Robust Learning of Visual Behaviors

视觉行为的稳健学习

• 批准号: RGPIN-2021-03461
• 负责人: Meger, David
• 金额: $ 2.11万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=742566
• 关键词: Robust; Learning; Visual; Behaviors

The Robust Learning of Visual Behaviors for robots will bring more effective helpers for Canadian society and industry. Rather than requiring a large team of engineers and programmers, this research proposes a general-purpose set of learning tools that allow a robot to improve its own behavior over time, by trial-and-error. This will not only make our robot helpers easier to use, it will allow them to adapt their behaviors appropriately, as the needs of human users and challenges of the environment change. There are three aspects to the work in this proposal. First, we will work on the core Reinforcement Learning technology, which produces software that acts as the robot's "brain" in this context. There, our technologies are continually improving, and only lately, it has been possible to use Reinforcement Learning to train software agents to beat humans at boardgames like Chess and Go as well as many video games. The same technology is now being extended to allow robots to learn in this way. Second, we will connect the robot's visual sensing to this learning system, so that robots guided by camera can learn tasks that involve eye-hand coordination, among other examples. Finally, we seek to make it easy for users to instruct robots to learn in a way that is most useful to human users. Interactive Behavior Authoring will provide ways for the robot and human to enter a dialogue where the human gives information on what is intended, and the robot queries for more information where it can benefit most. The core technology in this proposal, Reinforcement Learning, is already broadly used in Canadian industries. Technology and AI companies use this to train dialogue agents that answer phones and improve search engines. Medical researchers have used RL to investigate new courses of treatment. The most relevant uses to this proposal involve the automation of moving physical bodies. Robot arms are often present in today's factories and warehouses, assisting human workers with the most dull and repetitive tasks. The new work in this proposal will extend the set of places and tasks where robots can be deployed with Reinforcement Learning. We will target driving in forests so that timbre harvesting and transport can benefit. Farms, mines and watersheds are also areas where we could make great use of robotics, and this proposal will produce useful technologies for increasing efficiency in each of these domains. Finally, as this proposal is written during the time of the COVID pandemic, there are many humans sheltering at home and isolated. There is great potential for Reinforcement Learning to produce virtual and physical companions who are immune from the virus, and therefore of great prospect as housemates until the new normal returns.

机器人视觉行为的稳健学习将为加拿大社会和行业带来更有效的帮手。这项研究不需要庞大的工程师和程序员团队，而是提出了一套通用的学习工具，允许机器人通过反复尝试，随着时间的推移改善自己的行为。这不仅将使我们的机器人助手更容易使用，还将允许它们随着人类用户的需求和环境挑战的变化而适当地调整自己的行为。这项提案中的工作有三个方面。首先，我们将致力于核心强化学习技术，该技术产生的软件在这种情况下充当机器人的“大脑”。在那里，我们的技术在不断改进，直到最近，才有可能使用强化学习来训练软件代理，以便在国际象棋和围棋等棋类游戏以及许多视频游戏中击败人类。同样的技术现在正在扩展，允许机器人以这种方式学习。第二，我们将把机器人的视觉感应连接到这个学习系统，这样在摄像头的引导下，机器人就可以学习涉及眼手协调的任务，等等。最后，我们寻求使用户能够轻松地指导机器人以对人类用户最有用的方式进行学习。交互行为创作将为机器人和人类提供进入对话的方式，在对话中，人类提供关于意图的信息，而机器人在最大限度地受益的地方查询更多信息。该提案中的核心技术强化学习已经在加拿大的工业中得到了广泛的应用。科技和人工智能公司利用这一点来培训接听电话和改进搜索引擎的对话代理。医学研究人员已经使用RL来研究新的治疗过程。这一提议最相关的用途涉及移动物理物体的自动化。机械臂经常出现在今天的工厂和仓库里，帮助人类工人完成最枯燥和重复的任务。这项提案中的新工作将扩展机器人可以使用强化学习部署的地点和任务集。我们将瞄准在森林中驾驶，以便音色收获和运输能够受益。农场、矿山和流域也是我们可以充分利用机器人技术的领域，这一提议将产生有用的技术，以提高这些领域的效率。最后，由于这项提案是在新城疫大流行期间编写的，有许多人在家中避难并与世隔绝。强化学习有很大的潜力来产生对病毒免疫的虚拟和物理同伴，因此在新的常态回归之前，作为室友的前景很大。