Advanced AI and RobotIcS for autonomous task pErformance

保持可再生能源和农业等关键行业的竞争力和领导地位，取决于人工智能和机器人技术日益成为相关技术格局中广泛而不可或缺的一部分，尤其是在劳动力严重短缺的情况下。ARISE项目旨在围绕可重构机器人机械手引入感知和控制模块的组合，从而实现复杂操作任务自动化水平的逐步改变。ARISE将包括以下关键的新技术组件，这些组件将在自动任务分割、人机交互和复杂操作方面显著推动最新技术的发展：(1)安装在移动机器人平台上的两个可重构气动机器人机械手(2)具有可变刚度的新型软末端执行器，可执行多种操作任务(3)包含3D视觉算法的机器人感知模块(4)定位和映射模块，可使机器人准确识别其在环境中的位置(5)由场景理解算法提供支持的语义映射模块(6)知识表示框架，用于捕获有关对象属性和关系的重要信息(7)用于获取机器人技能以直接从人类演示者那里完成复杂任务的分层模仿学习框架(8)用于响应机器人控制的人机交互条件路径和任务规划模块(9)用于在边缘以流线型方式部署机器学习模型和计算机视觉算法的边缘ai框架。ARISE工具链将分别在太阳能和水培农场的5个实际用例场景中进行集成和验证，包括安装和维修，以及移植和收获任务。

Maintaining the competitiveness and leadership position of crucial industries such as renewable energy and agriculture, is contingent upon AI and Robotics increasingly becoming a widespread and integral part of the relevant technological landscapes, particularly in the face of steep labour shortages. ARISE project aims to introduce a combination of perception and control modules around a reconfigurable robotic manipulator that will enable a step change in the level of automation of complex manipulation tasks. ARISE will comprise the following key novel technology components that will significantly push the state of the art in terms of automatic task segmentation, human robot interaction and complex manipulation: (1) Two reconfigurable pneumatic-based robotic manipulators mounted on a mobile robotic platform (2) Novel soft end-effectors with variable stiffness that will allow for a diverse set of manipulation tasks (3) A Robotic perception module comprising 3D vision algorithms (4) A Localisation and Mapping module that will allow the robot to accurately identify its position within the environment (5) A Semantic Mapping module powered by scene understanding algorithms (6) A Knowledge Representation framework that will capture important information regarding objects’ properties and relationships (7) A Hierarchical Imitation Learning framework for acquiring robotic skills to accomplish complex tasks directly from human demonstrators (8) A Human-Interaction conditioned Path and Task planning module enabling reactive robot control (9) An edge-AI framework for deploying Machine Learning models and computer vision algorithms at the edge in a streamlined fashion. The ARISE toolchain will be integrated and validated in 5 real use case scenarios including installation and repair, and transplanting and harvesting tasks, in solar and hydroponic farms, respectively.