller without any additional programming effort, providing a Plug'n Play experience. 4) A POMDP solver schedules skill execution while properly balancing partial observability, stochastic behavior, and noisy sensing.

8 at. % yttria stabilized zirconia (8YSZ), doped with 0.25 at. % Mn, was deposited on a Si (100) substrate using radio frequency magnetron sputtering. The film was characterized by scanning electron microscopy, energy dispersive spectroscopy, and x-ray diffraction (XRD). They were used in order to obtain information regarding the composition and uniformity of the samples, determine their crystal structure, and measure the film thicknesses, respectively. In addition, the kinetics of the YSZ phase growth was investigated. Several samples were heat treated for 1 h in air at various temperatures in the range of 750–900 °C, which made it possible to estimate the apparent activation energy of the process. The activation energies were determined by intensity change and thickness variation with temperature. A decrease in film thickness was observed, and the growth of the YSZ phase showed low activation energies of 0.61 and 0.51 eV by thickness and intensity measurements, respectively. As expected, the composition complies with the composition of 8YSZ since the sputtering target was 8YSZ. No XRD shift of peaks was observed relative to undoped 8YSZ, apparently because the concentration of Mn was low.