Learning Adaptive Sensorimotor Representations

任何智能物理系统的核心能力都是能够在广泛的环境中成功运行，并自主适应任务条件的变化。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，通常是通过使用“大数据”和机器学习技术，但这些系统需要广泛的人类工程和数据注释，而且通常不够灵活，如果没有人类设计者的动手交互，就不能克服任务变化。

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.