Brain-inspired visually guided grasping system

类脑视觉引导抓取系统

• 批准号: 519891-2017
• 负责人: Tripp, Bryan
• 金额: $ 2.9万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=667149
• 关键词: Brain; inspired; visually; guided; grasping

The field of advanced robotics is expected to grow dramatically over the next decade, primarily by expanding from highly predictable factory settings to unstructured and novel situations. A key challenge in this area is enabling robots to reliably manipulate unfamiliar objects. The project goal is to develop an advanced grasping system that can sense the shape of an object, and position a robotic gripper appropriately to reliably grasp the object. This process is rapid and effortless for humans, but it has been difficult to achieve in robotics. In conventional robotic grasping, a human programs the exact movements that the robot performs. This is only useful in scenarios such as assembly lines, where it is known in advance exactly what shapes objects have, and where they will be at what time. Less-controlled environments require robots to make such determinations and choose appropriate grasp parameters automatically. The project will begin by adapting an existing state-of-the-art deep-learning system that has shown promise recently for automatic grasping (in somewhat restricted scenarios), to produce a working end-to-end system in the first six months. The system will then be extended to make more intelligent decisions about the applied grip force. In the second year, two new variations of the system will be developed and tested, with the goal of allowing the robot to approach objects from a wider variety of angles. This will allow better-quality grasps, as well as grasping for a wider variety of purposes. Finally, inspired by certain parallels between deep networks and the brain, we will systematically compare network activity in each of our networks to activity in the grasping-related areas of the monkey brain, using recent studies in the neuroscience literature. Finally, the third year of the project will develop an iteration of the system that incorporates lessons learned from the performance of earlier networks and comparisons with the primate brain. The resulting system will become a key product of Applied Brain Research. More broadly, it will contribute advanced technology to the Canadian robotics sector, and provide valuable training for HQP in this rapidly growing area. ******

高级机器人技术的领域预计将在未来十年内急剧增长，主要是通过从高度可预测的工厂环境扩展到非结构化和新颖的情况。该领域的一个主要挑战是使机器人能够可靠地操纵陌生的物体。项目目标是开发一个可以感知物体形状的高级握把系统，并适当地定位机器人抓手以可靠地掌握对象。对于人类而言，这个过程很快且毫不费力，但是在机器人技术中很难实现。在传统的机器人抓握中，人类计划机器人执行的确切动作。这仅在诸如组装线之类的方案中有用，在汇编线等情况下，它在该方案中得到了预先知道的形状对象所具有的，以及它们在何处的位置。控制较低的环境需要机器人来确定此类确定并自动选择适当的掌握参数。该项目将首先调整现有的最新深度学习系统，该系统最近显示出有望自动掌握的希望（在某种程度上受到限制的情况），可以在最初六个月内生成端到端的工作。然后将扩展该系统，以做出有关应用握力的更聪明的决定。在第二年，将开发和测试系统的两种新变化，目的是允许机器人从更广泛的角度接近对象。这将允许更好的掌握，并抓住各种目的。最后，受到深层网络与大脑之间某些相似之处的启发，我们将使用神经科学文献中的最新研究，系统地将每个网络中的网络活动与猴子大脑的握把相关区域的活动进行比较。最后，该项目的第三年将开发系统的迭代，该迭代结合了从早期网络的性能以及与灵长类动物大脑的比较中学到的经验教训。所得系统将成为应用大脑研究的关键产品。更广泛地说，它将为加拿大机器人部门提供先进的技术，并为这个快速增长的地区为HQP提供宝贵的培训。 ******