Tiago Vaquero

A focus group study on the design considerations and impressions of a socially assistive robot for long-term care

As older adults age, they are more likely to reside in long-term care facilities due to the decline in cognitive and/or physical abilities that prevent them from living independently. With a rapidly aging population there is an increasing demand on long-term care facilities to care for older adults. Such facilities need to provide medical services, assistance in activities of daily living, and scheduled leisure activities to improve health and quality of life. However, as the need for long-term care is increasing, the care workforce is faced with decreasing numbers of healthcare staff and high turnover rates. Our research focuses on the design of socially assistive robots to plan, schedule, and facilitate social and cognitive interventions for residents in long-term care facilities. In this paper, we investigate the specific design considerations and the impressions of long-term care residents, healthcare professionals, and family members on a socially assistive robot designed to autonomously facilitate cognitively and socially stimulating leisure activities. Thematic analysis of focus group sessions conducted at a long-term care facility with the aforementioned individuals revealed important design considerations for the development and integration of a socially assistive robot in long-term care facilities.

Post-design analysis for building and refining AI planning systems

The growth of industrial applications of artificial intelligence has raised the need for design tools to aid in the conception and implementation of such complex systems. The design of automated planning systems faces several engineering challenges including the proper modeling of the domain knowledge: the creation of a model that represents the problem to be solved, the world that surrounds the system, and the ways the system can interact with and change the world in order to solve the problem. Knowledge modeling in AI planning is a hard task that involves acquiring the system requirements and making design decisions that can determine the behavior and performance of the resulting system. In this paper we investigate how knowledge acquired during a post-design phase of modeling can be used to improve the prospective model. A post-design framework is introduced which combines a knowledge engineering tool and a virtual prototyping environment for the analysis and simulation of plans. This framework demonstrates that post-design analysis supports the discovery of missing requirements and can guide the model refinement cycle. We present three case studies using benchmark domains and eight state-of-the-art planners. Our results demonstrate that significant improvements in plan quality and an increase in planning speed of up to three orders of magnitude can be achieved through a careful post-design process. We argue that such a process is critical for the deployment of AI planning technology in real-world engineering applications.

Towards Architecture-wide Analysis, Verification, and Validation for Total System Stability During Goal-Seeking Space Robotics Operations

In this paper we discuss the beginnings of an attempt to define and analyze the stability of an entire modular robotic system architecture - one which includes a three-tier (3T) layer breakdown of ...

Robots in Retirement Homes: Applying Off-the-Shelf Planning and Scheduling to a Team of Assistive Robots (Extended Abstract)

We investigate Constraint Programming and Planning Domain Definition Language-based technologies for planning and scheduling multiple robots in a retirement home environment to assist elderly residents. Our robotics problem and investigation into proposed solution approaches provide a real world application of planning and scheduling, while highlighting the different modeling assumptions required to solve such a problem. This information is valuable to the planning and scheduling community as it provides insight into potential application avenues, in particular for robotics problems. Based on empirical results, we conclude that a constraint-based scheduling approach, specifically a decomposition using constraint programming, provides the most promising results for our application.

Faster Conflict Generation for Dynamic Controllability

In this paper, we focus on speeding up the temporal plan relaxation problem for dynamically controllable systems. We take a look at the current bestknown algorithm for determining dynamic controllability and augment it to efficiently generate conflicts when the network is deemed uncontrollable. Our work preserves the O(n) runtime of the best available dynamic controllability checker and improves on the previous best runtime of O(n) for extracting dynamic controllability conflicts. We then turn our attention to temporal plan relaxation tasks and show how we can leverage our work on conflicts and the structure of the network to efficiently make incremental updates intended to restore dynamic controllability by relaxing constraints. Our new algorithm, RELAXIDC, has the same asymptotic runtime as previous algorithms but sees dramatic empirical improvements over the course of repeated dynamic controllability checks.