‘This year, the conference left the beaten track and wandered to the Middle East for the first time in its history. We could spot a growing interest in solving multi-agent problems, exploiting LLMs in robotics, and in general leveraging learning-based approaches to enhance solutions to most basic problems like, perception, localization, planning and control. Besides presenting our research Krzysztof Walas served as chair of 3 oral sessions.’<\/p>\n\n\n\n ‘There is a growing interest in multi-robot systems. Yet, still many basic single-robot problems are far from being solved. People often ignore recent advances in the field and use very old methods as baselines.\u2019<\/p>\n\n\n\n So there’s still a lot to do!<\/p>\n\n\n\n 1. \u201eLearning Dynamics Models for Velocity Estimation in Autonomous Racing\u201d<\/strong><\/p>\n\n\n\n Authors propose a new velocity estimation method using a learned dynamics model and friction coefficient estimation, improving accuracy in aggressive autonomous racing and adapting to unseen road conditions.<\/p>\n\n\n\n https:\/\/www.arxiv.org\/abs\/2408.15610<\/a><\/p>\n\n\n\n 2. \u201eDeformable Linear Objects Manipulation with Online Model Parameters Estimation\u201d<\/strong><\/p>\n\n\n\n This study introduces a neural network-based framework for controlling the shape of deformable linear objects (DLOs) by adapting model parameters in real-time, improving manipulation accuracy in complex scenarios.<\/p>\n\n\n\n https:\/\/ieeexplore.ieee.org\/document\/10412116<\/a><\/p>\n\n\n\n 3\ufe0f. \u201eUsing Augmented Reality in Human-Robot Assembly: A Comparative Study of Eye-Gaze and Hand-Ray Pointing Methods\u201d<\/strong><\/p>\n\n\n\n This research explores how eye-gaze and hand-ray pointing methods in augmented reality can improve human-robot collaboration in assembly tasks, revealing that eye-gaze leads to faster task completion.<\/p>\n\n\n\n![\"\"](\"https:\/\/ideas-ncbr.pl\/wp-content\/uploads\/2024\/10\/10_2024_IROS_2024_Abu_Dhabi_Robotics-2-1024x768.jpg\")
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