We propose a human-operator guided planning approach to pushing-based manipulation in clutter. Most recent approaches to manipulation in clutter employs randomized planning. The problem, however, remains a challenging one where the planning times are still in the order of tens of seconds or minutes, and the success rates are low for difficult instances of the problem. We build on these control-based randomized planning approaches, but we investigate using them in conjunction with human-operator input. In our framework, the human operator supplies a high-level plan, in the form of an ordered sequence of objects and their approximate goal positions. We present experiments in simulation and on a real robotic setup, where we compare the success rate and planning times of our human-in-the-loop approach with fully autonomous sampling-based planners. We show that with a minimal amount of human input, the low-level planner can solve the problem faster and with higher success rates.

中文翻译：

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具有人在环的杂波中的非抓握操作

我们提出了一种人工操作员引导的规划方法，以在杂乱中进行基于推送的操作。最近在杂波中进行操作的方法采用随机规划。然而，这个问题仍然是一个具有挑战性的问题，其中规划时间仍然在几十秒或几分钟的数量级，并且问题的困难实例的成功率很低。我们建立在这些基于控制的随机规划方法的基础上，但我们研究将它们与人工操作员输入结合使用。在我们的框架中，人类操作员以有序的对象序列及其大致目标位置的形式提供高级计划。我们展示了模拟和真实机器人设置的实验，比较了我们的人工在环方法与完全自主的基于采样的规划器的成功率和规划时间。