海报:如何培养机器人-超越辅助类人机器人的访问控制约束

N. Hemken, F. Jacob, Fabian Peller-Konrad, Rainer Kartmann, T. Asfour, H. Hartenstein
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引用次数: 0

摘要

人形机器人将能够在日常生活中帮助人类,特别是由于它们的多功能动作能力。然而,虽然这些机器人需要一定程度的自主性来学习和探索,但它们也应该尊重各种限制,例如访问控制等。我们探索将隐私和安全约束(以活动为中心的访问控制和基于深度学习的访问控制)与机器人任务规划方法(经典符号规划和端到端基于学习的规划)相结合。我们报告了他们各自权衡的初步结果,并得出结论,混合方法最有可能是选择的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Poster: How to Raise a Robot - Beyond Access Control Constraints in Assistive Humanoid Robots
Humanoid robots will be able to assist humans in their daily life, in particular due to their versatile action capabilities. However, while these robots need a certain degree of autonomy to learn and explore, they also should respect various constraints, for access control and beyond. We explore incorporating privacy and security constraints (Activity-Centric Access Control and Deep Learning Based Access Control) with robot task planning approaches (classical symbolic planning and end-to-end learning-based planning). We report pre-liminary results on their respective trade-offs and conclude that a hybrid approach will most likely be the method of choice.
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