TRAnsparent InterpretabLe robots - TRAIL

透明可解释机器人 - TRAIL

• 批准号: EP/X035441/1
• 负责人: Angelo Cangelosi
• 金额: $ 67.6万
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX035441%2F1
• 关键词: TRAnsparent; InterpretabLe; robots; TRAIL

TRAIL strategically focuses on a novel, highly interdisciplinary and cross-sectorial research and training programme for a better understanding of transparency in deep learning, artificial intelligence and robotics systems. In order to train a new generation of Early Stage Researchers (ESR) to become experts in the design and implementation of transparent, interpretable neural systems and robots, we have built a highly interdisciplinary consortium, containing expert partners with long-standing expertise in cutting-edge artificial intelligence and robotics, including deep neural networks, computer science, mathematics, social robotics, human-robot interaction and psychology. In order to build transparent robotic systems, these new ESR researchers need to learn about the theory and practice of the principles of (1) internal decision understanding and (2) external transparent behaviour. Since the ability to interpret complex robotic systems needs highly interdisciplinary knowledge, we will start, on the decision level, to interpret deep neural learning and analyse what knowledge can be efficiently extracted. At the same time, on the behaviour level, the disciplines of human-robot interaction and psychology will be key in order to understand how to present the extracted knowledge as behaviour in an intuitive and natural way to a human user to integrate the robot into a cooperative human-robot interaction. A scaffolded training curriculum will guarantee that the ESRs have not only a deep understanding of both research areas, but experience optimal skill training to be fully prepared for a successful research career in academia and industry. The importance and need of this research for the industry is clearly visible with the full commitment of 7 leading European and world-wide-operating robotics companies that together cover the majority of Europe's robot market and a broad spectrum of AI applications.

TRAIL 战略性地专注于一项新颖的、高度跨学科和跨部门的研究和培训计划，以更好地了解深度学习、人工智能和机器人系统的透明度。为了培养新一代早期研究人员（ESR）成为设计和实施透明、可解释的神经系统和机器人的专家，我们建立了一个高度跨学科的联盟，其中包括在切割领域拥有长期专业知识的专家合作伙伴。边缘人工智能和机器人，包括深度神经网络、计算机科学、数学、社交机器人、人机交互和心理学。为了构建透明的机器人系统，这些新的 ESR 研究人员需要了解 (1) 内部决策理解和 (2) 外部透明行为原则的理论和实践。由于解释复杂机器人系统的能力需要高度跨学科的知识，因此我们将从决策层面开始解释深度神经学习并分析可以有效提取哪些知识。同时，在行为层面，人机交互和心理学学科将是关键，以便了解如何以直观、自然的方式将提取的知识作为行为呈现给人类用户，从而将机器人集成到机器人中。人机协作交互。支架式培训课程将保证 ESR 不仅对这两个研究领域有深入的了解，而且还能经历最佳的技能培训，为在学术界和工业界取得成功的研究生涯做好充分准备。这项研究对行业的重要性和需求是显而易见的，七家领先的欧洲和全球运营机器人公司共同覆盖了欧洲大部分机器人市场和广泛的人工智能应用。