Robotic Safe Adaptation In unprecedented Situations

明天的机器人将被赋予适应包括人类在内的环境中剧烈和不可预测的变化的能力。然而，这样的适应不可能是无限的：机器人必须保持可信，即适应不应该仅仅是恢复到退化的功能。相反，它必须是一种真正的适应，这意味着机器人将在保持甚至提高其预期性能的同时改变其行为，并至少保持与以前一样的安全和健壮。Robosapiens将专注于自主机器人软件适配，并将为确保以本质上安全、值得信赖和有效的方式进行此类软件适配奠定基础，从而使开放式自适应与设计安全性相协调。Robosapiens还将把这些基础转化为“首次正确”的设计工具和机器人平台，并将在TRL4之前对它们进行验证和演示。为了实现这一总体目标，Robosapiens将在四个主要目标中扩展最先进的技术。1.它将实现机器人的开放式自适应，以应对前所未有的系统结构和环境变化。2.推进安全工程技术，确保机器人在适应前、适应中和适应后的安全。3.提出深度学习技术，积极减少机器人自适应中的不确定性。4.它将确保同时使用深度学习和计算架构进行机器人自适应的系统的可信性。为了实现这些目标，Robosapiens将扩展MAPE K(监视、分析、计划、执行、知识)和深度学习等技术，以建立通用的适应程序，并使用SSH维度。Robosapiens将在四个行业规模的用例上展示这种值得信赖的机器人自适应，这些用例围绕工业拆卸机器人、仓库机器人群、自动船只的延长船体和人与机器人的交互。

The robots of tomorrow will be endowed with the ability to adapt to drastic and unpredicted changes in their environment including humans. Such adaptations can however not be boundless: the robot must stay trustworthy, i.e. the adaptations should not be just a recovery into a degraded functionality. Instead, it must be a true adaptation, meaning that the robot will change its behavior while maintaining or even increasing its expected performance, and stays at least as safe and robust as before. RoboSAPIENS will focus on autonomous robotic software adaptations and will lay the foundations for ensuring that such software adaptations are carried out in an intrinsically safe, trustworthy and efficient manner, thereby reconciling open-ended self-adaptation with safety by design. RoboSAPIENS will also transform these foundations into 'first time right'-design tools and robotic platforms, and will validate and demonstrate them up to TRL4. To achieve this over-all goal, RoboSAPIENS will extend the state of the art in four main objectives. 1. It will enable robotic open-ended self-adaptation in response to unprecedented system structural and environmental changes. 2. It will advance safety engineering techniques to assure robotic safety not only before, during and after adaptation. 3. It will advance deep learning techniques to actively reduce uncertainty in robotic self-adaptation. 4. It will assure trustworthiness of systems that use both deep-learning and computational architecturesfor robotic self-adaptation. To realise these objectives, RoboSAPIENS will extend techniquessuch as MAPE K (Monitor, Analyze, Plan, Execute, Knowledge) and Deep Learning to set up generic adaptation procedures and also use an SSH dimension. RoboSAPIENS will demonstrate this trustworthy robotic self-adaptation on four industry-scale use cases centered around an industrial disassembly robot, a warehouse robotic swarm, a prolonged hull of an autonomous vessel, and human-robotic interaction.