Data-Driven Architecture to Encode Information in the Kinematics of Robots and Artificial Avatars

IF 2.4 Q2 AUTOMATION & CONTROL SYSTEMS IEEE Control Systems Letters Pub Date : 2024-06-18 DOI:10.1109/LCSYS.2024.3416071
Francesco De Lellis;Marco Coraggio;Nathan C. Foster;Riccardo Villa;Cristina Becchio;Mario Di Bernardo
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Abstract

We present a data-driven control architecture designed to encode specific information, such as the presence or absence of an emotion, in the movements of an avatar or robot driven by a human operator. Our strategy leverages a set of human-recorded examples as the core for generating information-rich kinematic signals. To ensure successful object grasping, we propose a deep reinforcement learning strategy. We validate our approach using an experimental dataset obtained during the reach-to-grasp phase of a pick-and-place task.
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在机器人和人造头像运动学中编码信息的数据驱动架构
我们提出了一种数据驱动型控制架构,旨在将特定信息(如是否存在某种情绪)编码到由人类操作员驱动的化身或机器人的动作中。我们的策略以一组人类记录的示例为核心,生成信息丰富的运动信号。为确保成功抓取物体,我们提出了一种深度强化学习策略。我们利用在拾放任务的伸手抓取阶段获得的实验数据集验证了我们的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Control Systems Letters
IEEE Control Systems Letters Mathematics-Control and Optimization
CiteScore
4.40
自引率
13.30%
发文量
471
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