Key challenges to widespread deployment of mobile robots include collaboration and the ability to tailor sensing and information processing to the task at hand. Partially observable Markov decision processes (POMDPs), which are an instance of probabilistic sequential decision-making, can be used to address these challenges in domains characterized by partial observability and nondeterministic action outcomes. However, such formulations tend to be computationally intractable for domains that have large complex state spaces and require robots to respond to dynamic changes. This paper presents a hierarchical decomposition of POMDPs that incorporates adaptive observation functions, constrained convolutional policies, and automatic belief propagation, enabling robots to retain capabilities for different tasks, direct sensing to relevant locations, and determine the sequence of sensing and processing algorithms best suited to any given task. A communication layer is added to the POMDP hierarchy for belief sharing and collaboration in a team of robots. All algorithms are evaluated in simulation and on physical robots, localizing target objects in dynamic indoor domains.

Casiers are a class of tangible interface elements that structure the physical and functional composition of tangibles and complementary interactors (e.g., buttons and sliders). Casiers allow certain subsets of interactive functionality to be accessible across diverse interactive systems (with and without graphical mediation, employing varied sensing capabilities and supporting software). We illustrate examples of casiers in use, including iterations around a custom walk-up-and-use kiosk, as well as casiers operable across com- mercial platforms of widely varying cost and capability.