论文标题
自动扩展符号移动操纵技能集
Automatic Extension of a Symbolic Mobile Manipulation Skill Set
论文作者
论文摘要
符号计划可以通过抽象大部分低级编程来为非专家用户提供直观的界面，以便非专家用户操作自动驾驶机器人。但是，符号规划人员认为最初提供的抽象域和问题描述是封闭和完成的。这意味着他们从根本上无法适应初始描述未捕获的环境或任务的变化。我们提出了一种允许代理商在遇到这种情况后自动扩展其技能集，从而自动扩展其技能集的方法。我们介绍了从以前的经验中概括的策略，完成一系列关键动作序列并发现先决条件，以确保我们的技能序列探索方案的效率。在对象重排任务上的模拟中评估了所得系统。与蒙特卡洛树搜索基线相比，我们的有效搜索策略平均在运行时的成功率平均提高了29％。
Symbolic planning can provide an intuitive interface for non-expert users to operate autonomous robots by abstracting away much of the low-level programming. However, symbolic planners assume that the initially provided abstract domain and problem descriptions are closed and complete. This means that they are fundamentally unable to adapt to changes in the environment or task that are not captured by the initial description. We propose a method that allows an agent to automatically extend its skill set, and thus the abstract description, upon encountering such a situation. We introduce strategies for generalizing from previous experience, completing sequences of key actions and discovering preconditions to ensure the efficiency of our skill sequence exploration scheme. The resulting system is evaluated in simulation on object rearrangement tasks. Compared to a Monte Carlo Tree Search baseline, our strategies for efficient search have on average a 29% higher success rate at a 68% faster runtime.
