Integrated Task and Motion Planning (TMP) provides a promising class of approaches for solving robot planning problems with intricate symbolic and geometric constraints. However, the practical usefulness of TMP planners is limited by their need for symbolic abstractions of robot actions, which are difficult to construct even for experts. We propose an approach to automatically construct and continuously improve a symbolic abstraction of a robot action via observations of the robot performing the action. This approach, called automatic abstraction repair, allows symbolic abstractions to be initially incorrect or incomplete and converge toward a correct model over time. Abstraction repair uses constrained polynomial zonotopes (CPZs), an efficient non-convex set representation, to model predicates over joint symbolic and geometric state, and performs an optimizing search over symbolic edit operations to predicate formulae to improve the correspondence of a symbolic abstraction to the behavior of a physical robot controller. In this work, we describe the aforementioned predicate model, introduce the symbolic-geometric abstraction repair problem, and present an anytime algorithm for automatic abstraction repair. We then demonstrate that abstraction repair can improve realistic action abstractions for common mobile manipulation actions from a handful of observations.
翻译:综合任务和动作规划(TMP)为解决具有复杂象征和几何限制的机器人规划问题提供了有希望的一类办法,但是,TMP规划者的实际效用受到限制,因为他们需要机械行动的象征性抽象,即使专家也难以建立。我们建议一种办法,通过观察执行动作的机器人,自动建造和不断改进机器人行动的象征性抽象。这个办法称为自动抽象修复,使象征性抽象的抽象最初不正确或不完整,并逐渐接近正确的模型。抽象修复使用受限制的多元软体(CPZs),一个有效的非软体集成代表,以模拟联合象征和几何状态的假设,对象征性编辑作业进行最优化的搜索,以便改进符号抽象与物理机器人控制器行为的对应。在这项工作中,我们描述上述的上游模型,引入象征性地球测量抽象修复问题,并提出一个用于自动抽象修复的现代算法。我们然后表明,抽象修复能够改进从一小组观察中得出的通用移动操纵行动的现实行动抽象的抽象行动。