User—robot personality matching and assistive robot behavior adaptation for post-stroke rehabilitation therapy

This paper describes a hands-off socially assistive therapist robot designed to monitor, assist, encourage, and socially interact with post-stroke users engaged in rehabilitation exercises. We investigate the role of the robot’s personality in the hands-off therapy process, focusing on the relationship between the level of extroversion–introversion of the robot and the user. We also demonstrate a behavior adaptation system capable of adjusting its social interaction parameters (e.g., interaction distances/proxemics, speed, and vocal content) toward customized post-stroke rehabilitation therapy based on the user’s personality traits and task performance. Three validation experiment sets are described. The first maps the user’s extroversion–introversion personality dimension to a spectrum of robot therapy styles that range from challenging to nurturing. The second and the third experiments adjust the personality matching dynamically to adapt the robot’s therapy styles based on user personality and performance. The reported results provide first evidence for user preference for personality matching in the assistive domain and demonstrate how the socially assistive robot’s autonomous behavior adaptation to the user’s personality can result in improved human task performance. This work was supported by USC Women in Science and Engineering (WiSE) Program and the Okawa Foundation.     

Towards an intelligent system for generating an adapted verbal and nonverbal combined behavior in human–robot interaction

In human–robot interaction scenarios, an intelligent robot should be able to synthesize an appropriate behavior adapted to human profile (i.e., personality). Recent research studies discussed the effect of personality traits on human verbal and nonverbal behaviors. The dynamic characteristics of the generated gestures and postures during the nonverbal communication can differ according to personality traits, which similarly can influence the verbal content of human speech. This research tries to map human verbal behavior to a corresponding verbal and nonverbal combined robot behavior based on the extraversion–introversion personality dimension. We explore the human–robot personality matching aspect and the similarity attraction principle, in addition to the different effects of the adapted combined robot behavior expressed through speech and gestures, and the adapted speech-only robot behavior, on interaction. Experiments with the humanoid NAO robot are reported.     

Socially assistive robotics [Grand Challenges of Robotics]

Socially intelligent robotics is the pursuit of creating robots capable of exhibiting natural-appearing social qualities. Beyond the basic capabilities of moving and acting autonomously, the field has focused on the use of the robot's physical embodiment to communicate and interact with users in a social and engaging manner. One of its components, socially assistive robotics, focuses on helping human users through social rather than physical interaction. Early results already demonstrate the promises of socially assistive robotics, a new interdisciplinary research area with large horizons of fascinating and much needed research. Even as socially assistive robotic technology is still in its early stages of development, the next decade promises systems that will be used in hospitals, schools, and homes in therapeutic programs that monitor, encourage, and assist their users. This is an important time in the development of the field, when the board technical community and the beneficiary populations must work together to shape the field toward its intended impact on improved human quality of life     

Optimizing the parallel computation of linear recurrences using compact matrix representations

This paper presents a novel method for optimizing the parallel computation of linear recurrences. Our method can help reduce the resource requirements for both memory and computation. A unique feature of our technique is its formulation of linear recurrences as matrix computations, before exploiting their mathematical properties for more compact representations. Based on a general notion of closure for matrix multiplication, we present two classes of matrices that have compact representations. These classes are permutation matrices and matrices whose elements are linearly related to each other. To validate the proposed method, we experiment with solving recurrences whose matrices have compact representations using CUDA on nVidia GeForce 8800 GTX GPU. The advantages of our technique are that it enables the computation of larger recurrences in parallel and it provides good speedups of up to eleven times over the un-optimized parallel computations. Also, the memory usage can be as much as nine times lower than that of the un-optimized parallel computations. Our result confirms a promising approach for the adoption of more advanced parallelization techniques.