Brain-inspired visually guided grasping system

预计先进机器人领域在未来十年将大幅增长，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奇的情况。这一领域的一个关键挑战是使机器人能够可靠地操作不熟悉的物体。该项目的目标是开发一种先进的抓取系统，可以感知物体的形状，并适当地定位机器人抓取器，以可靠地抓取物体。这一过程对人类来说是快速和毫不费力的，但在机器人技术中却很难实现。在传统的机器人抓取中，人类对机器人执行的准确动作进行编程。这仅在装配线等场景中有用，在这些场景中，事先确切知道对象具有什么形状，以及它们将在什么时间位于何处。控制较少的环境要求机器人自动做出这样的决定并选择适当的抓取参数。该项目将首先采用现有的最先进的深度学习系统，该系统最近显示出自动抓取(在有些受限的情况下)的前景，以便在头六个月产生一个工作的端到端系统。然后，该系统将被扩展，以做出关于所应用的握力的更智能的决策。在第二年，该系统的两个新变种将被开发和测试，目标是允许机器人从更广泛的角度接近物体。这将允许更高质量的抓取，以及为更广泛的目的抓取。最后，受到深层网络和大脑之间某些相似之处的启发，我们将利用神经科学文献中最近的研究，系统地比较我们每个网络中的网络活动与猴子大脑中与抓取相关的区域的活动。最后，该项目的第三年将开发该系统的迭代，其中包括从早期网络的性能中吸取的经验教训，以及与灵长类大脑的比较。由此产生的系统将成为应用脑研究的关键产品。更广泛地说，它将为加拿大机器人行业贡献先进技术，并在这个快速增长的领域为HQP提供宝贵的培训。******

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. ******