Dynamic Action Selection Using Image Schema-based Reasoning for Robots

Dealing with robotic actions in uncertain environments has been demonstrated to be hard. Many classic planning approaches to robotic action make the closed world assumption, rendering them inefficient for everyday household activities, as they function without generalizability to other contexts or the ability to deal with unexpected changes. In contrast, humans robustly execute underspecified instructions in unfamiliar environments. In this paper, we initiate our research program where we propose the use of functional relations in the form of image-schematic micro-theories, formally represented in ISL𝐹 𝑂𝐿, to enrich action descriptors with semantic components. It builds on the body of work in embodied cognition showing that human conceptualization of action sequences is founded on abstract patterns learned from physical experiences in the form of spatiotemporal relationships between object, agents and environments. These theories are used to inform action selection mechanisms for behavioral robotics written in EL++ and we argue how these micro-patterns can be applied in a more general way to deal with underspecified action commands and commonsense problem-solving.