We describe a framework for changing-contact robot manipulation tasks that require the robot to make and break contacts with objects and surfaces. The discontinuous interaction dynamics of such tasks make it difficult to construct and use a single dynamics model or control strategy, and the highly non-linear nature of the dynamics during contact changes can be damaging to the robot and the objects. We present an adaptive control framework that enables the robot to incrementally learn to predict contact changes in a changing contact task, learn the interaction dynamics of the piece-wise continuous system, and provide smooth and accurate trajectory tracking using a task-space variable impedance controller. We experimentally compare the performance of our framework against that of representative control methods to establish that the adaptive control and incremental learning components of our framework are needed to achieve smooth control in the presence of discontinuous dynamics in changing-contact robot manipulation tasks.
翻译:我们描述一个不断变化的接触机器人操作任务框架,它要求机器人与天体和表面进行和断开接触。这些任务的不连续互动动态使得难以构建和使用单一动态模型或控制战略,而接触变化期间动态高度非线性可能对机器人和天体造成损害。我们提出了一个适应性控制框架,使机器人能够在变化中的接触任务中逐步学会预测接触变化,学习小片持续系统的互动动态,并利用任务空间变量阻力控制器提供平稳和准确的轨迹跟踪。我们实验性地比较了我们框架的性能与代表性控制方法的性能,以确定需要我们框架的适应控制和递增学习组成部分,以便在变化中的接触机器人操作任务中出现不连续的动态的情况下实现平稳控制。