PARC: A Plan and Activity Recognition Component for Assistive Robots

Abstract

Mobile robot assistants have many applications, such as helping people in their daily living activities. These robots have to detect and recognize the actions and goals of the humans they are assisting. While there are several wide-spread plan and activity recognition solutions for controlled environments with many built-in sensors, like smart-homes, there is a lack of such systems for mobile robots operating in open settings, such as an apartment. We propose a module for the recognition of activities and goals for daily living by mobile robots, in real time and for complex activities. Our approach recognizes human-object interaction using an RGB-D camera to infer low-level actions which are sent to a goal recognition algorithm. Results show that our approach is both in real time and requires little computational resources, which facilitates its deployment on a mobile and low-cost robotics platform.