UC San Diego

Service robots are designed to assist and aid humans in the day to day tasks as well as specific and minute tasks. In recent years there has been an increase in both demand and production of service robots. These robots are slowly becoming more prolific both in personal settings such as the home, and professional settings such as restaurants and hospitals. Despite this growth, there are still major challenges preventing service robots from being ubiquitous and operating intelligently in these unstructured domestic environments.

Like many autonomous robots, the technical challenges of service robots revolve around three main facets: sensing and modeling their environment, understanding and reasoning about said environment, and acting in a way that is safe and conducive towards a task goal. Unlike industry robots, service robots operate in dynamic environments and have tasks that cannot always be completed repetitively. This presents a rich set of challenges to solve in all three areas.

One exemplary task of this for service robots is tidying objects within the home. This task is important as it helps assist with activity of daily living (ADL) while meeting consumer demand. A few concrete illustrations of the challenges of this task are shown when we consider the robot must sense objects and ground language of these objects before we can determine where the objects belong. Notions of a correct placement vary as each user has their own personal preference to organization. Grasping and manipulating the objects as well as navigating within the environment must also be accomplished in order to successfully complete the task.

In this dissertation we present several contributions that help address these challenges and the surrounding gaps towards solving these issues. First, we explore the use of a service robot to put away the groceries in a precise and stable configuration that satisfies the desired semantic relationship of grocery and pantry objects. Second we explore the ability of our robot to put away objects throughout a home environment based on a user preference using a recommender system approach. We share details of our implementation of these systems through real-world experiments.