In 2022, a new team was formed to lead the Women in Engineering (WiE) Committee of the IEEE Robotics and Automation Society (RAS). The new team is led by Karinne Ramirez-Amaro from the Chalmers University of Technology as the new chair, with the fantastic support of two co-chairs: Daniel Leidner from the German Aerospace Center (DLR) and Georgia Chalvatzaki from the Technical University of Darmstadt. Together, we are committed to encouraging and making a significant advance in a diverse environment within the Society to promote an inclusive and equitable culture.
Robot failures in human-centered environments are inevitable. Therefore, the ability of robots to explain such failures is paramount for interacting with humans to increase trust and transparency. To achieve this skill, the main challenges addressed in this paper are I) acquiring enough data to learn a cause-effect model of the environment and II) generating causal explanations based on that model. We address I) by learning a causal Bayesian network from simulation data. Concerning II), we propose a novel method that enables robots to generate contrastive explanations upon task failures. The explanation is based on setting the failure state in contrast with the closest state that would have allowed for successful execution, which is found through breadth-first search and is based on success predictions from the learned causal model. We assess the sim2real transferability of the causal model on a cube stacking scenario. Based on real-world experiments with two differently embodied robots, we achieve a sim2real accuracy of 70% without any adaptation or retraining. Our method thus allowed real robots to give failure explanations like, 'the upper cube was dropped too high and too far to the right of the lower cube.'
The international RoboCup competition has, in the past, been described as "a treasure trove of rich diversity for research issues and interdisciplinary connections" [1]. This description is often credited to the facets of the federation itself, which hosts a variety of challenges for rescue robots, robots for service at home and in industrial environments, and, most prominently, robots that play soccer, in different sizes and formats, be they simulated, wheeled, or legged.A particular team with constant outstanding performance is the B-Human team of the
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