We present the design and analysis of a multi-session user study conducted with older adults to evaluate the effectiveness of a human-robot interaction (HRI) framework utilizing a neuroscience-inspired spatial language interpretation approach. The user study was designed to: 1) evaluate the effectiveness and feasibility of the spatial language interpretation framework with target users, and 2) collect data on the types of phrases, responses, and format of natural language instructions given by target users to help inform possible modifications to the HRI framework. In addition, an expanded study was conducted on Amazon's Mechanical Turk to gather further natural language instructions from general users for analysis. The results of both studies, evaluated across a variety of objective performance and participant evaluation measures, demonstrate the feasibility of the approach and its effectiveness in interpreting and following natural language instructions from target users, achieving high task success rates and high participant evaluations across multiple measures.

We present a methodology for enabling service robots to interpret spatial language instruction sequences expressed through natural language discourse from non-expert users. As part of our approach, we propose a novel probabilistic algorithm for the automatic extraction of contextually and semantically valid instruction sequences from unconstrained spatial language discourse. Additionally, we present the design and implementation details of a procedure for reference resolution of anaphoric expressions encountered within the user discourse. Towards application of our human-robot interaction (HRI) methodology on robot platforms in practice with end users, we discuss a generalized procedure for transfer to physical systems and provide solutions for key pragmatic considerations including the generation of safe robot execution paths for both the robot and people in the environment. The paper concludes with an evaluation of our spatial language-based HRI framework implemented on a PR2 robot to demonstrate the generalizability and usefulness of our approach in real world applications.

We present a methodology for enabling service robots to follow natural language commands from non-expert users, with and without user-specified constraints, with a particular focus on spatial language understanding. As part of our approach, we propose a novel extension to the semantic field model of spatial prepositions that enables the representation of dynamic spatial relations involving paths. The design, system modules, and implementation details of our robot software architecture are presented and the relevance of the proposed methodology to interactive instruction and task modification through the addition of constraints is discussed. The paper concludes with an evaluation of our robot software architecture implemented on a simulated mobile robot operating in both a 2D home environment and in real world environment maps to demonstrate the generalizability and usefulness of our approach in real world applications.

We present a methodology for the representation of dynamic spatial relations (DSRs) with global properties as part of an approach for enabling robots to follow natural language commands from non-expert users, with particular focus on the development of spatial language primitives. Our approach to modeling DSRs is based on related research in the fields of linguistics, cognitive science, and neuroscience, and contributes novel extensions to the semantic field model of spatial prepositions. We describe novel representations of the DSRs for “to”, “through”, and “around”, discuss their applicability in path classification scenarios, and provide implementation details of path generation routines instantiating these DSRs for use in robot task planning. The paper concludes with an evaluation of our robot architecture implemented on a simulated mobile robot in a 2D home environment.

We present a socially assistive robot (SAR) system designed to engage elderly users in physical exercise. We discuss the system approach, design methodology, and implementation details, which incorporate insights from psychology research on intrinsic motivation, and we present five clear design principles for SAR-based therapeutic interventions. We then describe the system evaluation, consisting of a multi-session user study with older adults (n = 33), to evaluate the effectiveness of our SAR exercise system and to investigate the role of embodiment by comparing user evaluations of similar physically and virtually embodied coaches. The results validate the system approach and effectiveness at motivating physical exercise in older adults according to a variety of user performance and outcomes measures. The results also show a clear preference by older adults for the physically embodied robot coach over the virtual coach in terms of enjoyableness, helpfulness, and social attraction, among other factors.

In this paper, we present the design, implementation, and user study evaluation of a socially assistive robot (SAR) system designed to engage elderly users in physical exercise aimed at achieving health benefits and improving quality of life. We discuss our design methodology, which incorporates insights from psychology research in the area of intrinsic motivation, and focuses on maintaining engagement through personalized social interaction. We describe two user studies conducted to test the motivation theory in practice with our system. The first study investigated the role of praise and relational discourse in the exercise system by comparing a relational robot coach to a nonrelational robot coach. The second study evaluated participant preferences regarding user choice in the task scenario. Both studies served to evaluate the feasibility and overall effectiveness of the robot exercise system. The results of both studies are presented; they show a strong user preference for the relational over the nonrelational robot in terms of enjoyableness, companionship, and as an exercise coach, varying user preferences regarding choice, and high user ratings of the system across multiple metrics. The outcomes of the presented user studies, brought together, support the motivational capabilities of the robot, and demonstrate the viability and usefulness of the system in motivating exercise in elderly users.

We describe the design and implementation of a socially assistive robot that monitors the performance of a user during a seated arm exercise scenario, with the purpose of providing motivation to the user to complete the task and to improve performance. The visual arm pose recognition procedure used by the robot in tracking user performance, the three exercise games, and the methodology behind the human-robot interaction dialogue are presented. A two-condition experimental study was conducted with elderly participants to test the feasibility and effectiveness of the robot exercise system, the results of which demonstrate the viability and usefulness of the system in motivating exercise among elderly users.

We describe the design and implementation of a socially assistive robot that is able to monitor the performance of a user during a combined mental and physical task, with the purpose of motivating the user to complete the task and to improve performance. A three-condition experimental study was constructed for evaluation of the robot and preliminary results of the robot's interaction with human participants are presented.

This paper describes an approach that performs visual object detection in real-time by combining the strength of processing the color segmented image along with that of the grayscale image of the same scene. This approach was developed with the annual RoboCup Competition in mind, specifically the 4-Legged League where teams of Sony AIBO robots compete in the game of soccer. The images used for processing were taken from the camera located in the head of the robots, and the objects of interest to be detected were the actual AIBO robots. We use color segmented images for producing initial hypotheses for the location of robots in the image, and grayscale images for final classification purposes. Using both representations to process a scene allows each to make up for the deficiencies of the other, and provides a good balance between fast processing time and high detection accuracy. We present our algorithms and show illustrative examples of their performance