Solution to the problem of designing a safe configuration of a human upper limb robotic prosthesis

На сегодняшний день остается актуальной разработка методов контроля позиционирования роботических манипуляторов с помощью систем технического зрения (СТЗ) с целью обеспечения безопасности пациентов и медицинского персонала при работе с медицинскими роботизированными реабилитационными устройствами. Целью исследования было разработать метод повышения безопасности применения роботизированных медицинских реабилитационных устройств путем разработки и апробации алгоритма расчета угловых положений роботизированных манипуляторов или роботических протезов, применяемых в восстановительном лечении и позволяющих воспроизвести естественную траекторию перемещения руки человека под контролем СТЗ. Дано описание роботизированного манипулятора, использованного при проведении исследований, представлены существующие подходы к расчету угловых положений приводов, а также описание предлагаемого алгоритма. Приведены сравнительные результаты работы предлагаемого алгоритма и существующих методов расчета угловых положений приводов роботизированных манипуляторов (роботических протезов) и предполагаемые направления для его доработки

Modeling High-Dimensional Humans for Activity Anticipation using Gaussian Process Latent CRFs

Abstract—For robots, the ability to model human configura-tions and temporal dynamics is crucial for the task of anticipating future human activities, yet requires conflicting properties: On one hand, we need a detailed high-dimensional description of human configurations to reason about the physical plausibility of the prediction; on the other hand, we need a compact representation to be able to parsimoniously model the relations between the human and the environment. We therefore propose a new model, GP-LCRF, which admits both the high-dimensional and low-dimensional representation of humans. It assumes that the high-dimensional representation is generated from a latent variable corresponding to its low-dimensional representation using a Gaussian process. The gener-ative process not only defines the mapping function between the high- and low-dimensional spaces, but also models a distribution of humans embedded as a potential function in GP-LCRF along with other potentials to jointly model the rich context among humans, objects and the activity. Through extensive experiments on activity anticipation, we show that our GP-LCRF consistently outperforms the state-of-the-art results and reduces the predicted human trajectory error by 11.6%. I

Human-Humanoid Joint Haptic Table Carrying Task with Height Stabilization using Vision

International audienceIn this paper, a first step is taken towards using vision in human-humanoid haptic joint actions. Haptic joint actions are characterized by physical interaction throughout the execution of a common goal. Because of this, most of the focus is on the use of force/torque-based control. However, force/torque information is not rich enough for some tasks. Here, a particular case is shown: height stabilization during table carrying. To achieve this, a visual servoing controller is used to generate a reference trajectory for the impedance controller. The control law design is fully described along with important considerations for the vision algorithm and a framework to make pose estimation robust during the table carrying task of the humanoid robot. We then demonstrate all this by an experiment where a human and the HRP-2 humanoid jointly transport a beam using combined force and vision data to adjust the interaction impedance while at the same time keeping the inclination of the beam horizontal

Attribution Biases and Trust Development in Physical Human-Machine Coordination: Blaming Yourself, Your Partner or an Unexpected Event

abstract: Reading partners’ actions correctly is essential for successful coordination, but interpretation does not always reflect reality. Attribution biases, such as self-serving and correspondence biases, lead people to misinterpret their partners’ actions and falsely assign blame after an unexpected event. These biases thus further influence people’s trust in their partners, including machine partners. The increasing capabilities and complexity of machines allow them to work physically with humans. However, their improvements may interfere with the accuracy for people to calibrate trust in machines and their capabilities, which requires an understanding of attribution biases’ effect on human-machine coordination. Specifically, the current thesis explores how the development of trust in a partner is influenced by attribution biases and people’s assignment of blame for a negative outcome. This study can also suggest how a machine partner should be designed to react to environmental disturbances and report the appropriate level of information about external conditions.Dissertation/ThesisMasters Thesis Human Systems Engineering 201