Franz to James (10 October 1962)

https://egrove.olemiss.edu/mercorr_pro/2110/thumbnail.jp

Magnetic fluctuations and superconductivity in Fe pnictides probed by electron spin resonance

The electron spin resonance absorption spectrum of Eu^{2+} ions serves as a probe of the normal and superconducting state in Eu_{0.5}K_{0.5}Fe_2As_2. The spin-lattice relaxation rate 1/T_1^{\rm ESR} obtained from the ESR linewidth exhibits a Korringa-like linear increase with temperature above T_C evidencing a normal Fermi-liquid behavior. Below 45 K deviations from the Korringa-law occur which are ascribed to enhanced magnetic fluctuations within the FeAs layers upon approaching the superconducting transition. Below T_C the spin-lattice relaxation rate 1/T_1^{\rm ESR} follows a T^{1.5}-behavior without the appearance of a coherence peak.Comment: 5 pages, 5 figure

Exploring AI-enhanced Shared Control for an Assistive Robotic Arm

Assistive technologies and in particular assistive robotic arms have the potential to enable people with motor impairments to live a self-determined life. More and more of these systems have become available for end users in recent years, such as the Kinova Jaco robotic arm. However, they mostly require complex manual control, which can overwhelm users. As a result, researchers have explored ways to let such robots act autonomously. However, at least for this specific group of users, such an approach has shown to be futile. Here, users want to stay in control to achieve a higher level of personal autonomy, to which an autonomous robot runs counter. In our research, we explore how Artifical Intelligence (AI) can be integrated into a shared control paradigm. In particular, we focus on the consequential requirements for the interface between human and robot and how we can keep humans in the loop while still significantly reducing the mental load and required motor skills.Comment: Workshop on Engineering Interactive Systems Embedding AI Technologies (EIS-embedding-AI) at EICS'2

Extending Cobot's Motion Intention Visualization by Haptic Feedback

Nowadays, robots are found in a growing number of areas where they collaborate closely with humans. Enabled by lightweight materials and safety sensors, these cobots are gaining increasing popularity in domestic care, supporting people with physical impairments in their everyday lives. However, when cobots perform actions autonomously, it remains challenging for human collaborators to understand and predict their behavior, which is crucial for achieving trust and user acceptance. One significant aspect of predicting cobot behavior is understanding their motion intention and comprehending how they "think" about their actions. Moreover, other information sources often occupy human visual and audio modalities, rendering them frequently unsuitable for transmitting such information. We work on a solution that communicates cobot intention via haptic feedback to tackle this challenge. In our concept, we map planned motions of the cobot to different haptic patterns to extend the visual intention feedback.Comment: Final CHI LBW 2023 submission: https://dx.doi.org/10.1145/3544549.358560

How to Communicate Robot Motion Intent: A Scoping Review

Robots are becoming increasingly omnipresent in our daily lives, supporting us and carrying out autonomous tasks. In Human-Robot Interaction, human actors benefit from understanding the robot's motion intent to avoid task failures and foster collaboration. Finding effective ways to communicate this intent to users has recently received increased research interest. However, no common language has been established to systematize robot motion intent. This work presents a scoping review aimed at unifying existing knowledge. Based on our analysis, we present an intent communication model that depicts the relationship between robot and human through different intent dimensions (intent type, intent information, intent location). We discuss these different intent dimensions and their interrelationships with different kinds of robots and human roles. Throughout our analysis, we classify the existing research literature along our intent communication model, allowing us to identify key patterns and possible directions for future research.Comment: Interactive Data Visualization of the Paper Corpus: https://rmi.robot-research.d