If robots are ever to achieve autonomous motion comparable to that exhibited by animals, they must acquire the ability to quickly recover motor behaviors when damage, malfunction, or environmental conditions compromise their ability to move effectively. We present an approach which allowed our robots and simulated robots to recover high-degree of freedom motor behaviors within a few dozen attempts. Our approach employs a behavior specification expressing the desired behaviors in terms as rank ordered differential constraints. We show how factoring these constraints through an encoding template produces a recipe for generalizing a previously optimized behavior to new circumstances in a form amenable to rapid learning. We further illustrate that adequate constraints are generically easy to determine in data-driven contexts. As illustration, we demonstrate our recovery approach on a physical 7 DOF hexapod robot, as well as a simulation of a 6 DOF 2D kinematic mechanism. In both cases we recovered a behavior functionally indistinguishable from the previously optimized motion.
翻译:如果机器人能够实现与动物所展示的运动相类似的自主运动,他们必须获得在损害、故障或环境条件损害其有效移动能力时迅速恢复运动行为的能力。 我们提出了一个方法,允许我们的机器人和模拟机器人在数十次尝试中恢复高度的自由运动行为。 我们的方法使用一种行为规范,以按顺序排列的差异限制等级来表达所期望的行为。 我们用编码模板来显示这些限制因素如何产生一种将先前最优化的行为推广到新环境的配方,其形式是便于快速学习的。 我们还进一步说明,在数据驱动的背景下,适当的限制通常很容易确定。例如,我们展示了我们对一个物理的7DOF六花机器人的回收方法,以及一个6DOF 2D运动机制的模拟。 在这两种情况下,我们恢复了一种与先前最优化的运动无法区分的行为。