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: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=634313
• 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.

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亲自交互的情况下克服任务变化。