This paper investigates how the network induced delay together with stabilizing strategies affect the performance of haptic telepresence systems in terms of transparency (human operators should feel as if they were directly acting in the remote environment). Therefore, the mechanical impedance (force over velocity) perceived by the human operator is compared with the real environment impedance in terms of their physical parameters stiffness, damping and mass dependent on the delay. The results are discussed from the human haptic perception point of view and validated in a one-degree-offreedom telepresence experiment.
Demographic change and its various implications will be some of the biggest challenges to be faced by society and our health-care systems in the coming decades. While the number of people in need of caregiving is steadily growing in most industrial nations, the number of caregivers does not keep up with this increasing demand. Robotic assistance systems have the potential to mitigate this problem and support caregivers, people in need, and thereby the health-care systems in numerous ways. We present the concept and demonstrate first application scenarios of a holistic ecosystem for robotic assistants in caregiving. This ecosystem involves various robots to cover individual demands, and it combines several robotic technologies ranging from autonomous operation over shared-control to telepresencemodes, in order to deal with the wide variety of situations in the everyday life in caregiving. Working towards this ecosystem we have already implemented its core functionalities on the basis of our robotic prototypes and demonstrate exemplary scenarios to showcase the feasibility of the approach.
Nowadays, robots are mechanically able to perform highly demanding tasks, where AI-based planning methods are used to schedule a sequence of actions that result in the desired effect. However, it is not always possible to know the exact outcome of an action in advance, as failure situations may occur at any time. To enhance failure tolerance, we propose to predict the effects of robot actions by augmenting collected experience with semantic knowledge and leveraging realistic physics simulations. That is, we consider semantic similarity of actions in order to predict outcome probabilities for previously unknown tasks. Furthermore, physical simulation is used to gather simulated experience that makes the approach robust even in extreme cases. We show how this concept is used to predict action success probabilities and how this information can be exploited throughout future planning trials. The concept is evaluated in a series of real world experiments conducted with the humanoid robot Rollin' Justin.
Human teleoperation of robots and autonomous operations go hand in hand in today's service robots. While robot teleoperation is typically performed on low to medium levels of abstraction, automated planning has to take place on a higher abstraction level, i.e. by means of semantic reasoning. Accordingly, an abstract state of the world has to be maintained in order to enable an operator to switch seamlessly between both operational modes. We propose a novel approach that combines simulation based geometric tracking and semantic state inference by means of so called State Inference Entities to overcome this issue. We also demonstrate how Evolutionary Strategies can be employed to refine simulation parameters. All experiments are demonstrated in real-world experiments conducted with the humanoid robot Rollin' Justin.
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