Creating an Interface for Fluent-Based Task Planning for Robotic Object Search in Household Environments

Abstract

Robot navigation and object search within household environments are foundational tasks in the robotics community. Current research focuses on improving the planning algorithms that robots use to complete such tasks. However, there is a need in the RAIL Group for an interface between other disparate technologies: the planner, which expects an abstract representation of the state, and the environment itself—either a simulation of a household environment or a physical robot operating in the lab. Therefore, the goal is to establish a connection between the planner and the robot by deriving the up-to-date symbolic representation of the environment as the robot explores. Such a connection allows the planner to be used in closed-loop deployments, affording use of the RAIL Group’s novel approach to learning-informed decision making. Abstract planners require that the state be described via a union of "fluents," each a predicate function representing some aspect of what's true about the world. Our interface determines which fluents are active in a given state via the robot’s partial map of the environment. Based on the active fluents, the planner will determine which action it should take in the given state, before returning that action to the interface. Upon receiving the planner's chosen action, the interface executes it in the environment, then proceeds to recalculate the active fluents. This interface will make it possible to use this planning tool, critical to the RAIL Group’s aims for effective task planning under uncertainty.