Learning Adaptive Sensorimotor Representations

学习自适应感觉运动表征

• 批准号: RGPIN-2016-04941
• 负责人: Meger, David
• 金额: $ 2.26万
• 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: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=615395
• 关键词: Learning; Adaptive; Sensorimotor; Representations

A core competency of any intelligent physical system is its ability to operate successfully in a wide range of environments and to adapt autonomously to changes in task conditions. Current state-of-the-art systems for robot perception and control are increasingly able to overcome challenging environmental factors, typically through the use of "big-data" and machine learning techniques, but these systems require extensive human engineering and data annotation and are typically not flexible enough to overcome task variations without hands-on interaction from their human designer. My research will focus on what I believe to be a core challenge of intelligent robotics - the automated learning of representations that link a robot's sensors and actuators to achieve adaptive solutions for perception and control tasks. I believe this can be accomplished by combining the state-of-the-art in learning visual representations (e.g., deep representations now used for recognizing objects) and in adaptive control (e.g., policy-gradient approaches now used to learn motor skills). The key assumption of this research direction is that an automated intermediate representation will more flexibly capture the correlations between perception and control than those chosen by a human designer. The key advantage, if the research is successful, will be robots that require drastically reduced setup and training to perform new tasks successfully. A motivational example for this work is an autonomous robotic chef. Today, we can feasibly produce robots to cook a small variety of meals in a specially-designed kitchen. One can visit Japan to find examples of this technology. However, it would require enormous effort to produce a robot that could cook even a single meal in a kitchen it had not seen before. The main challenge would be the robot's inability to generalize its knowledge to differences in the placement of items and their properties (e.g., my knife is slightly smaller and is kept in a different drawer). This research program will improve a robot's ability to handle new environments with a reduced amount of effort from human engineers. Beyond cooking, we will target applications to society, such as disaster rescue, and industry, such as mining and agriculture.

任何智能物理系统的核心竞争力都是其在广泛的环境中成功运行并自主适应任务条件变化的能力。目前最先进的机器人感知和控制系统越来越能够克服具有挑战性的环境因素，通常通过使用“大数据”和机器学习技术，但这些系统需要大量的人类工程和数据注释，并且通常不够灵活，无法在没有人类设计师的实际交互的情况下克服任务变化。 我的研究将集中在我认为是智能机器人的核心挑战-自动学习表示，连接机器人的传感器和执行器，以实现感知和控制任务的自适应解决方案。我相信这可以通过结合最先进的学习视觉表示（例如，现在用于识别对象的深度表示）和自适应控制（例如，现在用于学习运动技能的政策梯度方法）。这个研究方向的关键假设是，自动化的中间表示将比人类设计师选择的更灵活地捕捉感知和控制之间的相关性。如果研究成功，关键的优势将是机器人需要大幅减少的设置和训练才能成功执行新任务。 这项工作的一个激励性例子是一个自动机器人厨师。今天，我们可以生产机器人，在专门设计的厨房里做各种各样的饭菜。你可以到日本去寻找这种技术的例子。然而，要制造出一个能在它从未见过的厨房里做一顿饭的机器人，需要付出巨大的努力。主要的挑战将是机器人无法将其知识推广到物品放置及其属性的差异（例如，我的刀稍微小一点，放在另一个抽屉里）。 这项研究计划将提高机器人处理新环境的能力，减少人类工程师的工作量。除了烹饪之外，我们还将针对社会应用，例如灾难救援，以及工业应用，例如采矿和农业。