A Survey of Knowledge Representation in Service Robotics

Within the realm of service robotics, researchers have placed a great amount of effort into learning, understanding, and representing motions as manipulations for task execution by robots. The task of robot learning and problem-solving is very broad, as it integrates a variety of tasks such as object detection, activity recognition, task/motion planning, localization, knowledge representation and retrieval, and the intertwining of perception/vision and machine learning techniques. In this paper, we solely focus on knowledge representations and notably how knowledge is typically gathered, represented, and reproduced to solve problems as done by researchers in the past decades. In accordance with the definition of knowledge representations, we discuss the key distinction between such representations and useful learning models that have extensively been introduced and studied in recent years, such as machine learning, deep learning, probabilistic modelling, and semantic graphical structures. Along with an overview of such tools, we discuss the problems which have existed in robot learning and how they have been built and used as solutions, technologies or developments (if any) which have contributed to solving them. Finally, we discuss key principles that should be considered when designing an effective knowledge representation.Comment: Accepted for RAS Special Issue on Semantic Policy and Action Representations for Autonomous Robots - 22 Page

Healthcare Cost Savings Through Telemedicine Use At Correctional State Facilities

Telemedicine offers both soft and hard return on investment, including cost savings avoidance and convenience of access to care. Incarcerated individuals represent a patient population that uniquely benefit from receiving care via telemedicine. They lack access to subspecialty care as prison facilities are located outside of urban areas, which is compounded by security risks, risk to individuals around inmates, and transportation cost to tertiary care facilities. To attend a brief in-office medical visit, an inmate requires hours of administrative support and logistical coordination, including appointment scheduling, transport arrangement and related fuel expense, and guard accompaniment - all at a financial cost to taxpayers. Telemedicine stands as a proven solution to decrease these costs and improve access to the care of inmates. The Virginia Commonwealth University (VCU) Office of Telemedicine has provided telemedicine visits to more than 45,000 patients over 22 years and encompasses over 15 subspecialties, which have facilitated care to incarcerated patients at 30 Department of Corrections (DOC) sites in Virginia. Cost savings analysis was performed by the VCU Office of Telemedicine for the 2016 fiscal year. The amount saved per telemedicine visit was estimated by calculating officer costs and transportation costs associated with transporting an inmate to an on-site visit. It was found that each telemedicine visit represents a cost avoidance of $800 per visit. There were 2,850 Virginia DOC telemedicine visits in the fiscal year 2016, resulting in over 2 million dollars in estimated cost savings.https://scholarscompass.vcu.edu/gradposters/1034/thumbnail.jp

Long-Horizon Task Planning and Execution with Functional Object-Oriented Networks

Following work on joint object-action representation, functional object-oriented networks (FOON) were introduced as a knowledge representation for robots. A FOON contains symbolic (high-level) concepts useful to a robot's understanding of tasks and its environment for object-level planning. Prior to this work, little has been done to show how plans acquired from FOON can be executed by a robot, as the concepts in a FOON are too abstract for immediate execution. We propose a hierarchical task planning approach that translates a FOON graph into a PDDL-based representation of domain knowledge for task planning and execution. As a result of this process, a task plan can be acquired, which can be executed by a robot from start to end, leveraging the use of action contexts and skills as dynamic movement primitives (DMPs). We demonstrate the entire pipeline from planning to execution using CoppeliaSim and show how learned action contexts can be extended to never-before-seen scenarios.Comment: Preliminary Draft, 8 pages, IEEE Conference Forma

Manipulation Motion Taxonomy and Coding for Robots

This paper introduces a taxonomy of manipulations as seen especially in cooking for 1) grouping manipulations from the robotics point of view, 2) consolidating aliases and removing ambiguity for motion types, and 3) provide a path to transferring learned manipulations to new unlearned manipulations. Using instructional videos as a reference, we selected a list of common manipulation motions seen in cooking activities grouped into similar motions based on several trajectory and contact attributes. Manipulation codes are then developed based on the taxonomy attributes to represent the manipulation motions. The manipulation taxonomy is then used for comparing motion data in the Daily Interactive Manipulation (DIM) data set to reveal their motion similarities.Comment: IROS 2019 Submission -- 6 page