论文标题
学习上下文自适应任务限制机器人操纵
Learning Context-Adaptive Task Constraints for Robotic Manipulation
论文作者
论文摘要
基于约束的控制方法提供了一种灵活的方式来指定机器人操纵任务，并在具有许多自由度的机器人上执行它们。但是，任务限制及其相关优先级的规范通常需要人类专家，并且通常会导致针对特定情况的量身定制解决方案。本文介绍了我们最近的努力，从数据（上下文）（上下文）中自动从数据中自动获得基于约束的机器人控制器的任务约束。我们使用一种按示词的编程方法来生成给定任务多种变化（上下文更改）的培训数据。从这些数据中，我们了解了一个概率模型，该模型将上下文变量映射到任务约束及其各自的软任务优先级。我们在工业机器人上使用3种不同的双臂操纵任务评估了我们的方法，并表明它在繁殖精度方面的性能要比具有手动指定约束的基于约束的控制器更好。
Constraint-based control approaches offer a flexible way to specify robotic manipulation tasks and execute them on robots with many degrees of freedom. However, the specification of task constraints and their associated priorities usually requires a human-expert and often leads to tailor-made solutions for specific situations. This paper presents our recent efforts to automatically derive task constraints for a constraint-based robot controller from data and adapt them with respect to previously unseen situations (contexts). We use a programming-by-demonstration approach to generate training data in multiple variations (context changes) of a given task. From this data we learn a probabilistic model that maps context variables to task constraints and their respective soft task priorities. We evaluate our approach with 3 different dual-arm manipulation tasks on an industrial robot and show that it performs better in terms of reproduction accuracy than constraint-based controllers with manually specified constraints.
