Cognitive, neural, and social mechanisms of rhythmic interpersonal coordination

Humans possess the exceptional capacity to temporally coordinate their movements with one another with a high degree of accuracy, precision, and flexibility. Musical ensemble performance is a refined example of this, where a range of cognitive and sensory-motor processes work together to support rhythmic interpersonal coordination. However, the influence of social factors on the underlying cognitive-motor and neural mechanisms that facilitate rhythmic interpersonal coordination is yet to be established. This thesis draws on theoretical perspectives related to joint action, including co-representation, self-other integration and segregation, and theoretical models of sensorimotor synchronisation to consider this topic. Three experiments were conducted to investigate how social factors influence rhythmic interpersonal coordination. This broad empirical question was broken down by considering both extrinsic factors—such as the social context and perceived characteristics of an interaction partner (e.g. the degree of partner intentionality and responsiveness)—as well as intrinsic social factors, such as individual differences in attitudes and social preferences. This thesis concludes that extrinsic and intrinsic social factors affect rhythmic interpersonal coordination at multiple levels. A key aspect of this influence relates to how people regulate the integration and segregation of their representations of self and others. However, importantly, these effects are mediated by individual differences in intrinsic social factors such as personal preferences and biases. Top-down processes related to beliefs thus influence bottom-up sensorimotor processes during joint action, but the nature of this influence appears to be different for different people. This outcome highlights the necessity of taking individual differences into account, particularly when investigating the nuances of social processing during dynamic social interactions. Furthermore, the current findings suggest that beliefs about a partner during social interaction may be just as, or even more so, influential on performance than the actual characteristics of the partner. Recognising the potency of social beliefs has implications not only for research into basic psychological mechanisms underpinning rhythmic interpersonal coordination, but also for understanding the broader social dynamics of real-life situations involving cooperative joint action understanding the broader social dynamics of real-life situations involving cooperative joint action

Advancing automation and robotics technology for the Space Station Freedom and for the US economy

In April 1985, as required by Public Law 98-371, the NASA Advanced Technology Advisory Committee (ATAC) reported to Congress the results of its studies on advanced automation and robotics technology for use on the Freedom space station. This material was documented in the initial report (NASA Technical Memorandum 87566). A further requirement of the law was that ATAC follow NASA's progress in this area and report to Congress semiannually. This report is the seventh in a series of progress updates and covers the period between April 1, 1988 and September 30, 1988. NASA has accepted the basic recommendations of ATAC for its Space Station Freedom efforts. ATAC and NASA agree that the thrust of Congress is to build an advanced automation and robotics technology base that will support an evolutionary Space Station Freedom program and serve as a highly visible stimulator, affecting the U.S. long-term economy. The progress report identifies the work of NASA and the Freedom study contractors. It also describes research in progress, and it makes assessments of the advancement of automation and robotics technology on the Freedom space station

Improving human robot collaboration through Force/Torque based learning for object manipulation

Human–Robot Collaboration (HRC) is a term used to describe tasks in which robots and humans work together to achieve a goal. Unlike traditional industrial robots, collaborative robots need to be adaptive; able to alter their approach to better suit the situation and the needs of the human partner. As traditional programming techniques can struggle with the complexity required, an emerging approach is to learn a skill by observing human demonstration and imitating the motions; commonly known as Learning from Demonstration (LfD). In this work, we present a LfD methodology that combines an ensemble machine learning algorithm (i.e. Random Forest (RF)) with stochastic regression, using haptic information captured from human demonstration. The capabilities of the proposed method are evaluated using two collaborative tasks; co-manipulation of an object (where the human provides the guidance but the robot handles the objects weight) and collaborative assembly of simple interlocking parts. The proposed method is shown to be capable of imitation learning; interpreting human actions and producing equivalent robot motion across a diverse range of initial and final conditions. After verifying that ensemble machine learning can be utilised for real robotics problems, we propose a further extension utilising Weighted Random Forest (WRF) that attaches weights to each tree based on its performance. It is then shown that the WRF approach outperforms RF in HRC tasks.</p

Space station automation study: Automation requriements derived from space manufacturing concepts,volume 2

Automation reuirements were developed for two manufacturing concepts: (1) Gallium Arsenide Electroepitaxial Crystal Production and Wafer Manufacturing Facility, and (2) Gallium Arsenide VLSI Microelectronics Chip Processing Facility. A functional overview of the ultimate design concept incoporating the two manufacturing facilities on the space station are provided. The concepts were selected to facilitate an in-depth analysis of manufacturing automation requirements in the form of process mechanization, teleoperation and robotics, sensors, and artificial intelligence. While the cost-effectiveness of these facilities was not analyzed, both appear entirely feasible for the year 2000 timeframe

Proceedings of the NASA Conference on Space Telerobotics, volume 5

Papers presented at the NASA Conference on Space Telerobotics are compiled. The theme of the conference was man-machine collaboration in space. The conference provided a forum for researchers and engineers to exchange ideas on the research and development required for the application of telerobotics technology to the space systems planned for the 1990's and beyond. Volume 5 contains papers related to the following subject areas: robot arm modeling and control, special topics in telerobotics, telerobotic space operations, manipulator control, flight experiment concepts, manipulator coordination, issues in artificial intelligence systems, and research activities at the Johnson Space Center

Worker-robot cooperation and integration into the manufacturing workcell via the holonic control architecture

Cooperative manufacturing is a new field of research, which addresses new challenges beyond the physical safety of the worker. Those new challenges appear due to the need to connect the worker and the cobot from the informatics point of view in one cooperative workcell. This requires developing an appropriate manufacturing control system, which fits the nature of both the worker and the cobot. Furthermore, the manufacturing control system must be able to understand the production variations, to guide the cooperation between worker and the cobot and adapt with the production variations.Die kooperative Fertigung ist ein neues Forschungsgebiet, das sich neuen Herausforderungen stellt. Diese neuen Herausforderungen ergeben sich aus der Notwendigkeit, den Arbeiter und den Cobot aus der Sicht der Informatik in einem kooperativen Arbeitsplatz zu verbinden. Dies erfordert die Entwicklung eines geeigneten Produktionskontrollsystems, das sowohl der Natur des Arbeiters als auch der des Cobots entspricht. Darüber hinaus muss die Fertigungssteuerung in der Lage sein, die Produktionsschwankungen zu verstehen, um die Zusammenarbeit zwischen Arbeiter und Cobot zu steuern